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Merge remote-tracking branch 'remotes/origin/vars-rename' into UI2

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ebolam
2022-08-17 10:26:06 -04:00
parent 9188b63ce3 21c08353b9
commit 91ab8ca3ce
40 changed files with 5813 additions and 401 deletions
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1
.gitattributes vendored
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@@ -1,2 +1,3 @@
*.min.lua linguist-vendored
*documentation.html linguist-vendored
/static/swagger-ui/* linguist-vendored

2
.gitignore vendored
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@@ -25,6 +25,8 @@ softprompts
models
!models/models go here.txt
Uninstall
flask_session
accelerate-disk-cache
.ipynb_checkpoints
# Ignore PyCharm project files.

10
Uninstall.bat
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@@ -11,11 +11,15 @@ IF EXIST "Uninstall\unins000.exe" (
start Uninstall\unins000.exe
exit
) ELSE (
echo This will remove all KoboldAI folders that do not contain user data
pause
GOTO UNINSTALL
echo This will remove all KoboldAI folders that do not contain user data.
echo DO NOT CONTINUE IF KOBOLDAI IS NOT IN ITS OWN FOLDER! OTHERWISE YOUR OTHER DATA IN THIS FOLDER WILL BE DELETED AS WELL!
pause
set /P D=Type DELETE if you wish to continue the uninstallation:
)
IF %D%==DELETE GOTO UNINSTALL
exit
:UNINSTALL
echo Uninstallation in progress, please wait...
set DM=Y

3572
aiserver.py
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File diff suppressed because it is too large Load Diff

13
bridge.lua
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@@ -165,7 +165,7 @@ return function(_python, _bridged)
---@field num_outputs integer
---@field feedback string
---@field is_config_file_open boolean
local kobold = setmetatable({API_VERSION = 1.1}, metawrapper)
local kobold = setmetatable({API_VERSION = 1.2}, metawrapper)
local KoboldLib_mt = setmetatable({}, metawrapper)
local KoboldLib_getters = setmetatable({}, metawrapper)
local KoboldLib_setters = setmetatable({}, metawrapper)
@@ -505,6 +505,7 @@ return function(_python, _bridged)
elseif entries.name == "KoboldWorldInfoEntry" then
_entries = {entries}
else
_entries = {}
for k, v in pairs(entries) do
if type(v) == "table" and v.name == "KoboldWorldInfoEntry" and v:is_valid() then
_entries[k] = v.uid
@@ -725,11 +726,11 @@ return function(_python, _bridged)
if k == "content" then
if rawget(t, "_num") == 0 then
if bridged.koboldai_vars.gamestarted then
local prompt = koboldbridge.userstate == "genmod" and bridged.vars._prompt or bridged.koboldai_vars.prompt
local prompt = koboldbridge.userstate == "genmod" and bridged.koboldai_vars._prompt or bridged.koboldai_vars.prompt
return prompt
end
end
local actions = koboldbridge.userstate == "genmod" and bridged.vars._actions or bridged.koboldai_vars.actions
local actions = koboldbridge.userstate == "genmod" and bridged.koboldai_vars._actions or bridged.koboldai_vars.actions
return _python.as_attrgetter(actions).get(math.tointeger(rawget(t, "_num")) - 1)
end
end
@@ -751,7 +752,7 @@ return function(_python, _bridged)
error("Attempted to set the prompt chunk's content to the empty string; this is not allowed")
return
end
local actions = koboldbridge.userstate == "genmod" and bridged.vars._actions or bridged.koboldai_vars.actions
local actions = koboldbridge.userstate == "genmod" and bridged.koboldai_vars._actions or bridged.koboldai_vars.actions
if _k ~= 0 and _python.as_attrgetter(actions).get(_k-1) == nil then
return
end
@@ -776,7 +777,7 @@ return function(_python, _bridged)
---@return fun(): KoboldStoryChunk, table, nil
function KoboldStory:forward_iter()
local actions = koboldbridge.userstate == "genmod" and bridged.vars._actions or bridged.koboldai_vars.actions
local actions = koboldbridge.userstate == "genmod" and bridged.koboldai_vars._actions or bridged.koboldai_vars.actions
local nxt, iterator = _python.iter(actions)
local run_once = false
local function f()
@@ -804,7 +805,7 @@ return function(_python, _bridged)
---@return fun(): KoboldStoryChunk, table, nil
function KoboldStory:reverse_iter()
local actions = koboldbridge.userstate == "genmod" and bridged.vars._actions or bridged.koboldai_vars.actions
local actions = koboldbridge.userstate == "genmod" and bridged.koboldai_vars._actions or bridged.koboldai_vars.actions
local nxt, iterator = _python.iter(_python.builtins.reversed(actions))
local last_run = false
local function f()

67
colab/GPU.ipynb
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@@ -41,7 +41,7 @@
"\n",
"For more information about KoboldAI check our our Github readme : https://github.com/KoboldAI/KoboldAI-Client/blob/main/readme.md\n",
"\n",
"For the larger AI models (That are typically more coherent) check out our [TPU edition](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/TPU.ipynb)"
"For the larger AI models (That are typically more coherent) check out our **[TPU edition](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/TPU.ipynb)**!"
]
},
{
@@ -65,23 +65,56 @@
"cellView": "form"
},
"source": [
"#@title <b><-- Click this to start KoboldAI</b>\n",
"#@title <b><-- Select your model below and then click this to start KoboldAI</b>\n",
"#@markdown You can find a description of the models below along with instructions on how to start KoboldAI.\n",
"\n",
"Model = \"KoboldAI/fairseq-dense-2.7B-Nerys\" #@param [\"KoboldAI/fairseq-dense-2.7B-Nerys\", \"KoboldAI/GPT-Neo-2.7B-Janeway\", \"KoboldAI/GPT-Neo-2.7B-AID\", \"KoboldAI/GPT-Neo-2.7B-Picard\", \"KoboldAI/GPT-Neo-2.7B-Horni-LN\", \"KoboldAI/GPT-Neo-2.7B-Horni\", \"KoboldAI/GPT-Neo-2.7B-Shinen\", \"EleutherAI/gpt-neo-2.7B\"] {allow-input: true}\n",
"Model = \"Nerys 2.7B\" #@param [\"Nerys 2.7B\", \"Janeway 2.7B\", \"Picard 2.7B\", \"AID 2.7B\", \"Horni LN 2.7B\", \"Horni 2.7B\", \"Shinen 2.7B\", \"Neo 2.7B\"] {allow-input: true}\n",
"Version = \"Official\" #@param [\"Official\", \"United\"] {allow-input: true}\n",
"Provider = \"Localtunnel\" #@param [\"Localtunnel\", \"Cloudflare\"]\n",
"Provider = \"Cloudflare\" #@param [\"Localtunnel\", \"Cloudflare\"]\n",
"\n",
"!nvidia-smi\n",
"from google.colab import drive\n",
"drive.mount('/content/drive/')\n",
"\n",
"if Model == \"Nerys 2.7B\":\n",
" Model = \"KoboldAI/fairseq-dense-2.7B-Nerys\"\n",
" path = \"\"\n",
" download = \"\"\n",
"elif Model == \"Janeway 2.7B\":\n",
" Model = \"KoboldAI/GPT-Neo-2.7B-Janeway\"\n",
" path = \"\"\n",
" download = \"\"\n",
"elif Model == \"Picard 2.7B\":\n",
" Model = \"KoboldAI/GPT-Neo-2.7B-Picard\"\n",
" path = \"\"\n",
" download = \"\"\n",
"elif Model == \"AID 2.7B\":\n",
" Model = \"KoboldAI/GPT-Neo-2.7B-AID\"\n",
" path = \"\"\n",
" download = \"\"\n",
"elif Model == \"Horni LN 2.7B\":\n",
" Model = \"KoboldAI/GPT-Neo-2.7B-Horni-LN\"\n",
" path = \"\"\n",
" download = \"\"\n",
"elif Model == \"Horni 2.7B\":\n",
" Model = \"KoboldAI/GPT-Neo-2.7B-Horni\"\n",
" path = \"\"\n",
" download = \"\"\n",
"elif Model == \"Shinen 2.7B\":\n",
" Model = \"KoboldAI/GPT-Neo-2.7B-Shinen\"\n",
" path = \"\"\n",
" download = \"\"\n",
"elif Model == \"Neo 2.7B\":\n",
" Model = \"EleutherAI/gpt-neo-2.7B\"\n",
" path = \"\"\n",
" download = \"\"\n",
"\n",
"if Provider == \"Localtunnel\":\n",
" tunnel = \"--localtunnel yes\"\n",
"else:\n",
" tunnel = \"\"\n",
"\n",
"!wget https://henk.tech/ckds -O - | bash /dev/stdin -m $Model -g $Version $tunnel"
"!wget https://koboldai.org/ckds -O - | bash /dev/stdin -m $Model -g $Version $tunnel"
],
"execution_count": null,
"outputs": []
@@ -92,27 +125,25 @@
"# GPU Edition Model Descriptions\n",
"| Model | Size | Style | Description |\n",
"| --- | --- | --- | --- |\n",
"| [Fairseq-Dense-2.7B-Nerys](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | 2.7B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n",
"| [GPT-Neo-2.7B-Janeway](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | 2.7B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n",
"| [GPT-Neo-2.7B-Picard](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B | Novel | Picard is a model trained for SFW Novels based on GPT-Neo-2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |\n",
"| [GPT-Neo-2.7B-AID](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. |\n",
"| [GPT-Neo-2.7B-Horni-LN](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B | Novel | This model is based on GPT-Neo-2.7B-Horni and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. |\n",
"| [GPT-Neo-2.7B-Horni](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B | NSFW | This model is tuned on Literotica to produce a Novel style model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. |\n",
"| [GPT-Neo-2.7B-Shinen](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |\n",
"| [GPT-Neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. |\n",
"| [Nerys 2.7B](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | 2.7B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n",
"| [Janeway 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | 2.7B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n",
"| [Picard 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B | Novel | Picard is a model trained for SFW Novels based on Neo 2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |\n",
"| [AID 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. |\n",
"| [Horni LN 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B | Novel | This model is based on Horni 2.7B and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. |\n",
"| [Horni 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B | NSFW | This model is tuned on Literotica to produce a Novel style model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. |\n",
"| [Shinen 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you Shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |\n",
"| [Neo 2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. |\n",
"\n",
"# [TPU Edition Model Descriptions](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/TPU.ipynb)\n",
"\n",
"| Model | Size | Style | Description |\n",
"| --- | --- | --- | --- |\n",
"| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n",
"| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n",
"| [Janeway](https://huggingface.co/KoboldAI/fairseq-dense-13B-Janeway) by Mr Seeker | 13B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n",
"| [Shinen](https://huggingface.co/KoboldAI/fairseq-dense-13B-Shinen) by Mr Seeker | 13B | NSFW | Shinen is an NSFW model designed to be more explicit. Trained on a variety of stories from the website Sexstories it contains many different kinks. |\n",
"| [Skein](https://huggingface.co/KoboldAI/GPT-J-6B-Skein) by VE\\_FORBRYDERNE | 6B | Adventure | Skein is best used with Adventure mode enabled, it consists of a 4 times larger adventure dataset than the Adventure model making it excellent for text adventure gaming. On top of that it also consists of light novel training further expanding its knowledge and writing capabilities. It can be used with the You filter bias if you wish to write Novels with it, but dedicated Novel models can perform better for this task. |\n",
"| [Adventure](https://huggingface.co/KoboldAI/GPT-J-6B-Adventure) by VE\\_FORBRYDERNE | 6B | Adventure | Adventure is a 6B model designed to mimick the behavior of AI Dungeon. It is exclusively for Adventure Mode and can take you on the epic and wackey adventures that AI Dungeon players love. It also features the many tropes of AI Dungeon as it has been trained on very similar data. It must be used in second person (You). |\n",
"| [Lit](https://huggingface.co/hakurei/lit-6B) by Haru | 6B | NSFW | Lit is a great NSFW model trained by Haru on both a large set of Literotica stories and high quality novels along with tagging support. Creating a high quality model for your NSFW stories. This model is exclusively a novel model and is best used in third person. |\n",
"| [Convo](https://huggingface.co/hitomi-team/convo-6B) by Hitomi Team | 6B | Chatbot | Convo-6B is a GPT-J 6B model fine-tuned on a collection of high quality open source datasets which amount to 6 million messages. The primary goal of the model is to provide improved performance and generalization when generating multi-turn dialogue for characters that were not present from within the fine tuning data. The prompted performance has especially improved over the predecessor model [C1-6B](https://huggingface.co/hakurei/c1-6B). |\n",
"| [C1](https://huggingface.co/hakurei/c1-6B) by Haru | 6B | Chatbot | C1 has been trained on various internet chatrooms, it makes the basis for an interesting chatbot model and has been optimized to be used in the Chatmode. |\n",
"| Neo(X) by EleutherAI | 20B | Generic | NeoX is the largest EleutherAI model currently available, being a generic model it is not particularly trained towards anything and can do a variety of writing, Q&A and coding tasks. 20B's performance is closely compared to the 13B models and it is worth trying both especially if you have a task that does not involve english writing. Its behavior will be similar to the GPT-J-6B model since they are trained on the same dataset but with more sensitivity towards repetition penalty and with more knowledge. |\n",
"| [Fairseq Dense](https://huggingface.co/KoboldAI/fairseq-dense-13B) | 13B | Generic | Trained by Facebook Researchers this model stems from the MOE research project within Fairseq. This particular version has been converted by us for use in KoboldAI. It is known to be on par with the larger 20B model from EleutherAI and considered as better for pop culture and language tasks. Because the model has never seen a new line (enter) it may perform worse on formatting and paragraphing. |\n",
"| [GPT-J-6B](https://huggingface.co/EleutherAI/gpt-j-6B) by EleutherAI | 6B | Generic | This model serves as the basis for most other 6B models (Some being based on Fairseq Dense instead). Being trained on the Pile and not biased towards anything in particular it is suitable for a variety of tasks such as writing, Q&A and coding tasks. You will likely get better result with larger generic models or finetuned models. |\n",
@@ -123,9 +154,9 @@
"| Novel | For regular story writing, not compatible with Adventure mode or other specialty modes. |\n",
"| NSFW | Indicates that the model is strongly biased towards NSFW content and is not suitable for children, work environments or livestreaming. Most NSFW models are also Novel models in nature. |\n",
"| Adventure | These models are excellent for people willing to play KoboldAI like a Text Adventure game and are meant to be used with Adventure mode enabled. Even if you wish to use it as a Novel style model you should always have Adventure mode on and set it to story. These models typically have a strong bias towards the use of the word You and without Adventure mode enabled break the story flow and write actions on your behalf. |\n",
"| Chatbot | These models are specifically trained for chatting and are best used with the Chatmode enabled. Typically trained on either public chatrooms or private chats. |\n",
"| Generic | Generic models are not trained towards anything specific, typically used as a basis for other tasks and models. They can do everything the other models can do, but require much more handholding to work properly. Generic models are an ideal basis for tasks that we have no specific model for, or for experiencing a softprompt in its raw form. |\n",
"\n",
"---\n",
"# How to start KoboldAI in 7 simple steps\n",
"Using KoboldAI on Google Colab is easy! Simply follow these steps to get started:\n",
"1. Mobile phone? Tap the play button below next to \"<--- Tap this if you play on mobile\" to reveal an audio player, play the silent audio to keep the tab alive so Google will not shut you down when your using KoboldAI. If no audio player is revealed your phone browser does not support Google Colab in the mobile view, go to your browser menu and enable Desktop mode before you continue.\n",
@@ -143,4 +174,4 @@
}
}
]
}
}

79
colab/TPU.ipynb
View File

@@ -32,7 +32,9 @@
"\n",
"Further down the page you can find descriptions of the models, and tips to get the most out of your Google Colab experience.\n",
"\n",
"Make sure to keep this page open while you are using KoboldAI, and check back regularly to see if you got a Captcha. Failure to complete the captcha's in time can result in termination of your session or a lower priority towards the TPUs."
"Make sure to keep this page open while you are using KoboldAI, and check back regularly to see if you got a Captcha. Failure to complete the captcha's in time can result in termination of your session or a lower priority towards the TPUs.\n",
"\n",
"Firefox users need to disable the enhanced tracking protection or use a different browser in order to be able to use Google Colab without errors (This is not something we can do anything about, the cookie blocker breaks the Google Drive integration because it uses different domains)."
],
"metadata": {
"id": "zrLGxVCEaqZx"
@@ -64,10 +66,9 @@
"#@title <b><-- Select your model below and then click this to start KoboldAI</b>\n",
"#@markdown You can find a description of the models below along with instructions on how to start KoboldAI.\n",
"\n",
"#@title <b><-- Click this to start KoboldAI</b>\n",
"Model = \"Nerys 13B\" #@param [\"Nerys 13B\", \"Janeway 13B\", \"Shinen 13B\", \"Skein 6B\", \"Janeway 6B\", \"Adventure 6B\", \"Shinen 6B\", \"Lit 6B\", \"NeoX 20B\", \"facebook/opt-13b\", \"KoboldAI/fairseq-dense-13B\", \"EleutherAI/gpt-j-6B\"] {allow-input: true}\n",
"Model = \"Nerys 13B V2\" #@param [\"Nerys 13B V2\", \"Janeway 13B\", \"Shinen 13B\", \"Skein 6B\", \"Janeway 6B\", \"Adventure 6B\", \"Shinen 6B\", \"Lit 6B\", \"NeoX 20B\", \"OPT 13B\", \"Fairseq Dense 13B\", \"GPT-J-6B\"] {allow-input: true}\n",
"Version = \"Official\" #@param [\"Official\", \"United\"] {allow-input: true}\n",
"Provider = \"Localtunnel\" #@param [\"Localtunnel\", \"Cloudflare\"]\n",
"Provider = \"Cloudflare\" #@param [\"Localtunnel\", \"Cloudflare\"]\n",
"\n",
"import os\n",
"try:\n",
@@ -84,8 +85,8 @@
" Model = \"KoboldAI/fairseq-dense-13B-Janeway\"\n",
" path = \"\"\n",
" download = \"\"\n",
"elif Model == \"Nerys 13B\":\n",
" Model = \"KoboldAI/fairseq-dense-13B-Nerys\"\n",
"elif Model == \"Nerys 13B V2\":\n",
" Model = \"KoboldAI/fairseq-dense-13B-Nerys-v2\"\n",
" path = \"\"\n",
" download = \"\"\n",
"elif Model == \"Shinen 13B\":\n",
@@ -93,13 +94,9 @@
" path = \"\"\n",
" download = \"\"\n",
"elif Model == \"NeoX 20B\":\n",
" Model = \"TPUMeshTransformerGPTNeoX\"\n",
" path = \" -p gpt-neox-20b-jax\"\n",
" location = \"colab\"\n",
" download = \" -a https://storage.henk.tech/KoboldAI/neox-20b.txt\"\n",
" extract = \"\"\n",
" Drive = \"Unextracted (Less Space)\"\n",
" ![[ -f /content/drive/MyDrive/KoboldAI/settings/gpt-neox-20b-jax.settings ]] || echo -e \"{\\n \\\"apikey\\\": \\\"\\\",\\n \\\"andepth\\\": 3,\\n \\\"temp\\\": 0.5,\\n \\\"top_p\\\": 0.9,\\n \\\"top_k\\\": 0,\\n \\\"tfs\\\": 1.0,\\n \\\"rep_pen\\\": 1.03,\\n \\\"genamt\\\": 80,\\n \\\"max_length\\\": 2048,\\n \\\"ikgen\\\": 200,\\n \\\"formatoptns\\\": {\\n \\\"frmttriminc\\\": true,\\n \\\"frmtrmblln\\\": false,\\n \\\"frmtrmspch\\\": false,\\n \\\"frmtadsnsp\\\": false\\n },\\n \\\"numseqs\\\": 1,\\n \\\"widepth\\\": 3,\\n \\\"useprompt\\\": true,\\n \\\"adventure\\\": false\\n}\" > /content/drive/MyDrive/KoboldAI/settings/gpt-neox-20b-jax.settings\n",
" Model = \"EleutherAI/gpt-neox-20b\"\n",
" path = \"\"\n",
" download = \"\"\n",
"elif Model == \"Skein 6B\":\n",
" Model = \"KoboldAI/GPT-J-6B-Skein\"\n",
" path = \"\"\n",
@@ -120,14 +117,18 @@
" Model = \"KoboldAI/GPT-J-6B-Shinen\"\n",
" path = \"\"\n",
" download = \"\"\n",
"elif Model == \"Convo 6B\":\n",
" Model = \"hitomi-team/convo-6B\"\n",
"elif Model == \"OPT 13B\":\n",
" Model = \"facebook/opt-13b\"\n",
" path = \"\"\n",
" download = \"\"\n",
"elif Model == \"C1 6B\":\n",
" Model = \"hakurei/c1-6B\"\n",
"elif Model == \"Fairseq Dense 13B\":\n",
" Model = \"KoboldAI/fairseq-dense-13B\"\n",
" path = \"\"\n",
" download = \"\"\n",
"elif Model == \"GPT-J-6B\":\n",
" Model = \"EleutherAI/gpt-j-6B\"\n",
" path = \"\"\n",
" download = \"\"\n",
"else:\n",
" path = \"\"\n",
" download = \"\"\n",
@@ -137,17 +138,17 @@
"else:\n",
" tunnel = \"\"\n",
"\n",
"!wget https://henk.tech/ckds -O - | bash /dev/stdin $path$download -m $Model -g $Version $tunnel"
"!wget https://koboldai.org/ckds -O - | bash /dev/stdin $path$download -m $Model -g $Version $tunnel"
]
},
{
"cell_type": "markdown",
"source": [
"## TPU Edition Model Descriptions\n",
"# TPU Edition Model Descriptions\n",
"\n",
"| Model | Size | Style | Description |\n",
"| --- | --- | --- | --- |\n",
"| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n",
"| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n",
"| [Janeway](https://huggingface.co/KoboldAI/fairseq-dense-13B-Janeway) by Mr Seeker | 13B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n",
"| [Shinen](https://huggingface.co/KoboldAI/fairseq-dense-13B-Shinen) by Mr Seeker | 13B | NSFW | Shinen is an NSFW model designed to be more explicit. Trained on a variety of stories from the website Sexstories it contains many different kinks. |\n",
"| [Skein](https://huggingface.co/KoboldAI/GPT-J-6B-Skein) by VE\\_FORBRYDERNE | 6B | Adventure | Skein is best used with Adventure mode enabled, it consists of a 4 times larger adventure dataset than the Adventure model making it excellent for text adventure gaming. On top of that it also consists of light novel training further expanding its knowledge and writing capabilities. It can be used with the You filter bias if you wish to write Novels with it, but dedicated Novel models can perform better for this task. |\n",
@@ -162,35 +163,47 @@
"\n",
"| Model | Size | Style | Description |\n",
"| --- | --- | --- | --- |\n",
"| [Fairseq-Dense-2.7B-Nerys](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | 2.7B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n",
"| [GPT-Neo-2.7B-Janeway](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | 2.7B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n",
"| [GPT-Neo-2.7B-Picard](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B | Novel | Picard is a model trained for SFW Novels based on GPT-Neo-2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |\n",
"| [GPT-Neo-2.7B-AID](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. |\n",
"| [GPT-Neo-2.7B-Horni-LN](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B | Novel | This model is based on GPT-Neo-2.7B-Horni and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. |\n",
"| [GPT-Neo-2.7B-Horni](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B | NSFW | This model is tuned on Literotica to produce a Novel style model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. |\n",
"| [GPT-Neo-2.7B-Shinen](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |\n",
"| [GPT-Neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. |\n",
"| [Nerys 2.7B](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | 2.7B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n",
"| [Janeway 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | 2.7B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n",
"| [Picard 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B | Novel | Picard is a model trained for SFW Novels based on Neo 2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |\n",
"| [AID 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. |\n",
"| [Horni LN 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B | Novel | This model is based on Horni 2.7B and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. |\n",
"| [Horni 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B | NSFW | This model is tuned on Literotica to produce a Novel style model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. |\n",
"| [Shinen 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you Shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |\n",
"| [Neo 2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. |\n",
"\n",
"| Style | Description |\n",
"| --- | --- |\n",
"| Novel | For regular story writing, not compatible with Adventure mode or other specialty modes. |\n",
"| NSFW | Indicates that the model is strongly biased towards NSFW content and is not suitable for children, work environments or livestreaming. Most NSFW models are also Novel models in nature. |\n",
"| Adventure | These models are excellent for people willing to play KoboldAI like a Text Adventure game and are meant to be used with Adventure mode enabled. Even if you wish to use it as a Novel style model you should always have Adventure mode on and set it to story. These models typically have a strong bias towards the use of the word You and without Adventure mode enabled break the story flow and write actions on your behalf. |\n",
"| Chatbot | These models are specifically trained for chatting and are best used with the Chatmode enabled. Typically trained on either public chatrooms or private chats. |\n",
"| Generic | Generic models are not trained towards anything specific, typically used as a basis for other tasks and models. They can do everything the other models can do, but require much more handholding to work properly. Generic models are an ideal basis for tasks that we have no specific model for, or for experiencing a softprompt in its raw form. |\n",
"\n",
"---\n",
"## Tips to get the most out of Google Colab\n",
"# Tips to get the most out of Google Colab\n",
"- Google will occationally show a Captcha, typically after it has been open for 30 minutes but it can be more frequent if you often use Colab. Make sure to do these properly, or you risk getting your instance shut down and getting a lower priority towards the TPU's.\n",
"- KoboldAI uses Google Drive to store your files and settings, if you wish to upload a softprompt or userscript this can be done directly on the Google Drive website. You can also use this to download backups of your KoboldAI related files or upload models of your own.\n",
"- Don't want to save your stories on Google Drive for privacy reasons? Do not use KoboldAI's save function and instead click Download as .json, this will automatically download the story to your own computer without ever touching Google's harddrives. You can load this back trough the Load from file option.\n",
"- Google shut your instance down unexpectedly? You can still make use of the Download as .json button to recover your story as long as you did not close the KoboldAI window. You can then load this back up in your next session.\n",
"- Done with KoboldAI? Go to the Runtime menu, click on Manage Sessions and terminate your open sessions that you no longer need. This trick can help you maintain higher priority towards getting a TPU.\n",
"- Models stored on Google Drive typically load faster than models we need to download from the internet."
"- Done with KoboldAI? Go to the Runtime menu, click on Manage Sessions and terminate your open sessions that you no longer need. This trick can help you maintain higher priority towards getting a TPU."
],
"metadata": {
"id": "i0-9ARA3c4Fx"
}
},
{
"cell_type": "code",
"source": [
"#@title <b>Model Cleaner</b>\n",
"#@markdown Out of space? Run this to remove all cached models (Google Drive models are not effected).\n",
"!rm /content/KoboldAI-Client/cache/*\n"
],
"metadata": {
"cellView": "form",
"id": "QQZSmoNol04V"
},
"execution_count": null,
"outputs": []
}
],
"metadata": {
@@ -212,4 +225,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
}
}

76
colab/vscode.ipynb
View File

@@ -1,76 +0,0 @@
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "ColabKobold Code",
"provenance": [],
"authorship_tag": "ABX9TyOuIHmyxj4U9dipAib4hfIi",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "TPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/henk717/KoboldAI/blob/united/colab/vscode.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# ColabKobold VSCode Edition\n",
"This is a special edition of ColabKobold aimed at developers, it will not start a KoboldAI instance for you to play KoboldAI and instead will launch a fully functional copy of VSCode for easy development.\n",
"\n",
"Few things of note:\n",
"1. Make sure the desired (or no) accelertor is selected on Colab, you do not want a TPU ban for not using it.\n",
"1. The Version can be replaced with your github URL and appended with -b for the branch for example \"https://github.com/henk717/koboldai -b united\" dependencies will automatically be installed from requirements.txt or requirements_mtj.txt.\n",
"1. With the args you can specify launch options for the KoboldAI Deployment Script, this way you can easily preinstall models to your development instance so you have a model to test with. To install TPU requirements specify the -m TPUMeshTransformerGPTJ argument.\n",
"1. You will need an Ngrok auth token which you can obtain here : https://dashboard.ngrok.com/get-started/your-authtoken\n",
"1. KoboldAI is installed in /content/koboldai-client opening this folder is enough to automatically get full git history and revision support. Also keep in mind that it mounts your Google Drive, be careful comitting directly from this instance.\n",
"1. With Ctrl + Shift + ` you can get a terminal to launch KoboldAI with your own parameters, launching with --colab is recommended.\n",
"\n",
"# [If you are not a developer and are looking to use KoboldAI click here](https://henk.tech/colabkobold)"
],
"metadata": {
"id": "hMRnGz42Xsy3"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "40B1QvI3Xv02"
},
"outputs": [],
"source": [
"#@title VSCode Server\n",
"Version = \"United\" #@param [\"Official\", \"United\"] {allow-input: true}\n",
"Args = \"-m TPUMeshTransformerGPTJ -a https://api.wandb.ai/files/ve-forbryderne/skein/files/gpt-j-6b-skein-jax/aria2.txt\" #@param {type:\"string\"}\n",
"Authtoken = \"\" #@param {type:\"string\"}\n",
"\n",
"from google.colab import drive\n",
"drive.mount('/content/drive/')\n",
"\n",
"!wget https://henk.tech/ckds -O - | bash /dev/stdin -g $Version -i only $Args\n",
"\n",
"!pip install colabcode\n",
"!pip install 'flask>=2.1.0'\n",
"from colabcode import ColabCode\n",
"ColabCode(authtoken=Authtoken)"
]
}
]
}

1
customsettings_template.json Normal file
View File

@@ -0,0 +1 @@
{"aria2_port":null, "breakmodel":null, "breakmodel_disklayers":null, "breakmodel_gpulayers":null, "breakmodel_layers":null, "colab":null, "configname":null, "cpu":null, "host":null, "localtunnel":null, "lowmem":null, "model":null, "ngrok":null, "no_aria2":null, "noaimenu":null, "nobreakmodel":null, "override_delete":null, "override_rename":null, "path":null, "port":null, "quiet":null, "remote":null, "revision":null, "savemodel":null, "unblock":null}

8
docker-standalone/Dockerfile Normal file
View File

@@ -0,0 +1,8 @@
FROM debian
RUN apt update && apt install wget aria2 git bzip2 -y
RUN git clone https://github.com/henk717/koboldai /opt/koboldai
WORKDIR /opt/koboldai
RUN ./install_requirements.sh cuda
COPY docker-helper.sh /opt/koboldai/docker-helper.sh
EXPOSE 5000/tcp
CMD /opt/koboldai/docker-helper.sh

17
docker-standalone/Readme.txt Normal file
View File

@@ -0,0 +1,17 @@
These are the source files for the official versions of the standalone docker and are provided for completeness.
Using these files you will not use any of the local modifications you make, instead it will use the latest github version of KoboldAI as the basis.
If you wish to run KoboldAI containerised with access to the local directory you can do so using docker-cuda.sh or docker-rocm.sh instead.
We do not support ROCm in the standalone docker as it is intended for cloud deployment on CUDA systems.
If you wish to build a ROCm version instead, you can do so by modifying the Dockerfile and changing the install_requirements.sh from cuda to rocm.
Similarly you need to modify the Dockerfile to specify which branch of KoboldAI the docker is being built for.
Usage:
This docker will automatically assume the persistent volume is mounted to /content and will by default not store models there.
The following environment variables exist to adjust the behavior if desired.
KOBOLDAI_DATADIR=/content , this can be used to specify a different default location for your stories, settings, userscripts, etc in case your provider does not let you change the mounted folder path.
KOBOLDAI_MODELDIR= , This variable can be used to make model storage persistent, it can be the same location as your datadir but this is not required.
KOBOLDAI_ARGS= , This variable is built in KoboldAI and can be used to override the default launch options. Right now the docker by default will launch in remote mode, with output hidden from the logs and file management enabled.

47
docker-standalone/docker-helper.sh Normal file
View File

@@ -0,0 +1,47 @@
#!/bin/bash
cd /opt/koboldai
git pull
#./install_requirements.sh cuda
if [[ ! -v KOBOLDAI_DATADIR ]];then
mkdir /content
KOBOLDAI_DATADIR=/content
fi
mkdir $KOBOLDAI_DATADIR/stories
if [[ ! -v KOBOLDAI_MODELDIR ]];then
mkdir $KOBOLDAI_MODELDIR/models
fi
mkdir $KOBOLDAI_DATADIR/settings
mkdir $KOBOLDAI_DATADIR/softprompts
mkdir $KOBOLDAI_DATADIR/userscripts
#mkdir $KOBOLDAI_MODELDIR/cache
cp -rn stories/* $KOBOLDAI_DATADIR/stories/
cp -rn userscripts/* $KOBOLDAI_DATADIR/userscripts/
cp -rn softprompts/* $KOBOLDAI_DATADIR/softprompts/
rm stories
rm -rf stories/
rm userscripts
rm -rf userscripts/
rm softprompts
rm -rf softprompts/
if [[ ! -v KOBOLDAI_MODELDIR ]];then
rm models
rm -rf models/
#rm cache
#rm -rf cache/
fi
ln -s $KOBOLDAI_DATADIR/stories/ stories
ln -s $KOBOLDAI_DATADIR/settings/ settings
ln -s $KOBOLDAI_DATADIR/softprompts/ softprompts
ln -s $KOBOLDAI_DATADIR/userscripts/ userscripts
if [[ ! -v KOBOLDAI_MODELDIR ]];then
ln -s $KOBOLDAI_MODELDIR/models/ models
#ln -s $KOBOLDAI_MODELDIR/cache/ cache
fi
PYTHONUNBUFFERED=1 ./play.sh --remote --quiet --override_delete --override_rename

3
environments/finetuneanon.yml
View File

@@ -6,6 +6,7 @@ channels:
dependencies:
- colorama
- flask-socketio
- flask-session
- pytorch
- cudatoolkit=11.1
- tensorflow-gpu
@@ -15,6 +16,8 @@ dependencies:
- bleach=4.1.0
- pip
- git=2.35.1
- marshmallow>=3.13
- apispec-webframeworks
- pip:
- git+https://github.com/finetuneanon/transformers@gpt-neo-localattention3-rp-b
- flask-cloudflared

3
environments/huggingface.yml
View File

@@ -6,6 +6,7 @@ channels:
dependencies:
- colorama
- flask-socketio
- flask-session
- pytorch=1.11.*
- python=3.8.*
- cudatoolkit=11.1
@@ -16,6 +17,8 @@ dependencies:
- git=2.35.1
- sentencepiece
- protobuf
- marshmallow>=3.13
- apispec-webframeworks
- pip:
- flask-cloudflared
- flask-ngrok

3
environments/rocm-finetune.yml
View File

@@ -5,12 +5,15 @@ channels:
dependencies:
- colorama
- flask-socketio
- flask-session
- python=3.8.*
- eventlet
- markdown
- bleach=4.1.0
- pip
- git=2.35.1
- marshmallow>=3.13
- apispec-webframeworks
- pip:
- --find-links https://download.pytorch.org/whl/rocm4.2/torch_stable.html
- torch

3
environments/rocm.yml
View File

@@ -5,6 +5,7 @@ channels:
dependencies:
- colorama
- flask-socketio
- flask-session
- python=3.8.*
- eventlet
- markdown
@@ -13,6 +14,8 @@ dependencies:
- git=2.35.1
- sentencepiece
- protobuf
- marshmallow>=3.13
- apispec-webframeworks
- pip:
- --find-links https://download.pytorch.org/whl/rocm4.2/torch_stable.html
- torch==1.10.*

23
gensettings.py
View File

@@ -21,7 +21,7 @@ gensettingstf = [
"id": "settemp",
"min": 0.1,
"max": 2.0,
"step": 0.05,
"step": 0.01,
"default": 0.5,
"tooltip": "Randomness of sampling. High values can increase creativity but may make text less sensible. Lower values will make text more predictable but can become repetitious.",
"menu_path": "Settings",
@@ -36,7 +36,7 @@ gensettingstf = [
"id": "settopp",
"min": 0.0,
"max": 1.0,
"step": 0.05,
"step": 0.01,
"default": 0.9,
"tooltip": "Used to discard unlikely text in the sampling process. Lower values will make text more predictable but can become repetitious. (Put this value on 1 to disable its effect)",
"menu_path": "Settings",
@@ -67,7 +67,7 @@ gensettingstf = [
"id": "settfs",
"min": 0.0,
"max": 1.0,
"step": 0.05,
"step": 0.01,
"default": 1.0,
"tooltip": "Alternative sampling method; it is recommended to disable top_p and top_k (set top_p to 1 and top_k to 0) if using this. 0.95 is thought to be a good value. (Put this value on 1 to disable its effect)",
"menu_path": "Settings",
@@ -82,7 +82,7 @@ gensettingstf = [
"id": "settypical",
"min": 0.0,
"max": 1.0,
"step": 0.05,
"step": 0.01,
"default": 1.0,
"tooltip": "Alternative sampling method described in the paper \"Typical Decoding for Natural Language Generation\" (10.48550/ARXIV.2202.00666). The paper suggests 0.2 as a good value for this setting. Set this setting to 1 to disable its effect.",
"menu_path": "Settings",
@@ -315,6 +315,17 @@ gensettingstf = [
"classname": "user",
"name": "nogenmod"
},
{
"uitype": "toggle",
"unit": "bool",
"label": "Full Determinism",
"id": "setfulldeterminism",
"min": 0,
"max": 1,
"step": 1,
"default": 0,
"tooltip": "Causes generation to be fully deterministic -- the model will always output the same thing as long as your story, settings and RNG seed are the same. If this is off, only the sequence of outputs that the model makes will be deterministic."
},
{
"uitype": "toggle",
"unit": "bool",
@@ -579,9 +590,9 @@ formatcontrols = [{
"tooltip": "Remove special characters (@,#,%,^, etc)"
},
{
"label": "Add sentence spacing",
"label": "Automatic spacing",
"id": "frmtadsnsp",
"tooltip": "If the last action ended with punctuation, add a space to the beginning of the next action."
"tooltip": "Add spaces automatically if needed"
},
{
"label": "Single Line",

19
install_requirements.bat
View File

@@ -1,16 +1,11 @@
@echo off
title KoboldAI Runtime Installer (MicroMamba)
echo Please choose one of the following transformers options
echo 1. Official Transformers (Recommended)
echo 2. Finetune Transformers (For old 6B models)
echo.
echo Errors? Rerun this as admin so it can add the needed LongPathsEnabled registery tweak.
echo Installer failed or crashed? Run it again so it can continue.
echo Only Windows 10 and higher officially supported, older Windows installations can't handle the paths.
echo.
SET /P B=Type the number of the desired option and then press ENTER:
Reg add "HKLM\SYSTEM\CurrentControlSet\Control\FileSystem" /v "LongPathsEnabled" /t REG_DWORD /d "1" /f 2>nul
cd /D %~dp0
@@ -19,7 +14,7 @@ if exist miniconda3\ (
echo This is required if you are switching modes, or if you get dependency errors in the game.
echo 1. Yes
echo 2. No
SET /P D=Type the number of the desired option and then press ENTER:
SET /P D=Type the number of the desired option and then press ENTER:
) ELSE (
SET D=Workaround
)
@@ -30,7 +25,7 @@ echo Which installation mode would you like?
echo 1. Temporary Drive Letter (Mounts the folder as drive B:, more stable and portable)
echo 2. Subfolder (Traditional method, can't run in folder paths that contain spaces)
echo.
SET /P M=Type the number of the desired option and then press ENTER:
SET /P M=Type the number of the desired option and then press ENTER:
IF %M%==1 GOTO drivemap
IF %M%==2 GOTO subfolder
ECHO Incorrect choice
@@ -40,7 +35,7 @@ GOTO MODE
:drivemap
echo 3 > loader.settings
subst B: /D >nul
mkdir miniconda3
mkdir miniconda3
subst B: miniconda3
SET TEMP=B:\
SET TMP=B:\
@@ -49,8 +44,7 @@ copy loader.settings B:\loader.settings
copy disconnect-kobold-drive.bat B:\disconnect-kobold-drive.bat
B:
umamba.exe create -r B:\python\ -n base
IF %B%==1 umamba.exe install --no-shortcuts -r B:\python\ -n base -f "%~dp0\environments\huggingface.yml" -y --always-copy
IF %B%==2 umamba.exe install --no-shortcuts -r B:\python\ -n base -f "%~dp0\environments\finetuneanon.yml" -y --always-copy
umamba.exe install --no-shortcuts -r B:\python\ -n base -f "%~dp0\environments\huggingface.yml" -y --always-copy
umamba.exe -r B:\ clean -a -y
rd B:\Python\pkgs /S /Q
subst B: /d
@@ -62,8 +56,7 @@ echo 2 > loader.settings
SET TEMP=%~DP0MINICONDA3
SET TMP=%~DP0MINICONDA3
umamba.exe create -r miniconda3\ -n base
IF %B%==1 umamba.exe install --no-shortcuts -r miniconda3 -n base -f environments\huggingface.yml -y --always-copy
IF %B%==2 umamba.exe install --no-shortcuts -r miniconda3 -n base -f environments\finetuneanon.yml -y --always-copy
umamba.exe install --no-shortcuts -r miniconda3 -n base -f environments\huggingface.yml -y --always-copy
umamba.exe clean -a -y
rd miniconda3\Python\pkgs /S /Q
pause

30
maps/bloom.json Normal file
View File

@@ -0,0 +1,30 @@
{
"mtj_compat": "bloom",
"mtj_pe": "alibi",
"mtj_config_map": {
"d_model": "n_embed",
"n_heads": "num_attention_heads",
"layers": "n_layer"
},
"static_weights": {
"word_embeddings.weight": {"mtj": {"module": "embedding_shard/~/linear", "param": "w", "transforms": ["no_transpose", "vocab_pad"]}},
"word_embeddings_layernorm.weight": {"mtj": {"module": "embedding_shard/~/replicated_layer_norm", "param": "scale"}},
"word_embeddings_layernorm.bias": {"mtj": {"module": "embedding_shard/~/replicated_layer_norm", "param": "offset"}},
"ln_f.weight": {"mtj": {"module": "projection_shard/~/replicated_layer_norm", "param": "scale"}},
"ln_f.bias": {"mtj": {"module": "projection_shard/~/replicated_layer_norm", "param": "offset"}}
},
"layer_weights": {
"h.{layer}.self_attention.query_key_value.weight": {"mtj": {"module": "layer_{layer}/~/combined_qkv", "param": "w"}},
"h.{layer}.self_attention.query_key_value.bias": {"mtj": {"module": "layer_{layer}/~/combined_qkv", "param": "b"}},
"h.{layer}.self_attention.dense.weight": {"mtj": {"module": "layer_{layer}/~/linear_3", "param": "w"}},
"h.{layer}.self_attention.dense.bias": {"mtj": {"module": "layer_{layer}/~/linear_3", "param": "b", "transforms": ["divide_by_shards"]}},
"h.{layer}.mlp.dense_h_to_4h.weight": {"mtj": {"module": "layer_{layer}/~/linear_4", "param": "w"}},
"h.{layer}.mlp.dense_h_to_4h.bias": {"mtj": {"module": "layer_{layer}/~/linear_4", "param": "b"}},
"h.{layer}.mlp.dense_4h_to_h.weight": {"mtj": {"module": "layer_{layer}/~/linear_5", "param": "w"}},
"h.{layer}.mlp.dense_4h_to_h.bias": {"mtj": {"module": "layer_{layer}/~/linear_5", "param": "b", "transforms": ["divide_by_shards"]}},
"h.{layer}.input_layernorm.weight": {"mtj": {"module": "layer_{layer}/~/replicated_layer_norm", "param": "scale"}},
"h.{layer}.input_layernorm.bias": {"mtj": {"module": "layer_{layer}/~/replicated_layer_norm", "param": "offset"}},
"h.{layer}.post_attention_layernorm.weight": {"mtj": {"module": "layer_{layer}/~/replicated_layer_norm_1", "param": "scale"}},
"h.{layer}.post_attention_layernorm.bias": {"mtj": {"module": "layer_{layer}/~/replicated_layer_norm_1", "param": "offset"}}
}
}

60
readme.md
View File

@@ -8,7 +8,7 @@ Stories can be played like a Novel, a text adventure game or used as a chatbot w
### Adventure mode
By default KoboldAI will run in a generic mode optimized for writing, but with the right model you can play this like AI Dungeon without any issues. You can enable this in the settings and bring your own prompt, try generating a random prompt or download one of the prompts available at [prompts.aidg.club](https://prompts.aidg.club) .
By default KoboldAI will run in a generic mode optimized for writing, but with the right model you can play this like AI Dungeon without any issues. You can enable this in the settings and bring your own prompt, try generating a random prompt or download one of the prompts available at [/aids/ Prompts](https://aetherroom.club/).
The gameplay will be slightly different than the gameplay in AI Dungeon because we adopted the Type of the Unleashed fork, giving you full control over all the characters because we do not automatically adapt your sentences behind the scenes. This means you can more reliably control characters that are not you.
@@ -21,7 +21,7 @@ If you want to do this with your friends we advise using the main character as Y
### Writing assistant
If you want to use KoboldAI as a writing assistant this is best done in the regular mode with a model optimized for Novels. These models do not make the assumption that there is a You character and focus on Novel like writing. For writing these will often give you better results than Adventure or Generic models. That said, if you give it a good introduction to the story large generic models like 6B can be used if a more specific model is not available for what you wish to write. You can also try to use models that are not specific to what you wish to do, for example a NSFW Novel model for a SFW story if a SFW model is unavailable. This will mean you will have to correct the model more often because of its bias, but can still produce good enough results if it is familiar enough with your topic.
If you want to use KoboldAI as a writing assistant this is best done in the regular mode with a model optimized for Novels. These models do not make the assumption that there is a You character and focus on Novel like writing. For writing these will often give you better results than Adventure or Generic models. That said, if you give it a good introduction to the story large generic models like 13B can be used if a more specific model is not available for what you wish to write. You can also try to use models that are not specific to what you wish to do, for example a NSFW Novel model for a SFW story if a SFW model is unavailable. This will mean you will have to correct the model more often because of its bias, but can still produce good enough results if it is familiar enough with your topic.
### Chatbot Mode
@@ -48,18 +48,16 @@ If you would like to play KoboldAI online for free on a powerful computer you ca
Each edition features different models and requires different hardware to run, this means that if you are unable to obtain a TPU or a GPU you might still be able to use the other version. The models you can use are listed underneath the edition. To open a Colab click the big link featuring the editions name.
### [Click here for the TPU Edition Colab](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/TPU.ipynb)
## [TPU Edition Model Descriptions](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/TPU.ipynb)
| Model | Size | Style | Description |
| --- | --- | --- | --- |
| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |
| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |
| [Janeway](https://huggingface.co/KoboldAI/fairseq-dense-13B-Janeway) by Mr Seeker | 13B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |
| [Shinen](https://huggingface.co/KoboldAI/fairseq-dense-13B-Shinen) by Mr Seeker | 13B | NSFW | Shinen is an NSFW model designed to be more explicit. Trained on a variety of stories from the website Sexstories it contains many different kinks. |
| [Skein](https://huggingface.co/KoboldAI/GPT-J-6B-Skein) by VE\_FORBRYDERNE | 6B | Adventure | Skein is best used with Adventure mode enabled, it consists of a 4 times larger adventure dataset than the Adventure model making it excellent for text adventure gaming. On top of that it also consists of light novel training further expanding its knowledge and writing capabilities. It can be used with the You filter bias if you wish to write Novels with it, but dedicated Novel models can perform better for this task. |
| [Adventure](https://huggingface.co/KoboldAI/GPT-J-6B-Adventure) by VE\_FORBRYDERNE | 6B | Adventure | Adventure is a 6B model designed to mimick the behavior of AI Dungeon. It is exclusively for Adventure Mode and can take you on the epic and wackey adventures that AI Dungeon players love. It also features the many tropes of AI Dungeon as it has been trained on very similar data. It must be used in second person (You). |
| [Lit](https://huggingface.co/hakurei/lit-6B) by Haru | 6B | NSFW | Lit is a great NSFW model trained by Haru on both a large set of Literotica stories and high quality novels along with tagging support. Creating a high quality model for your NSFW stories. This model is exclusively a novel model and is best used in third person. |
| [Convo](https://huggingface.co/hitomi-team/convo-6B) by Hitomi Team | 6B | Chatbot | Convo-6B is a GPT-J 6B model fine-tuned on a collection of high quality open source datasets which amount to 6 million messages. The primary goal of the model is to provide improved performance and generalization when generating multi-turn dialogue for characters that were not present from within the fine tuning data. The prompted performance has especially improved over the predecessor model [C1-6B](https://huggingface.co/hakurei/c1-6B). |
| [C1](https://huggingface.co/hakurei/c1-6B) by Haru | 6B | Chatbot | C1 has been trained on various internet chatrooms, it makes the basis for an interesting chatbot model and has been optimized to be used in the Chatmode. |
| Neo(X) by EleutherAI | 20B | Generic | NeoX is the largest EleutherAI model currently available, being a generic model it is not particularly trained towards anything and can do a variety of writing, Q&A and coding tasks. 20B's performance is closely compared to the 13B models and it is worth trying both especially if you have a task that does not involve english writing. Its behavior will be similar to the GPT-J-6B model since they are trained on the same dataset but with more sensitivity towards repetition penalty and with more knowledge. |
| [Fairseq Dense](https://huggingface.co/KoboldAI/fairseq-dense-13B) | 13B | Generic | Trained by Facebook Researchers this model stems from the MOE research project within Fairseq. This particular version has been converted by us for use in KoboldAI. It is known to be on par with the larger 20B model from EleutherAI and considered as better for pop culture and language tasks. Because the model has never seen a new line (enter) it may perform worse on formatting and paragraphing. |
| [GPT-J-6B](https://huggingface.co/EleutherAI/gpt-j-6B) by EleutherAI | 6B | Generic | This model serves as the basis for most other 6B models (Some being based on Fairseq Dense instead). Being trained on the Pile and not biased towards anything in particular it is suitable for a variety of tasks such as writing, Q&A and coding tasks. You will likely get better result with larger generic models or finetuned models. |
@@ -68,21 +66,23 @@ Each edition features different models and requires different hardware to run, t
| Model | Size | Style | Description |
| --- | --- | --- | --- |
| [Fairseq-Dense-2.7B-Nerys](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | 2.7B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |
| [GPT-Neo-2.7B-Janeway](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | 2.7B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |
| [GPT-Neo-2.7B-Picard](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B | Novel | Picard is a model trained for SFW Novels based on GPT-Neo-2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |
| [GPT-Neo-2.7B-AID](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. |
| [GPT-Neo-2.7B-Horni-LN](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B | Novel | This model is based on GPT-Neo-2.7B-Horni and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. |
| [GPT-Neo-2.7B-Horni](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B | NSFW | This model is tuned on Literotica to produce a Novel style model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. |
| [GPT-Neo-2.7B-Shinen](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |
| [GPT-Neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. |
| [Nerys 2.7B](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | 2.7B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |
| [Janeway 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | 2.7B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |
| [Picard 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B | Novel | Picard is a model trained for SFW Novels based on Neo 2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |
| [AID 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. |
| [Horni LN 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B | Novel | This model is based on Horni 2.7B and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. |
| [Horni 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B | NSFW | This model is tuned on Literotica to produce a Novel style model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. |
| [Shinen 2.7B ](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you Shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |
| [Neo 2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. |
| Style | Description |
### Styles
| Type | Description |
| --- | --- |
| Novel | For regular story writing, not compatible with Adventure mode or other specialty modes. |
| NSFW | Indicates that the model is strongly biased towards NSFW content and is not suitable for children, work environments or livestreaming. Most NSFW models are also Novel models in nature. |
| Adventure | These models are excellent for people willing to play KoboldAI like a Text Adventure game and are meant to be used with Adventure mode enabled. Even if you wish to use it as a Novel style model you should always have Adventure mode on and set it to story. These models typically have a strong bias towards the use of the word You and without Adventure mode enabled break the story flow and write actions on your behalf. |
| Chatbot | These models are specifically trained for chatting and are best used with the Chatmode enabled. Typically trained on either public chatrooms or private chats. |
| Adventure | These models are excellent for people willing to play KoboldAI like a Text Adventure game and are meant to be used with Adventure mode enabled. Even if you wish to use it as a Novel Type model you should always have Adventure mode on and set it to story. These models typically have a strong bias towards the use of the word You and without Adventure mode enabled break the story flow and write actions on your behalf. |
| Hybrid | Hybrid models are a blend between different Types, for example they are trained on both Novel stories and Adventure stories. These models are great variety models that you can use for multiple different playTypes and modes, but depending on your usage you may need to enable Adventure Mode or the You bias (in userscripts). |
| Generic | Generic models are not trained towards anything specific, typically used as a basis for other tasks and models. They can do everything the other models can do, but require much more handholding to work properly. Generic models are an ideal basis for tasks that we have no specific model for, or for experiencing a softprompt in its raw form. |
## Tips to get the most out of Google Colab
@@ -94,28 +94,6 @@ Each edition features different models and requires different hardware to run, t
* Done with KoboldAI? Go to the Runtime menu, click on Manage Sessions and terminate your open sessions that you no longer need. This trick can help you maintain higher priority towards getting a TPU.
* Models stored on Google Drive typically load faster than models we need to download from the internet.
### [Click here for the GPU Edition Colab](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/GPU.ipynb)
| Model | Size | Type | Description |
| --- | --- | --- | --- |
| [GPT-Neo-2.7B-Picard](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B GPU | Novel | Picard is a model trained for SFW Novels based on GPT-Neo-2.7B. It is focused on Novel Type writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |
| [GPT-Neo-2.7B-AID](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B GPU | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. |
| [GPT-Neo-2.7B-Horni-LN](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B GPU | Novel | This model is based on GPT-Neo-2.7B-Horni and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. |
| [GPT-Neo-2.7B-Horni](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B GPU | NSFW | This model is tuned on Literotica to produce a Novel Type model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. |
| [GPT-Neo-2.7B-Shinen](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B GPU | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |
| [GPT-Neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B GPU | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. |
### Model Types
| Type | Description |
| --- | --- |
| Novel | For regular story writing, not compatible with Adventure mode or other specialty modes. |
| NSFW | Indicates that the model is strongly biased towards NSFW content and is not suitable for children, work environments or livestreaming. Most NSFW models are also Novel models in nature. |
| Adventure | These models are excellent for people willing to play KoboldAI like a Text Adventure game and are meant to be used with Adventure mode enabled. Even if you wish to use it as a Novel Type model you should always have Adventure mode on and set it to story. These models typically have a strong bias towards the use of the word You and without Adventure mode enabled break the story flow and write actions on your behalf. |
| Chatbot | These models are specifically trained for chatting and are best used with the Chatmode enabled. Typically trained on either public chatrooms or private chats. |
| Hybrid | Hybrid models are a blend between different Types, for example they are trained on both Novel stories and Adventure stories. These models are great variety models that you can use for multiple different playTypes and modes, but depending on your usage you may need to enable Adventure Mode or the You bias (in userscripts). |
| Generic | Generic models are not trained towards anything specific, typically used as a basis for other tasks and models. They can do everything the other models can do, but require much more handholding to work properly. Generic models are an ideal basis for tasks that we have no specific model for, or for experiencing a softprompt in its raw form. |
## Install KoboldAI on your own computer
KoboldAI has a large number of dependencies you will need to install on your computer, unfortunately Python does not make it easy for us to provide instructions that work for everyone. The instructions below will work on most computers, but if you have multiple versions of Python installed conflicts can occur.
@@ -195,11 +173,11 @@ If you get these errors you either did not select the correct folder for your cu
Softprompts (also known as Modules in other products) are addons that can change the output of existing models. For example you may load a softprompt that biases the AI towards a certain subject and style like transcripts from your favorite TV show.
Since these softprompts are often based on existing franchises we currently do not bundle any of them with KoboldAI due to copyright concerns (We do not want to put the entire project at risk). Instead look at community resources like #softprompts on the [KoboldAI Discord](https://discord.gg/XuQWadgU9k) or the [community hosted mirror](https://storage.henk.tech/KoboldAI/softprompts/) .
Since these softprompts are often based on existing franchises we currently do not bundle any of them with KoboldAI due to copyright concerns (We do not want to put the entire project at risk). Instead look at community resources like #softprompts on the [KoboldAI Discord](https://discord.gg/XuQWadgU9k) or the [community hosted mirror](https://storage.henk.tech/KoboldAI/softprompts/).
That way we are better protected from any DMCA claims as things can be taken down easier than directly on Github. If you have a copyright free softprompt that you made from scratch and is not based on existing IP that you would like to see officially bundled with KoboldAI issue a pull request with your softprompt.
Training softprompts can be done for free with the [mtj-softtuner colab](https://colab.research.google.com/github/VE-FORBRYDERNE/mtj-softtuner/blob/main/mtj-softtuner.ipynb) , in that case you can leave most of the settings default. Your source data needs to be a folder with text files that are UTF-8 formatted and contain Unix line endings.
Training softprompts can be done for free with the [Easy Softprompt Tuner](https://colab.research.google.com/gist/henk717/281fd57ebd2e88d852ef9dcc3f29bebf/easy-softprompt-tuner.ipynb#sandboxMode=true), in that case you can leave most of the settings default. Your source data needs to be a folder with text files that are UTF-8 formatted and contain Unix line endings.
## Userscripts

4
requirements.txt
View File

@@ -13,4 +13,6 @@ sentencepiece
protobuf
accelerate
python-socketio[client]
flask_session
flask_session
marshmallow>=3.13
apispec-webframeworks

4
requirements_mtj.txt
View File

@@ -18,4 +18,6 @@ lupa==1.10
markdown
bleach==4.1.0
python-socketio[client]
flask_session
flask-session
marshmallow>=3.13
apispec-webframeworks

673
static/application.js
View File

@@ -78,6 +78,8 @@ var rs_accept;
var rs_close;
var seqselmenu;
var seqselcontents;
var stream_preview;
var token_prob_container;
var storyname = null;
var memorymode = false;
@@ -87,6 +89,7 @@ var wiscroll = 0;
var editmode = false;
var connected = false;
var newly_loaded = true;
var all_modified_chunks = new Set();
var modified_chunks = new Set();
var empty_chunks = new Set();
var gametext_bound = false;
@@ -102,6 +105,7 @@ var gamestate = "";
var gamesaved = true;
var modelname = null;
var model = "";
var ignore_stream = false;
// This is true iff [we're in macOS and the browser is Safari] or [we're in iOS]
var using_webkit_patch = true;
@@ -129,6 +133,7 @@ var adventure = false;
var chatmode = false;
var sliders_throttle = getThrottle(200);
var submit_throttle = null;
//=================================================================//
// METHODS
@@ -507,6 +512,16 @@ function addWiLine(ob) {
$(".wisortable-excluded-dynamic").removeClass("wisortable-excluded-dynamic");
$(this).parent().css("max-height", "").find(".wicomment").find(".form-control").css("max-height", "");
});
for (const wientry of document.getElementsByClassName("wientry")) {
// If we are uninitialized, skip.
if ($(wientry).closest(".wilistitem-uninitialized").length) continue;
// add() will not add if the class is already present
wientry.classList.add("tokens-counted");
}
registerTokenCounters();
}
function addWiFolder(uid, ob) {
@@ -830,6 +845,7 @@ function exitMemoryMode() {
button_actmem.html("Memory");
show([button_actback, button_actfwd, button_actretry, button_actwi]);
input_text.val("");
updateInputBudget(input_text[0]);
// Hide Author's Note field
anote_menu.slideUp("fast");
}
@@ -886,11 +902,25 @@ function formatChunkInnerText(chunk) {
}
function dosubmit(disallow_abort) {
beginStream();
submit_start = Date.now();
var txt = input_text.val().replace(/\u00a0/g, " ");
if((disallow_abort || gamestate !== "wait") && !memorymode && !gamestarted && ((!adventure || !action_mode) && txt.trim().length == 0)) {
return;
}
chunkOnFocusOut("override");
// Wait for editor changes to be applied before submitting
submit_throttle = getThrottle(70);
submit_throttle.txt = txt;
submit_throttle.disallow_abort = disallow_abort;
submit_throttle(0, _dosubmit);
}
function _dosubmit() {
beginStream();
var txt = submit_throttle.txt;
var disallow_abort = submit_throttle.disallow_abort;
submit_throttle = null;
input_text.val("");
hideMessage();
hidegenseqs();
@@ -1030,6 +1060,18 @@ function buildLoadModelList(ar, menu, breadcrumbs, showdelete) {
if (breadcrumbs.length > 0) {
$("#loadmodellistbreadcrumbs").append("<hr size='1'>")
}
//If we're in the custom load menu (we need to send the path data back in that case)
if(['NeoCustom', 'GPT2Custom'].includes(menu)) {
$("#loadmodel"+i).off("click").on("click", (function () {
return function () {
socket.send({'cmd': 'selectmodel', 'data': $(this).attr("name"), 'path': $(this).attr("pretty_name")});
highlightLoadLine($(this));
}
})(i));
$("#custommodelname").removeClass("hidden");
$("#custommodelname")[0].setAttribute("menu", menu);
}
for(i=0; i<ar.length; i++) {
if (Array.isArray(ar[i][0])) {
full_path = ar[i][0][0];
@@ -1043,11 +1085,12 @@ function buildLoadModelList(ar, menu, breadcrumbs, showdelete) {
html = "<div class=\"flex\">\
<div class=\"loadlistpadding\"></div>"
//if the menu item is a link to another menu
if(ar[i][3]) {
console.log(ar[i]);
if((ar[i][3]) || (['Load a model from its directory', 'Load an old GPT-2 model (eg CloverEdition)'].includes(ar[i][0]))) {
html = html + "<span class=\"loadlisticon loadmodellisticon-folder oi oi-folder allowed\" aria-hidden=\"true\"></span>"
} else {
//this is a model
html = html + "<div class=\"loadlistpadding\"></div>"
html = html + "<div class=\"loadlisticon oi oi-caret-right allowed\"></div>&nbsp;&nbsp;&nbsp;"
}
//now let's do the delete icon if applicable
@@ -1065,6 +1108,7 @@ function buildLoadModelList(ar, menu, breadcrumbs, showdelete) {
</div>"
loadmodelcontent.append(html);
//If this is a menu
console.log(ar[i]);
if(ar[i][3]) {
$("#loadmodel"+i).off("click").on("click", (function () {
return function () {
@@ -1072,27 +1116,29 @@ function buildLoadModelList(ar, menu, breadcrumbs, showdelete) {
disableButtons([load_model_accept]);
}
})(i));
//If we're in the custom load menu (we need to send the path data back in that case)
} else if(['NeoCustom', 'GPT2Custom'].includes(menu)) {
$("#loadmodel"+i).off("click").on("click", (function () {
return function () {
socket.send({'cmd': 'selectmodel', 'data': $(this).attr("name"), 'path': $(this).attr("pretty_name")});
highlightLoadLine($(this));
}
})(i));
$("#custommodelname").removeClass("hidden");
$("#custommodelname")[0].setAttribute("menu", menu);
//Normal load
} else {
$("#loadmodel"+i).off("click").on("click", (function () {
return function () {
$("#use_gpu_div").addClass("hidden");
$("#modelkey").addClass("hidden");
$("#modellayers").addClass("hidden");
socket.send({'cmd': 'selectmodel', 'data': $(this).attr("name")});
highlightLoadLine($(this));
}
})(i));
if (['NeoCustom', 'GPT2Custom'].includes(menu)) {
$("#loadmodel"+i).off("click").on("click", (function () {
return function () {
$("#use_gpu_div").addClass("hidden");
$("#modelkey").addClass("hidden");
$("#modellayers").addClass("hidden");
socket.send({'cmd': 'selectmodel', 'data': $(this).attr("name"), 'path': $(this).attr("pretty_name")});
highlightLoadLine($(this));
}
})(i));
} else {
$("#loadmodel"+i).off("click").on("click", (function () {
return function () {
$("#use_gpu_div").addClass("hidden");
$("#modelkey").addClass("hidden");
$("#modellayers").addClass("hidden");
socket.send({'cmd': 'selectmodel', 'data': $(this).attr("name")});
highlightLoadLine($(this));
}
})(i));
}
}
}
}
@@ -1522,14 +1568,30 @@ function chunkOnTextInput(event) {
r.deleteContents();
}
// In Chrome the added <br/> will go outside of the chunks if we press
// In Chrome and Safari the added <br/> will go outside of the chunks if we press
// enter at the end of the story in the editor, so this is here
// to put the <br/> back in the right place
var br = $("#_EDITOR_LINEBREAK_")[0];
if(br.parentNode === game_text[0]) {
var parent = br.previousSibling;
if(br.previousSibling.nodeType !== 1) {
parent = br.previousSibling.previousSibling;
br.previousSibling.previousSibling.appendChild(br.previousSibling);
}
if(parent.lastChild.tagName === "BR") {
parent.lastChild.remove(); // Chrome and Safari also insert an extra <br/> in this case for some reason so we need to remove it
if(using_webkit_patch) {
// Safari on iOS has a bug where it selects all text in the last chunk of the story when this happens so we collapse the selection to the end of the chunk in that case
setTimeout(function() {
var s = getSelection();
var r = s.getRangeAt(0);
r.selectNodeContents(parent);
r.collapse(false);
s.removeAllRanges();
s.addRange(r);
}, 2);
}
}
br.previousSibling.appendChild(br);
r.selectNodeContents(br.parentNode);
s.removeAllRanges();
@@ -1711,22 +1773,29 @@ function applyChunkDeltas(nodes) {
var chunks = Array.from(buildChunkSetFromNodeArray(nodes));
for(var i = 0; i < chunks.length; i++) {
modified_chunks.add(chunks[i]);
all_modified_chunks.add(chunks[i]);
}
setTimeout(function() {
var chunks = Array.from(modified_chunks);
var selected_chunks = buildChunkSetFromNodeArray(getSelectedNodes());
for(var i = 0; i < chunks.length; i++) {
var chunk = document.getElementById("n" + chunks[i]);
if(chunk && formatChunkInnerText(chunk).length != 0 && chunks[i] != '0') {
if(chunk && formatChunkInnerText(chunk).trim().length != 0 && chunks[i] != '0') {
if(!selected_chunks.has(chunks[i])) {
modified_chunks.delete(chunks[i]);
socket.send({'cmd': 'inlineedit', 'chunk': chunks[i], 'data': formatChunkInnerText(chunk)});
if(submit_throttle !== null) {
submit_throttle(0, _dosubmit);
}
}
empty_chunks.delete(chunks[i]);
} else {
if(!selected_chunks.has(chunks[i])) {
modified_chunks.delete(chunks[i]);
socket.send({'cmd': 'inlineedit', 'chunk': chunks[i], 'data': ''});
socket.send({'cmd': 'inlineedit', 'chunk': chunks[i], 'data': formatChunkInnerText(chunk)});
if(submit_throttle !== null) {
submit_throttle(0, _dosubmit);
}
}
empty_chunks.add(chunks[i]);
}
@@ -1748,6 +1817,9 @@ function syncAllModifiedChunks(including_selected_chunks=false) {
empty_chunks.delete(chunks[i]);
}
socket.send({'cmd': 'inlineedit', 'chunk': chunks[i], 'data': data});
if(submit_throttle !== null) {
submit_throttle(0, _dosubmit);
}
}
}
}
@@ -1800,10 +1872,16 @@ function restorePrompt() {
if(this.innerText.trim().length) {
saved_prompt = this.innerText.trim();
socket.send({'cmd': 'inlinedelete', 'data': this.getAttribute("n")});
if(submit_throttle !== null) {
submit_throttle(0, _dosubmit);
}
this.parentNode.removeChild(this);
return false;
}
socket.send({'cmd': 'inlinedelete', 'data': this.getAttribute("n")});
if(submit_throttle !== null) {
submit_throttle(0, _dosubmit);
}
this.parentNode.removeChild(this);
});
}
@@ -1818,6 +1896,9 @@ function restorePrompt() {
modified_chunks.delete('0');
empty_chunks.delete('0');
socket.send({'cmd': 'inlineedit', 'chunk': '0', 'data': saved_prompt});
if(submit_throttle !== null) {
submit_throttle(0, _dosubmit);
}
}
function deleteEmptyChunks() {
@@ -1829,13 +1910,21 @@ function deleteEmptyChunks() {
restorePrompt();
} else {
socket.send({'cmd': 'inlinedelete', 'data': chunks[i]});
if(submit_throttle !== null) {
submit_throttle(0, _dosubmit);
}
}
}
if(modified_chunks.has('0')) {
modified_chunks.delete(chunks[i]);
socket.send({'cmd': 'inlineedit', 'chunk': chunks[i], 'data': formatChunkInnerText(document.getElementById("n0"))});
if(submit_throttle !== null) {
submit_throttle(0, _dosubmit);
}
}
if(gamestarted) {
saved_prompt = formatChunkInnerText($("#n0")[0]);
}
saved_prompt = formatChunkInnerText($("#n0")[0]);
}
function highlightEditingChunks() {
@@ -1858,6 +1947,70 @@ function highlightEditingChunks() {
}
}
function cleanupChunkWhitespace() {
unbindGametext();
var chunks = Array.from(all_modified_chunks);
for(var i = 0; i < chunks.length; i++) {
var original_chunk = document.getElementById("n" + chunks[i]);
if(original_chunk === null || original_chunk.innerText.trim().length === 0) {
all_modified_chunks.delete(chunks[i]);
modified_chunks.delete(chunks[i]);
empty_chunks.add(chunks[i]);
}
}
// Merge empty chunks with the next chunk
var chunks = Array.from(empty_chunks);
chunks.sort(function(e) {parseInt(e)});
for(var i = 0; i < chunks.length; i++) {
if(chunks[i] == "0") {
continue;
}
var original_chunk = document.getElementById("n" + chunks[i]);
if(original_chunk === null) {
continue;
}
var chunk = original_chunk.nextSibling;
while(chunk) {
if(chunk.tagName === "CHUNK") {
break;
}
chunk = chunk.nextSibling;
}
if(chunk) {
chunk.innerText = original_chunk.innerText + chunk.innerText;
if(original_chunk.innerText.length != 0 && !modified_chunks.has(chunk.getAttribute("n"))) {
modified_chunks.add(chunk.getAttribute("n"));
}
}
original_chunk.innerText = "";
}
// Move whitespace at the end of non-empty chunks into the beginning of the next non-empty chunk
var chunks = Array.from(all_modified_chunks);
chunks.sort(function(e) {parseInt(e)});
for(var i = 0; i < chunks.length; i++) {
var original_chunk = document.getElementById("n" + chunks[i]);
var chunk = original_chunk.nextSibling;
while(chunk) {
if(chunk.tagName === "CHUNK" && !empty_chunks.has(chunk.getAttribute("n"))) {
break;
}
chunk = chunk.nextSibling;
}
var ln = original_chunk.innerText.trimEnd().length;
if (chunk) {
chunk.innerText = original_chunk.innerText.substring(ln) + chunk.innerText;
if(ln != original_chunk.innerText.length && !modified_chunks.has(chunk.getAttribute("n"))) {
modified_chunks.add(chunk.getAttribute("n"));
}
}
original_chunk.innerText = original_chunk.innerText.substring(0, ln);
}
bindGametext();
}
// This gets run every time the text in a chunk is edited
// or a chunk is deleted
function chunkOnDOMMutate(mutations, observer) {
@@ -1929,13 +2082,15 @@ function chunkOnKeyDownSelectionChange(event) {
// This gets run when you defocus the editor by clicking
// outside of the editor or by pressing escape or tab
function chunkOnFocusOut(event) {
if(!gametext_bound || !allowedit || event.target !== game_text[0]) {
if(event !== "override" && (!gametext_bound || !allowedit || event.target !== game_text[0])) {
return;
}
setTimeout(function() {
if(document.activeElement === game_text[0] || game_text[0].contains(document.activeElement)) {
return;
}
cleanupChunkWhitespace();
all_modified_chunks = new Set();
syncAllModifiedChunks(true);
setTimeout(function() {
var blurred = game_text[0] !== document.activeElement;
@@ -1959,6 +2114,20 @@ function unbindGametext() {
gametext_bound = false;
}
function beginStream() {
ignore_stream = false;
token_prob_container[0].innerHTML = "";
}
function endStream() {
// Clear stream, the real text is about to be displayed.
ignore_stream = true;
if (stream_preview) {
stream_preview.remove();
stream_preview = null;
}
}
function update_gpu_layers() {
var gpu_layers
gpu_layers = 0;
@@ -1987,6 +2156,45 @@ function RemoveAllButFirstOption(selectElement) {
}
}
function interpolateRGB(color0, color1, t) {
return [
color0[0] + ((color1[0] - color0[0]) * t),
color0[1] + ((color1[1] - color0[1]) * t),
color0[2] + ((color1[2] - color0[2]) * t),
]
}
function updateInputBudget(inputElement) {
let data = {"unencoded": inputElement.value, "field": inputElement.id};
if (inputElement.id === "anoteinput") {
data["anotetemplate"] = $("#anotetemplate").val();
}
socket.send({"cmd": "getfieldbudget", "data": data});
}
function registerTokenCounters() {
// Add token counters to all input containers with the class of "tokens-counted",
// if a token counter is not already a child of said container.
for (const el of document.getElementsByClassName("tokens-counted")) {
if (el.getElementsByClassName("input-token-usage").length) continue;
let span = document.createElement("span");
span.classList.add("input-token-usage");
span.innerText = "?/? Tokens";
el.appendChild(span);
let inputElement = el.querySelector("input, textarea");
inputElement.addEventListener("input", function() {
updateInputBudget(this);
});
updateInputBudget(inputElement);
}
}
//=================================================================//
// READY/RUNTIME
//=================================================================//
@@ -2078,9 +2286,16 @@ $(document).ready(function(){
rs_close = $("#btn_rsclose");
seqselmenu = $("#seqselmenu");
seqselcontents = $("#seqselcontents");
token_prob_container = $("#token_prob_container");
token_prob_menu = $("#token_prob_menu");
// Connect to SocketIO server
socket = io.connect(window.document.origin, {transports: ['polling', 'websocket'], closeOnBeforeunload: false, query:{"ui": "1"}});
socket.on('load_popup', function(data){load_popup(data);});
socket.on('popup_items', function(data){popup_items(data);});
socket.on('popup_breadcrumbs', function(data){popup_breadcrumbs(data);});
socket.on('popup_edit_file', function(data){popup_edit_file(data);});
socket.on('error_popup', function(data){error_popup(data);});
socket.on('from_server', function(msg) {
//console.log(msg);
@@ -2130,6 +2345,75 @@ $(document).ready(function(){
active_element.focus();
})();
$("body").addClass("connected");
} else if (msg.cmd == "streamtoken") {
// Sometimes the stream_token messages will come in too late, after
// we have recieved the full text. This leads to some stray tokens
// appearing after the output. To combat this, we only allow tokens
// to be displayed after requesting and before recieving text.
if (ignore_stream) return;
let streamingEnabled = $("#setoutputstreaming")[0].checked;
let probabilitiesEnabled = $("#setshowprobs")[0].checked;
if (!streamingEnabled && !probabilitiesEnabled) return;
if (!stream_preview && streamingEnabled) {
stream_preview = document.createElement("span");
game_text.append(stream_preview);
}
for (const token of msg.data) {
if (streamingEnabled) stream_preview.innerText += token.decoded;
if (probabilitiesEnabled) {
// Probability display
let probDiv = document.createElement("div");
probDiv.classList.add("token-probs");
let probTokenSpan = document.createElement("span");
probTokenSpan.classList.add("token-probs-header");
probTokenSpan.innerText = token.decoded.replaceAll("\n", "\\n");
probDiv.appendChild(probTokenSpan);
let probTable = document.createElement("table");
let probTBody = document.createElement("tbody");
probTable.appendChild(probTBody);
for (const probToken of token.probabilities) {
let tr = document.createElement("tr");
let rgb = interpolateRGB(
[255, 255, 255],
[0, 255, 0],
probToken.score
).map(Math.round);
let color = `rgb(${rgb.join(", ")})`;
if (probToken.decoded === token.decoded) {
tr.classList.add("token-probs-final-token");
}
let tds = {};
for (const property of ["tokenId", "decoded", "score"]) {
let td = document.createElement("td");
td.style.color = color;
tds[property] = td;
tr.appendChild(td);
}
tds.tokenId.innerText = probToken.tokenId;
tds.decoded.innerText = probToken.decoded.toString().replaceAll("\n", "\\n");
tds.score.innerText = (probToken.score * 100).toFixed(2) + "%";
probTBody.appendChild(tr);
}
probDiv.appendChild(probTable);
token_prob_container.append(probDiv);
}
}
scrollToBottom();
} else if(msg.cmd == "updatescreen") {
var _gamestarted = gamestarted;
gamestarted = msg.gamestarted;
@@ -2140,6 +2424,7 @@ $(document).ready(function(){
unbindGametext();
allowedit = gamestarted && $("#allowediting").prop('checked');
game_text.attr('contenteditable', allowedit);
all_modified_chunks = new Set();
modified_chunks = new Set();
empty_chunks = new Set();
game_text.html(msg.data);
@@ -2159,6 +2444,7 @@ $(document).ready(function(){
scrollToBottom();
} else if(msg.cmd == "updatechunk") {
hideMessage();
game_text.attr('contenteditable', allowedit);
if (typeof submit_start !== 'undefined') {
$("#runtime")[0].innerHTML = `Generation time: ${Math.round((Date.now() - submit_start)/1000)} sec`;
delete submit_start;
@@ -2178,7 +2464,11 @@ $(document).ready(function(){
} else if (!empty_chunks.has(index.toString())) {
// Append at the end
unbindGametext();
var lc = game_text[0].lastChild;
// game_text can contain things other than chunks (stream
// preview), so we use querySelector to get the last chunk.
var lc = game_text[0].querySelector("chunk:last-of-type");
if(lc.tagName === "CHUNK" && lc.lastChild !== null && lc.lastChild.tagName === "BR") {
lc.removeChild(lc.lastChild);
}
@@ -2194,7 +2484,11 @@ $(document).ready(function(){
var element = game_text.children('#n' + index);
if(element.length) {
unbindGametext();
if((element[0].nextSibling === null || element[0].nextSibling.nodeType !== 1 || element[0].nextSibling.tagName !== "CHUNK") && element[0].previousSibling !== null && element[0].previousSibling.tagName === "CHUNK") {
if(
(element[0].nextSibling === null || element[0].nextSibling.nodeType !== 1 || element[0].nextSibling.tagName !== "CHUNK")
&& element[0].previousSibling !== null
&& element[0].previousSibling.tagName === "CHUNK"
) {
element[0].previousSibling.appendChild(document.createElement("br"));
}
element.remove(); // Remove the chunk
@@ -2204,6 +2498,7 @@ $(document).ready(function(){
} else if(msg.cmd == "setgamestate") {
// Enable or Disable buttons
if(msg.data == "ready") {
endStream();
enableSendBtn();
enableButtons([button_actmem, button_actwi, button_actback, button_actfwd, button_actretry]);
hideWaitAnimation();
@@ -2243,6 +2538,7 @@ $(document).ready(function(){
memorytext = msg.data;
input_text.val(msg.data);
}
updateInputBudget(input_text[0]);
} else if(msg.cmd == "setmemory") {
memorytext = msg.data;
if(memorymode) {
@@ -2364,6 +2660,7 @@ $(document).ready(function(){
} else if(msg.cmd == "setanote") {
// Set contents of Author's Note field
anote_input.val(msg.data);
updateInputBudget(anote_input[0]);
} else if(msg.cmd == "setanotetemplate") {
// Set contents of Author's Note Template field
$("#anotetemplate").val(msg.data);
@@ -2390,6 +2687,17 @@ $(document).ready(function(){
} else if(msg.cmd == "updatesingleline") {
// Update toggle state
$("#singleline").prop('checked', msg.data).change();
} else if(msg.cmd == "updateoutputstreaming") {
// Update toggle state
$("#setoutputstreaming").prop('checked', msg.data).change();
} else if(msg.cmd == "updateshowprobs") {
$("#setshowprobs").prop('checked', msg.data).change();
if(msg.data) {
token_prob_menu.removeClass("hidden");
} else {
token_prob_menu.addClass("hidden");
}
} else if(msg.cmd == "allowtoggle") {
// Allow toggle change states to propagate
allowtoggle = msg.data;
@@ -2564,6 +2872,9 @@ $(document).ready(function(){
} else if(msg.cmd == "updatenogenmod") {
// Update toggle state
$("#setnogenmod").prop('checked', msg.data).change();
} else if(msg.cmd == "updatefulldeterminism") {
// Update toggle state
$("#setfulldeterminism").prop('checked', msg.data).change();
} else if(msg.cmd == "runs_remotely") {
remote = true;
hide([button_savetofile, button_import, button_importwi]);
@@ -2589,6 +2900,8 @@ $(document).ready(function(){
if (msg.key) {
$("#modelkey").removeClass("hidden");
$("#modelkey")[0].value = msg.key_value;
//if we're in the API list, disable to load button until the model is selected (after the API Key is entered)
disableButtons([load_model_accept]);
} else {
$("#modelkey").addClass("hidden");
@@ -2626,6 +2939,7 @@ $(document).ready(function(){
}
} else if(msg.cmd == 'oai_engines') {
$("#oaimodel").removeClass("hidden")
enableButtons([load_model_accept]);
selected_item = 0;
length = $("#oaimodel")[0].options.length;
for (let i = 0; i < length; i++) {
@@ -2648,6 +2962,7 @@ $(document).ready(function(){
$("#showmodelnamecontainer").removeClass("hidden");
} else if(msg.cmd == 'hide_model_name') {
$("#showmodelnamecontainer").addClass("hidden");
location.reload();
//console.log("Closing window");
} else if(msg.cmd == 'model_load_status') {
$("#showmodelnamecontent").html("<div class=\"flex\"><div class=\"loadlistpadding\"></div><div class=\"loadlistitem\" style='align: left'>" + msg.data + "</div></div>");
@@ -2661,7 +2976,18 @@ $(document).ready(function(){
opt.innerHTML = engine[1];
$("#oaimodel")[0].appendChild(opt);
}
} else if(msg.cmd == 'showfieldbudget') {
let inputElement = document.getElementById(msg.data.field);
let tokenBudgetElement = inputElement.parentNode.getElementsByClassName("input-token-usage")[0];
if (msg.data.max === null) {
tokenBudgetElement.innerText = "";
} else {
let tokenLength = msg.data.length ?? "?";
let tokenMax = msg.data.max ?? "?";
tokenBudgetElement.innerText = `${tokenLength}/${tokenMax} Tokens`;
}
}
enableButtons([load_model_accept]);
});
socket.on('disconnect', function() {
@@ -2691,6 +3017,12 @@ $(document).ready(function(){
chunkOnFocusOut
);
mutation_observer = new MutationObserver(chunkOnDOMMutate);
$("#gamescreen").on('click', function(e) {
if(this !== e.target) {
return;
}
document.activeElement.blur();
});
// This is required for the editor to work correctly in Firefox on desktop
// because the gods of HTML and JavaScript say so
@@ -2776,6 +3108,7 @@ $(document).ready(function(){
});
button_actretry.on("click", function(ev) {
beginStream();
hideMessage();
socket.send({'cmd': 'retry', 'chatname': chatmode ? chat_name.val() : undefined, 'data': ''});
hidegenseqs();
@@ -3022,6 +3355,7 @@ $(document).ready(function(){
});
rs_accept.on("click", function(ev) {
beginStream();
hideMessage();
socket.send({'cmd': 'rndgame', 'memory': $("#rngmemory").val(), 'data': topic.val()});
hideRandomStoryPopup();
@@ -3095,4 +3429,287 @@ $(document).ready(function(){
return true;
}
});
// Shortcuts
$(window).keydown(function (ev) {
// Only ctrl prefixed (for now)
if (!ev.ctrlKey) return;
let handled = true;
switch (ev.key) {
// Ctrl+Z - Back
case "z":
button_actback.click();
break;
// Ctrl+Y - Forward
case "y":
button_actfwd.click();
break;
// Ctrl+E - Retry
case "e":
button_actretry.click();
break;
default:
handled = false;
}
if (handled) ev.preventDefault();
});
$("#anotetemplate").on("input", function() {
updateInputBudget(anote_input[0]);
})
registerTokenCounters();
updateInputBudget(input_text[0]);
});
var popup_deleteable = false;
var popup_editable = false;
var popup_renameable = false;
function load_popup(data) {
document.getElementById('spcontainer').classList.add('hidden');
document.getElementById('uscontainer').classList.add('hidden');
popup_deleteable = data.deleteable;
popup_editable = data.editable;
popup_renameable = data.renameable;
var popup = document.getElementById("popup");
var popup_title = document.getElementById("popup_title");
popup_title.textContent = data.popup_title;
var popup_list = document.getElementById("popup_list");
//first, let's clear out our existing data
while (popup_list.firstChild) {
popup_list.removeChild(popup_list.firstChild);
}
var breadcrumbs = document.getElementById('popup_breadcrumbs');
while (breadcrumbs.firstChild) {
breadcrumbs.removeChild(breadcrumbs.firstChild);
}
if (data.upload) {
const dropArea = document.getElementById('popup_list');
dropArea.addEventListener('dragover', (event) => {
event.stopPropagation();
event.preventDefault();
// Style the drag-and-drop as a "copy file" operation.
event.dataTransfer.dropEffect = 'copy';
});
dropArea.addEventListener('drop', (event) => {
event.stopPropagation();
event.preventDefault();
const fileList = event.dataTransfer.files;
for (file of fileList) {
reader = new FileReader();
reader.onload = function (event) {
socket.emit("upload_file", {'filename': file.name, "data": event.target.result});
};
reader.readAsArrayBuffer(file);
}
});
} else {
}
popup.classList.remove("hidden");
//adjust accept button
if (data.call_back == "") {
document.getElementById("popup_accept").classList.add("hidden");
} else {
document.getElementById("popup_accept").classList.remove("hidden");
var accept = document.getElementById("popup_accept");
accept.classList.add("disabled");
accept.setAttribute("emit", data.call_back);
accept.setAttribute("selected_value", "");
accept.onclick = function () {
socket.emit(this.emit, this.getAttribute("selected_value"));
document.getElementById("popup").classList.add("hidden");
};
}
}
function popup_items(data) {
var popup_list = document.getElementById('popup_list');
//first, let's clear out our existing data
while (popup_list.firstChild) {
popup_list.removeChild(popup_list.firstChild);
}
document.getElementById('popup_upload_input').value = "";
for (item of data) {
var list_item = document.createElement("span");
list_item.classList.add("item");
//create the folder icon
var folder_icon = document.createElement("span");
folder_icon.classList.add("folder_icon");
if (item[0]) {
folder_icon.classList.add("oi");
folder_icon.setAttribute('data-glyph', "folder");
}
list_item.append(folder_icon);
//create the edit icon
var edit_icon = document.createElement("span");
edit_icon.classList.add("edit_icon");
if ((popup_editable) && !(item[0])) {
edit_icon.classList.add("oi");
edit_icon.setAttribute('data-glyph', "spreadsheet");
edit_icon.title = "Edit"
edit_icon.id = item[1];
edit_icon.onclick = function () {
socket.emit("popup_edit", this.id);
};
}
list_item.append(edit_icon);
//create the rename icon
var rename_icon = document.createElement("span");
rename_icon.classList.add("rename_icon");
if ((popup_renameable) && !(item[0])) {
rename_icon.classList.add("oi");
rename_icon.setAttribute('data-glyph', "pencil");
rename_icon.title = "Rename"
rename_icon.id = item[1];
rename_icon.setAttribute("filename", item[2]);
rename_icon.onclick = function () {
var new_name = prompt("Please enter new filename for \n"+ this.getAttribute("filename"));
if (new_name != null) {
socket.emit("popup_rename", {"file": this.id, "new_name": new_name});
}
};
}
list_item.append(rename_icon);
//create the delete icon
var delete_icon = document.createElement("span");
delete_icon.classList.add("delete_icon");
if (popup_deleteable) {
delete_icon.classList.add("oi");
delete_icon.setAttribute('data-glyph', "x");
delete_icon.title = "Delete"
delete_icon.id = item[1];
delete_icon.setAttribute("folder", item[0]);
delete_icon.onclick = function () {
if (this.getAttribute("folder") == "true") {
if (window.confirm("Do you really want to delete this folder and ALL files under it?")) {
socket.emit("popup_delete", this.id);
}
} else {
if (window.confirm("Do you really want to delete this file?")) {
socket.emit("popup_delete", this.id);
}
}
};
}
list_item.append(delete_icon);
//create the actual item
var popup_item = document.createElement("span");
popup_item.classList.add("file");
popup_item.id = item[1];
popup_item.setAttribute("folder", item[0]);
popup_item.setAttribute("valid", item[3]);
popup_item.textContent = item[2];
popup_item.onclick = function () {
var accept = document.getElementById("popup_accept");
if (this.getAttribute("valid") == "true") {
accept.classList.remove("disabled");
accept.setAttribute("selected_value", this.id);
} else {
console.log("not valid");
accept.setAttribute("selected_value", "");
accept.classList.add("disabled");
if (this.getAttribute("folder") == "true") {
console.log("folder");
socket.emit("popup_change_folder", this.id);
}
}
};
list_item.append(popup_item);
popup_list.append(list_item);
}
}
function popup_breadcrumbs(data) {
var breadcrumbs = document.getElementById('popup_breadcrumbs')
while (breadcrumbs.firstChild) {
breadcrumbs.removeChild(breadcrumbs.firstChild);
}
for (item of data) {
var button = document.createElement("button");
button.id = item[0];
button.textContent = item[1];
button.classList.add("breadcrumbitem");
button.onclick = function () {
socket.emit("popup_change_folder", this.id);
};
breadcrumbs.append(button);
var span = document.createElement("span");
span.textContent = "\\";
breadcrumbs.append(span);
}
}
function popup_edit_file(data) {
var popup_list = document.getElementById('popup_list');
var accept = document.getElementById("popup_accept");
accept.classList.add("btn-secondary");
accept.classList.remove("btn-primary");
accept.textContent = "Save";
//first, let's clear out our existing data
while (popup_list.firstChild) {
popup_list.removeChild(popup_list.firstChild);
}
var accept = document.getElementById("popup_accept");
accept.setAttribute("selected_value", "");
accept.onclick = function () {
var textarea = document.getElementById("filecontents");
socket.emit("popup_change_file", {"file": textarea.getAttribute("filename"), "data": textarea.value});
document.getElementById("popup").classList.add("hidden");
this.classList.add("hidden");
};
var textarea = document.createElement("textarea");
textarea.classList.add("fullwidth");
textarea.rows = 25;
textarea.id = "filecontents"
textarea.setAttribute("filename", data.file);
textarea.value = data.text;
textarea.onblur = function () {
var accept = document.getElementById("popup_accept");
accept.classList.remove("hidden");
accept.classList.remove("btn-secondary");
accept.classList.add("btn-primary");
};
popup_list.append(textarea);
}
function error_popup(data) {
alert(data);
}
function upload_file(file_box) {
var fileList = file_box.files;
for (file of fileList) {
reader = new FileReader();
reader.onload = function (event) {
socket.emit("upload_file", {'filename': file.name, "data": event.target.result});
};
reader.readAsArrayBuffer(file);
}
}

178
static/custom.css
View File

@@ -4,6 +4,7 @@ body {
chunk {
color: #ffffff;
white-space: pre-wrap;
}
#gametext.adventure action {
@@ -290,7 +291,7 @@ body.connected #formatmenu, #formatmenu.always-available {
align-items: center;
}
#popup {
#popup_old {
width: 75%;
min-width: 500px;
max-width: 1000px;
@@ -1545,4 +1546,179 @@ body.connected .popupfooter, .popupfooter.always-available {
.change .menubar3 {
transform: translate(0px, -6px) rotate(45deg);
}
/*---------------------------------- Popup -------------------------------------------------*/
.new_popup {
position: absolute;
top: 10vh;
left: 10%;
z-index: 999;
width: 80%;
height: 80vh;
background-color: black;
display: flex;
flex-direction: column;
background-color: #474B4F;
color: white;
}
.new_popup .title {
width: 100%;
background-color: #337AB7;
text-align: center;
font-size: 1.3em;
}
.new_popup .popup_list_area {
height: 70vh;
overflow-x: hidden;
}
.new_popup .item {
width: 100%;
background-color: #262626;
padding: 2px;
display: grid;
grid-template-areas: "folder_icon delete_icon edit_icon rename_icon file";
grid-template-columns: 20px 20px 20px 20px auto;
}
.new_popup .item .folder_icon {
grid-area: folder_icon;
}
.new_popup .item .edit_icon {
grid-area: edit_icon;
}
.new_popup .item .rename_icon {
grid-area: rename_icon;
}
.new_popup .item .delete_icon {
grid-area: delete_icon;
}
.new_popup .item .file {
grid-area: file;
}
.new_popup .item .file:hover {
background-color: #688f1f;
}
.new_popup textarea {
grid-area: textarea;
background-color: #404040;
color: white;
resize: none;
width: 100%;
}
.new_popup .popup_load_cancel {
text-align: center;
background-color: #285070;
}
.popup_load_cancel_button {
vertical-align: bottom;
display: inline;
}
.popup_load_cancel_button.btn-secondary {
color: rgb(51, 51, 51);
background-color: #686c68;
}
.breadcrumbitem {
padding: 5px 10px 5px 10px;
color: #ffffff;
background-color: transparent;
border: none;
-moz-transition: background-color 0.25s ease-in;
-o-transition: background-color 0.25s ease-in;
-webkit-transition: background-color 0.25s ease-in;
transition: background-color 0.25s ease-in;
}
.breadcrumbitem:hover {
cursor: pointer;
background-color: #688f1f;
}
#token_prob_menu {
color: white;
background-color: #262626;
}
.token-probs {
display: inline-block;
text-align: center;
margin-right: 5px;
}
.token-probs > table {
width: 100%;
}
.token-probs > table > tbody > tr > td {
border: 1px solid #262626;
border-collapse: collapse;
padding: 2px 15px;
}
.token-probs > table > tbody > tr {
background-color: #3e3e3e;
}
.token-probs > table > tbody > tr:nth-child(2n) {
background-color: #575757;
}
.token-probs-final-token {
font-weight: bold;
text-decoration: underline;
}
.token-probs-final-token > td {
background: #5c8a5a;
}
.token-probs-header {
display: block;
}
#token_prob_container {
overflow-x: auto;
white-space: nowrap;
}
.tokens-counted {
position: relative;
}
.input-token-usage {
color: white;
position: absolute;
font-size: 10px;
bottom: 2px;
right: 5px;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
/* Override needed here due to the 10px right padding on inputrowleft; add 10 px. */
#inputrowleft > .input-token-usage {
right: 15px;
bottom: 1px;
}
.wientry > .input-token-usage {
bottom: 8px;
}

202
static/swagger-ui/LICENSE vendored Normal file
View File

@@ -0,0 +1,202 @@
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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853
static/swagger-ui/SwaggerDark.css vendored Normal file
View File

@@ -0,0 +1,853 @@
/*!
* MIT License
*
* Copyright (c) 2020 Romans Pokrovskis
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
a { color: #8c8cfa; }
::-webkit-scrollbar-track-piece { background-color: rgba(255, 255, 255, .2) !important; }
::-webkit-scrollbar-track { background-color: rgba(255, 255, 255, .3) !important; }
::-webkit-scrollbar-thumb { background-color: rgba(255, 255, 255, .5) !important; }
embed[type="application/pdf"] { filter: invert(90%); }
html {
background: #1f1f1f !important;
box-sizing: border-box;
filter: contrast(100%) brightness(100%) saturate(100%);
overflow-y: scroll;
}
body {
background: #1f1f1f;
background-color: #1f1f1f;
background-image: none !important;
}
button, input, select, textarea {
background-color: #1f1f1f;
color: #bfbfbf;
}
font, html { color: #bfbfbf; }
.swagger-ui, .swagger-ui section h3 { color: #b5bac9; }
.swagger-ui a { background-color: transparent; }
.swagger-ui mark {
background-color: #664b00;
color: #bfbfbf;
}
.swagger-ui legend { color: inherit; }
.swagger-ui .debug * { outline: #e6da99 solid 1px; }
.swagger-ui .debug-white * { outline: #fff solid 1px; }
.swagger-ui .debug-black * { outline: #bfbfbf solid 1px; }
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.swagger-ui .b--black { border-color: #000; }
.swagger-ui .b--near-black { border-color: #121212; }
.swagger-ui .b--dark-gray { border-color: #333; }
.swagger-ui .b--mid-gray { border-color: #545454; }
.swagger-ui .b--gray { border-color: #787878; }
.swagger-ui .b--silver { border-color: #999; }
.swagger-ui .b--light-silver { border-color: #6e6e6e; }
.swagger-ui .b--moon-gray { border-color: #4d4d4d; }
.swagger-ui .b--light-gray { border-color: #2b2b2b; }
.swagger-ui .b--near-white { border-color: #242424; }
.swagger-ui .b--white { border-color: #1c1c21; }
.swagger-ui .b--white-90 { border-color: rgba(28, 28, 33, .9); }
.swagger-ui .b--white-80 { border-color: rgba(28, 28, 33, .8); }
.swagger-ui .b--white-70 { border-color: rgba(28, 28, 33, .7); }
.swagger-ui .b--white-60 { border-color: rgba(28, 28, 33, .6); }
.swagger-ui .b--white-50 { border-color: rgba(28, 28, 33, .5); }
.swagger-ui .b--white-40 { border-color: rgba(28, 28, 33, .4); }
.swagger-ui .b--white-30 { border-color: rgba(28, 28, 33, .3); }
.swagger-ui .b--white-20 { border-color: rgba(28, 28, 33, .2); }
.swagger-ui .b--white-10 { border-color: rgba(28, 28, 33, .1); }
.swagger-ui .b--white-05 { border-color: rgba(28, 28, 33, .05); }
.swagger-ui .b--white-025 { border-color: rgba(28, 28, 33, .024); }
.swagger-ui .b--white-0125 { border-color: rgba(28, 28, 33, .01); }
.swagger-ui .b--black-90 { border-color: rgba(0, 0, 0, .9); }
.swagger-ui .b--black-80 { border-color: rgba(0, 0, 0, .8); }
.swagger-ui .b--black-70 { border-color: rgba(0, 0, 0, .7); }
.swagger-ui .b--black-60 { border-color: rgba(0, 0, 0, .6); }
.swagger-ui .b--black-50 { border-color: rgba(0, 0, 0, .5); }
.swagger-ui .b--black-40 { border-color: rgba(0, 0, 0, .4); }
.swagger-ui .b--black-30 { border-color: rgba(0, 0, 0, .3); }
.swagger-ui .b--black-20 { border-color: rgba(0, 0, 0, .2); }
.swagger-ui .b--black-10 { border-color: rgba(0, 0, 0, .1); }
.swagger-ui .b--black-05 { border-color: rgba(0, 0, 0, .05); }
.swagger-ui .b--black-025 { border-color: rgba(0, 0, 0, .024); }
.swagger-ui .b--black-0125 { border-color: rgba(0, 0, 0, .01); }
.swagger-ui .b--dark-red { border-color: #bc2f36; }
.swagger-ui .b--red { border-color: #c83932; }
.swagger-ui .b--light-red { border-color: #ab3c2b; }
.swagger-ui .b--orange { border-color: #cc6e33; }
.swagger-ui .b--purple { border-color: #5e2ca5; }
.swagger-ui .b--light-purple { border-color: #672caf; }
.swagger-ui .b--dark-pink { border-color: #ab2b81; }
.swagger-ui .b--hot-pink { border-color: #c03086; }
.swagger-ui .b--pink { border-color: #8f2464; }
.swagger-ui .b--light-pink { border-color: #721d4d; }
.swagger-ui .b--dark-green { border-color: #1c6e50; }
.swagger-ui .b--green { border-color: #279b70; }
.swagger-ui .b--light-green { border-color: #228762; }
.swagger-ui .b--navy { border-color: #0d1d35; }
.swagger-ui .b--dark-blue { border-color: #20497e; }
.swagger-ui .b--blue { border-color: #4380d0; }
.swagger-ui .b--light-blue { border-color: #20517e; }
.swagger-ui .b--lightest-blue { border-color: #143a52; }
.swagger-ui .b--washed-blue { border-color: #0c312d; }
.swagger-ui .b--washed-green { border-color: #0f3d2c; }
.swagger-ui .b--washed-red { border-color: #411010; }
.swagger-ui .b--transparent { border-color: transparent; }
.swagger-ui .b--gold, .swagger-ui .b--light-yellow, .swagger-ui .b--washed-yellow, .swagger-ui .b--yellow { border-color: #664b00; }
.swagger-ui .shadow-1 { box-shadow: rgba(0, 0, 0, .2) 0 0 4px 2px; }
.swagger-ui .shadow-2 { box-shadow: rgba(0, 0, 0, .2) 0 0 8px 2px; }
.swagger-ui .shadow-3 { box-shadow: rgba(0, 0, 0, .2) 2px 2px 4px 2px; }
.swagger-ui .shadow-4 { box-shadow: rgba(0, 0, 0, .2) 2px 2px 8px 0; }
.swagger-ui .shadow-5 { box-shadow: rgba(0, 0, 0, .2) 4px 4px 8px 0; }
@media screen and (min-width: 30em) {
.swagger-ui .shadow-1-ns { box-shadow: rgba(0, 0, 0, .2) 0 0 4px 2px; }
.swagger-ui .shadow-2-ns { box-shadow: rgba(0, 0, 0, .2) 0 0 8px 2px; }
.swagger-ui .shadow-3-ns { box-shadow: rgba(0, 0, 0, .2) 2px 2px 4px 2px; }
.swagger-ui .shadow-4-ns { box-shadow: rgba(0, 0, 0, .2) 2px 2px 8px 0; }
.swagger-ui .shadow-5-ns { box-shadow: rgba(0, 0, 0, .2) 4px 4px 8px 0; }
}
@media screen and (max-width: 60em) and (min-width: 30em) {
.swagger-ui .shadow-1-m { box-shadow: rgba(0, 0, 0, .2) 0 0 4px 2px; }
.swagger-ui .shadow-2-m { box-shadow: rgba(0, 0, 0, .2) 0 0 8px 2px; }
.swagger-ui .shadow-3-m { box-shadow: rgba(0, 0, 0, .2) 2px 2px 4px 2px; }
.swagger-ui .shadow-4-m { box-shadow: rgba(0, 0, 0, .2) 2px 2px 8px 0; }
.swagger-ui .shadow-5-m { box-shadow: rgba(0, 0, 0, .2) 4px 4px 8px 0; }
}
@media screen and (min-width: 60em) {
.swagger-ui .shadow-1-l { box-shadow: rgba(0, 0, 0, .2) 0 0 4px 2px; }
.swagger-ui .shadow-2-l { box-shadow: rgba(0, 0, 0, .2) 0 0 8px 2px; }
.swagger-ui .shadow-3-l { box-shadow: rgba(0, 0, 0, .2) 2px 2px 4px 2px; }
.swagger-ui .shadow-4-l { box-shadow: rgba(0, 0, 0, .2) 2px 2px 8px 0; }
.swagger-ui .shadow-5-l { box-shadow: rgba(0, 0, 0, .2) 4px 4px 8px 0; }
}
.swagger-ui .black-05 { color: rgba(191, 191, 191, .05); }
.swagger-ui .bg-black-05 { background-color: rgba(0, 0, 0, .05); }
.swagger-ui .black-90, .swagger-ui .hover-black-90:focus, .swagger-ui .hover-black-90:hover { color: rgba(191, 191, 191, .9); }
.swagger-ui .black-80, .swagger-ui .hover-black-80:focus, .swagger-ui .hover-black-80:hover { color: rgba(191, 191, 191, .8); }
.swagger-ui .black-70, .swagger-ui .hover-black-70:focus, .swagger-ui .hover-black-70:hover { color: rgba(191, 191, 191, .7); }
.swagger-ui .black-60, .swagger-ui .hover-black-60:focus, .swagger-ui .hover-black-60:hover { color: rgba(191, 191, 191, .6); }
.swagger-ui .black-50, .swagger-ui .hover-black-50:focus, .swagger-ui .hover-black-50:hover { color: rgba(191, 191, 191, .5); }
.swagger-ui .black-40, .swagger-ui .hover-black-40:focus, .swagger-ui .hover-black-40:hover { color: rgba(191, 191, 191, .4); }
.swagger-ui .black-30, .swagger-ui .hover-black-30:focus, .swagger-ui .hover-black-30:hover { color: rgba(191, 191, 191, .3); }
.swagger-ui .black-20, .swagger-ui .hover-black-20:focus, .swagger-ui .hover-black-20:hover { color: rgba(191, 191, 191, .2); }
.swagger-ui .black-10, .swagger-ui .hover-black-10:focus, .swagger-ui .hover-black-10:hover { color: rgba(191, 191, 191, .1); }
.swagger-ui .hover-white-90:focus, .swagger-ui .hover-white-90:hover, .swagger-ui .white-90 { color: rgba(255, 255, 255, .9); }
.swagger-ui .hover-white-80:focus, .swagger-ui .hover-white-80:hover, .swagger-ui .white-80 { color: rgba(255, 255, 255, .8); }
.swagger-ui .hover-white-70:focus, .swagger-ui .hover-white-70:hover, .swagger-ui .white-70 { color: rgba(255, 255, 255, .7); }
.swagger-ui .hover-white-60:focus, .swagger-ui .hover-white-60:hover, .swagger-ui .white-60 { color: rgba(255, 255, 255, .6); }
.swagger-ui .hover-white-50:focus, .swagger-ui .hover-white-50:hover, .swagger-ui .white-50 { color: rgba(255, 255, 255, .5); }
.swagger-ui .hover-white-40:focus, .swagger-ui .hover-white-40:hover, .swagger-ui .white-40 { color: rgba(255, 255, 255, .4); }
.swagger-ui .hover-white-30:focus, .swagger-ui .hover-white-30:hover, .swagger-ui .white-30 { color: rgba(255, 255, 255, .3); }
.swagger-ui .hover-white-20:focus, .swagger-ui .hover-white-20:hover, .swagger-ui .white-20 { color: rgba(255, 255, 255, .2); }
.swagger-ui .hover-white-10:focus, .swagger-ui .hover-white-10:hover, .swagger-ui .white-10 { color: rgba(255, 255, 255, .1); }
.swagger-ui .hover-moon-gray:focus, .swagger-ui .hover-moon-gray:hover, .swagger-ui .moon-gray { color: #ccc; }
.swagger-ui .hover-light-gray:focus, .swagger-ui .hover-light-gray:hover, .swagger-ui .light-gray { color: #ededed; }
.swagger-ui .hover-near-white:focus, .swagger-ui .hover-near-white:hover, .swagger-ui .near-white { color: #f5f5f5; }
.swagger-ui .dark-red, .swagger-ui .hover-dark-red:focus, .swagger-ui .hover-dark-red:hover { color: #e6999d; }
.swagger-ui .hover-red:focus, .swagger-ui .hover-red:hover, .swagger-ui .red { color: #e69d99; }
.swagger-ui .hover-light-red:focus, .swagger-ui .hover-light-red:hover, .swagger-ui .light-red { color: #e6a399; }
.swagger-ui .hover-orange:focus, .swagger-ui .hover-orange:hover, .swagger-ui .orange { color: #e6b699; }
.swagger-ui .gold, .swagger-ui .hover-gold:focus, .swagger-ui .hover-gold:hover { color: #e6d099; }
.swagger-ui .hover-yellow:focus, .swagger-ui .hover-yellow:hover, .swagger-ui .yellow { color: #e6da99; }
.swagger-ui .hover-light-yellow:focus, .swagger-ui .hover-light-yellow:hover, .swagger-ui .light-yellow { color: #ede6b6; }
.swagger-ui .hover-purple:focus, .swagger-ui .hover-purple:hover, .swagger-ui .purple { color: #b99ae4; }
.swagger-ui .hover-light-purple:focus, .swagger-ui .hover-light-purple:hover, .swagger-ui .light-purple { color: #bb99e6; }
.swagger-ui .dark-pink, .swagger-ui .hover-dark-pink:focus, .swagger-ui .hover-dark-pink:hover { color: #e699cc; }
.swagger-ui .hot-pink, .swagger-ui .hover-hot-pink:focus, .swagger-ui .hover-hot-pink:hover, .swagger-ui .hover-pink:focus, .swagger-ui .hover-pink:hover, .swagger-ui .pink { color: #e699c7; }
.swagger-ui .hover-light-pink:focus, .swagger-ui .hover-light-pink:hover, .swagger-ui .light-pink { color: #edb6d5; }
.swagger-ui .dark-green, .swagger-ui .green, .swagger-ui .hover-dark-green:focus, .swagger-ui .hover-dark-green:hover, .swagger-ui .hover-green:focus, .swagger-ui .hover-green:hover { color: #99e6c9; }
.swagger-ui .hover-light-green:focus, .swagger-ui .hover-light-green:hover, .swagger-ui .light-green { color: #a1e8ce; }
.swagger-ui .hover-navy:focus, .swagger-ui .hover-navy:hover, .swagger-ui .navy { color: #99b8e6; }
.swagger-ui .blue, .swagger-ui .dark-blue, .swagger-ui .hover-blue:focus, .swagger-ui .hover-blue:hover, .swagger-ui .hover-dark-blue:focus, .swagger-ui .hover-dark-blue:hover { color: #99bae6; }
.swagger-ui .hover-light-blue:focus, .swagger-ui .hover-light-blue:hover, .swagger-ui .light-blue { color: #a9cbea; }
.swagger-ui .hover-lightest-blue:focus, .swagger-ui .hover-lightest-blue:hover, .swagger-ui .lightest-blue { color: #d6e9f5; }
.swagger-ui .hover-washed-blue:focus, .swagger-ui .hover-washed-blue:hover, .swagger-ui .washed-blue { color: #f7fdfc; }
.swagger-ui .hover-washed-green:focus, .swagger-ui .hover-washed-green:hover, .swagger-ui .washed-green { color: #ebfaf4; }
.swagger-ui .hover-washed-yellow:focus, .swagger-ui .hover-washed-yellow:hover, .swagger-ui .washed-yellow { color: #fbf9ef; }
.swagger-ui .hover-washed-red:focus, .swagger-ui .hover-washed-red:hover, .swagger-ui .washed-red { color: #f9e7e7; }
.swagger-ui .color-inherit, .swagger-ui .hover-inherit:focus, .swagger-ui .hover-inherit:hover { color: inherit; }
.swagger-ui .bg-black-90, .swagger-ui .hover-bg-black-90:focus, .swagger-ui .hover-bg-black-90:hover { background-color: rgba(0, 0, 0, .9); }
.swagger-ui .bg-black-80, .swagger-ui .hover-bg-black-80:focus, .swagger-ui .hover-bg-black-80:hover { background-color: rgba(0, 0, 0, .8); }
.swagger-ui .bg-black-70, .swagger-ui .hover-bg-black-70:focus, .swagger-ui .hover-bg-black-70:hover { background-color: rgba(0, 0, 0, .7); }
.swagger-ui .bg-black-60, .swagger-ui .hover-bg-black-60:focus, .swagger-ui .hover-bg-black-60:hover { background-color: rgba(0, 0, 0, .6); }
.swagger-ui .bg-black-50, .swagger-ui .hover-bg-black-50:focus, .swagger-ui .hover-bg-black-50:hover { background-color: rgba(0, 0, 0, .5); }
.swagger-ui .bg-black-40, .swagger-ui .hover-bg-black-40:focus, .swagger-ui .hover-bg-black-40:hover { background-color: rgba(0, 0, 0, .4); }
.swagger-ui .bg-black-30, .swagger-ui .hover-bg-black-30:focus, .swagger-ui .hover-bg-black-30:hover { background-color: rgba(0, 0, 0, .3); }
.swagger-ui .bg-black-20, .swagger-ui .hover-bg-black-20:focus, .swagger-ui .hover-bg-black-20:hover { background-color: rgba(0, 0, 0, .2); }
.swagger-ui .bg-white-90, .swagger-ui .hover-bg-white-90:focus, .swagger-ui .hover-bg-white-90:hover { background-color: rgba(28, 28, 33, .9); }
.swagger-ui .bg-white-80, .swagger-ui .hover-bg-white-80:focus, .swagger-ui .hover-bg-white-80:hover { background-color: rgba(28, 28, 33, .8); }
.swagger-ui .bg-white-70, .swagger-ui .hover-bg-white-70:focus, .swagger-ui .hover-bg-white-70:hover { background-color: rgba(28, 28, 33, .7); }
.swagger-ui .bg-white-60, .swagger-ui .hover-bg-white-60:focus, .swagger-ui .hover-bg-white-60:hover { background-color: rgba(28, 28, 33, .6); }
.swagger-ui .bg-white-50, .swagger-ui .hover-bg-white-50:focus, .swagger-ui .hover-bg-white-50:hover { background-color: rgba(28, 28, 33, .5); }
.swagger-ui .bg-white-40, .swagger-ui .hover-bg-white-40:focus, .swagger-ui .hover-bg-white-40:hover { background-color: rgba(28, 28, 33, .4); }
.swagger-ui .bg-white-30, .swagger-ui .hover-bg-white-30:focus, .swagger-ui .hover-bg-white-30:hover { background-color: rgba(28, 28, 33, .3); }
.swagger-ui .bg-white-20, .swagger-ui .hover-bg-white-20:focus, .swagger-ui .hover-bg-white-20:hover { background-color: rgba(28, 28, 33, .2); }
.swagger-ui .bg-black, .swagger-ui .hover-bg-black:focus, .swagger-ui .hover-bg-black:hover { background-color: #000; }
.swagger-ui .bg-near-black, .swagger-ui .hover-bg-near-black:focus, .swagger-ui .hover-bg-near-black:hover { background-color: #121212; }
.swagger-ui .bg-dark-gray, .swagger-ui .hover-bg-dark-gray:focus, .swagger-ui .hover-bg-dark-gray:hover { background-color: #333; }
.swagger-ui .bg-mid-gray, .swagger-ui .hover-bg-mid-gray:focus, .swagger-ui .hover-bg-mid-gray:hover { background-color: #545454; }
.swagger-ui .bg-gray, .swagger-ui .hover-bg-gray:focus, .swagger-ui .hover-bg-gray:hover { background-color: #787878; }
.swagger-ui .bg-silver, .swagger-ui .hover-bg-silver:focus, .swagger-ui .hover-bg-silver:hover { background-color: #999; }
.swagger-ui .bg-white, .swagger-ui .hover-bg-white:focus, .swagger-ui .hover-bg-white:hover { background-color: #1c1c21; }
.swagger-ui .bg-transparent, .swagger-ui .hover-bg-transparent:focus, .swagger-ui .hover-bg-transparent:hover { background-color: transparent; }
.swagger-ui .bg-dark-red, .swagger-ui .hover-bg-dark-red:focus, .swagger-ui .hover-bg-dark-red:hover { background-color: #bc2f36; }
.swagger-ui .bg-red, .swagger-ui .hover-bg-red:focus, .swagger-ui .hover-bg-red:hover { background-color: #c83932; }
.swagger-ui .bg-light-red, .swagger-ui .hover-bg-light-red:focus, .swagger-ui .hover-bg-light-red:hover { background-color: #ab3c2b; }
.swagger-ui .bg-orange, .swagger-ui .hover-bg-orange:focus, .swagger-ui .hover-bg-orange:hover { background-color: #cc6e33; }
.swagger-ui .bg-gold, .swagger-ui .bg-light-yellow, .swagger-ui .bg-washed-yellow, .swagger-ui .bg-yellow, .swagger-ui .hover-bg-gold:focus, .swagger-ui .hover-bg-gold:hover, .swagger-ui .hover-bg-light-yellow:focus, .swagger-ui .hover-bg-light-yellow:hover, .swagger-ui .hover-bg-washed-yellow:focus, .swagger-ui .hover-bg-washed-yellow:hover, .swagger-ui .hover-bg-yellow:focus, .swagger-ui .hover-bg-yellow:hover { background-color: #664b00; }
.swagger-ui .bg-purple, .swagger-ui .hover-bg-purple:focus, .swagger-ui .hover-bg-purple:hover { background-color: #5e2ca5; }
.swagger-ui .bg-light-purple, .swagger-ui .hover-bg-light-purple:focus, .swagger-ui .hover-bg-light-purple:hover { background-color: #672caf; }
.swagger-ui .bg-dark-pink, .swagger-ui .hover-bg-dark-pink:focus, .swagger-ui .hover-bg-dark-pink:hover { background-color: #ab2b81; }
.swagger-ui .bg-hot-pink, .swagger-ui .hover-bg-hot-pink:focus, .swagger-ui .hover-bg-hot-pink:hover { background-color: #c03086; }
.swagger-ui .bg-pink, .swagger-ui .hover-bg-pink:focus, .swagger-ui .hover-bg-pink:hover { background-color: #8f2464; }
.swagger-ui .bg-light-pink, .swagger-ui .hover-bg-light-pink:focus, .swagger-ui .hover-bg-light-pink:hover { background-color: #721d4d; }
.swagger-ui .bg-dark-green, .swagger-ui .hover-bg-dark-green:focus, .swagger-ui .hover-bg-dark-green:hover { background-color: #1c6e50; }
.swagger-ui .bg-green, .swagger-ui .hover-bg-green:focus, .swagger-ui .hover-bg-green:hover { background-color: #279b70; }
.swagger-ui .bg-light-green, .swagger-ui .hover-bg-light-green:focus, .swagger-ui .hover-bg-light-green:hover { background-color: #228762; }
.swagger-ui .bg-navy, .swagger-ui .hover-bg-navy:focus, .swagger-ui .hover-bg-navy:hover { background-color: #0d1d35; }
.swagger-ui .bg-dark-blue, .swagger-ui .hover-bg-dark-blue:focus, .swagger-ui .hover-bg-dark-blue:hover { background-color: #20497e; }
.swagger-ui .bg-blue, .swagger-ui .hover-bg-blue:focus, .swagger-ui .hover-bg-blue:hover { background-color: #4380d0; }
.swagger-ui .bg-light-blue, .swagger-ui .hover-bg-light-blue:focus, .swagger-ui .hover-bg-light-blue:hover { background-color: #20517e; }
.swagger-ui .bg-lightest-blue, .swagger-ui .hover-bg-lightest-blue:focus, .swagger-ui .hover-bg-lightest-blue:hover { background-color: #143a52; }
.swagger-ui .bg-washed-blue, .swagger-ui .hover-bg-washed-blue:focus, .swagger-ui .hover-bg-washed-blue:hover { background-color: #0c312d; }
.swagger-ui .bg-washed-green, .swagger-ui .hover-bg-washed-green:focus, .swagger-ui .hover-bg-washed-green:hover { background-color: #0f3d2c; }
.swagger-ui .bg-washed-red, .swagger-ui .hover-bg-washed-red:focus, .swagger-ui .hover-bg-washed-red:hover { background-color: #411010; }
.swagger-ui .bg-inherit, .swagger-ui .hover-bg-inherit:focus, .swagger-ui .hover-bg-inherit:hover { background-color: inherit; }
.swagger-ui .shadow-hover { transition: all .5s cubic-bezier(.165, .84, .44, 1) 0s; }
.swagger-ui .shadow-hover::after {
border-radius: inherit;
box-shadow: rgba(0, 0, 0, .2) 0 0 16px 2px;
content: "";
height: 100%;
left: 0;
opacity: 0;
position: absolute;
top: 0;
transition: opacity .5s cubic-bezier(.165, .84, .44, 1) 0s;
width: 100%;
z-index: -1;
}
.swagger-ui .bg-animate, .swagger-ui .bg-animate:focus, .swagger-ui .bg-animate:hover { transition: background-color .15s ease-in-out 0s; }
.swagger-ui .nested-links a {
color: #99bae6;
transition: color .15s ease-in 0s;
}
.swagger-ui .nested-links a:focus, .swagger-ui .nested-links a:hover {
color: #a9cbea;
transition: color .15s ease-in 0s;
}
.swagger-ui .opblock-tag {
border-bottom: 1px solid rgba(58, 64, 80, .3);
color: #b5bac9;
transition: all .2s ease 0s;
}
.swagger-ui .opblock-tag svg, .swagger-ui section.models h4 svg { transition: all .4s ease 0s; }
.swagger-ui .opblock {
border: 1px solid #000;
border-radius: 4px;
box-shadow: rgba(0, 0, 0, .19) 0 0 3px;
margin: 0 0 15px;
}
.swagger-ui .opblock .tab-header .tab-item.active h4 span::after { background: gray; }
.swagger-ui .opblock.is-open .opblock-summary { border-bottom: 1px solid #000; }
.swagger-ui .opblock .opblock-section-header {
background: rgba(28, 28, 33, .8);
box-shadow: rgba(0, 0, 0, .1) 0 1px 2px;
}
.swagger-ui .opblock .opblock-section-header > label > span { padding: 0 10px 0 0; }
.swagger-ui .opblock .opblock-summary-method {
background: #000;
color: #fff;
text-shadow: rgba(0, 0, 0, .1) 0 1px 0;
}
.swagger-ui .opblock.opblock-post {
background: rgba(72, 203, 144, .1);
border-color: #48cb90;
}
.swagger-ui .opblock.opblock-post .opblock-summary-method, .swagger-ui .opblock.opblock-post .tab-header .tab-item.active h4 span::after { background: #48cb90; }
.swagger-ui .opblock.opblock-post .opblock-summary { border-color: #48cb90; }
.swagger-ui .opblock.opblock-put {
background: rgba(213, 157, 88, .1);
border-color: #d59d58;
}
.swagger-ui .opblock.opblock-put .opblock-summary-method, .swagger-ui .opblock.opblock-put .tab-header .tab-item.active h4 span::after { background: #d59d58; }
.swagger-ui .opblock.opblock-put .opblock-summary { border-color: #d59d58; }
.swagger-ui .opblock.opblock-delete {
background: rgba(200, 50, 50, .1);
border-color: #c83232;
}
.swagger-ui .opblock.opblock-delete .opblock-summary-method, .swagger-ui .opblock.opblock-delete .tab-header .tab-item.active h4 span::after { background: #c83232; }
.swagger-ui .opblock.opblock-delete .opblock-summary { border-color: #c83232; }
.swagger-ui .opblock.opblock-get {
background: rgba(42, 105, 167, .1);
border-color: #2a69a7;
}
.swagger-ui .opblock.opblock-get .opblock-summary-method, .swagger-ui .opblock.opblock-get .tab-header .tab-item.active h4 span::after { background: #2a69a7; }
.swagger-ui .opblock.opblock-get .opblock-summary { border-color: #2a69a7; }
.swagger-ui .opblock.opblock-patch {
background: rgba(92, 214, 188, .1);
border-color: #5cd6bc;
}
.swagger-ui .opblock.opblock-patch .opblock-summary-method, .swagger-ui .opblock.opblock-patch .tab-header .tab-item.active h4 span::after { background: #5cd6bc; }
.swagger-ui .opblock.opblock-patch .opblock-summary { border-color: #5cd6bc; }
.swagger-ui .opblock.opblock-head {
background: rgba(140, 63, 207, .1);
border-color: #8c3fcf;
}
.swagger-ui .opblock.opblock-head .opblock-summary-method, .swagger-ui .opblock.opblock-head .tab-header .tab-item.active h4 span::after { background: #8c3fcf; }
.swagger-ui .opblock.opblock-head .opblock-summary { border-color: #8c3fcf; }
.swagger-ui .opblock.opblock-options {
background: rgba(36, 89, 143, .1);
border-color: #24598f;
}
.swagger-ui .opblock.opblock-options .opblock-summary-method, .swagger-ui .opblock.opblock-options .tab-header .tab-item.active h4 span::after { background: #24598f; }
.swagger-ui .opblock.opblock-options .opblock-summary { border-color: #24598f; }
.swagger-ui .opblock.opblock-deprecated {
background: rgba(46, 46, 46, .1);
border-color: #2e2e2e;
opacity: .6;
}
.swagger-ui .opblock.opblock-deprecated .opblock-summary-method, .swagger-ui .opblock.opblock-deprecated .tab-header .tab-item.active h4 span::after { background: #2e2e2e; }
.swagger-ui .opblock.opblock-deprecated .opblock-summary { border-color: #2e2e2e; }
.swagger-ui .filter .operation-filter-input { border: 2px solid #2b3446; }
.swagger-ui .tab li:first-of-type::after { background: rgba(0, 0, 0, .2); }
.swagger-ui .download-contents {
background: #7c8192;
color: #fff;
}
.swagger-ui .scheme-container {
background: #1c1c21;
box-shadow: rgba(0, 0, 0, .15) 0 1px 2px 0;
}
.swagger-ui .loading-container .loading::before {
animation: 1s linear 0s infinite normal none running rotation, .5s ease 0s 1 normal none running opacity;
border-color: rgba(0, 0, 0, .6) rgba(84, 84, 84, .1) rgba(84, 84, 84, .1);
}
.swagger-ui .response-control-media-type--accept-controller select { border-color: #196619; }
.swagger-ui .response-control-media-type__accept-message { color: #99e699; }
.swagger-ui .version-pragma__message code { background-color: #3b3b3b; }
.swagger-ui .btn {
background: 0 0;
border: 2px solid gray;
box-shadow: rgba(0, 0, 0, .1) 0 1px 2px;
color: #b5bac9;
}
.swagger-ui .btn:hover { box-shadow: rgba(0, 0, 0, .3) 0 0 5px; }
.swagger-ui .btn.authorize, .swagger-ui .btn.cancel {
background-color: transparent;
border-color: #a72a2a;
color: #e69999;
}
.swagger-ui .btn.authorize {
border-color: #48cb90;
color: #9ce3c3;
}
.swagger-ui .btn.authorize svg { fill: #9ce3c3; }
.swagger-ui .btn.execute {
background-color: #5892d5;
border-color: #5892d5;
color: #fff;
}
.swagger-ui .copy-to-clipboard { background: #7c8192; }
.swagger-ui .copy-to-clipboard button { background: url("data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"16\" height=\"16\" aria-hidden=\"true\"><path fill=\"%23fff\" fill-rule=\"evenodd\" d=\"M2 13h4v1H2v-1zm5-6H2v1h5V7zm2 3V8l-3 3 3 3v-2h5v-2H9zM4.5 9H2v1h2.5V9zM2 12h2.5v-1H2v1zm9 1h1v2c-.02.28-.11.52-.3.7-.19.18-.42.28-.7.3H1c-.55 0-1-.45-1-1V4c0-.55.45-1 1-1h3c0-1.11.89-2 2-2 1.11 0 2 .89 2 2h3c.55 0 1 .45 1 1v5h-1V6H1v9h10v-2zM2 5h8c0-.55-.45-1-1-1H8c-.55 0-1-.45-1-1s-.45-1-1-1-1 .45-1 1-.45 1-1 1H3c-.55 0-1 .45-1 1z\"/></svg>") 50% center no-repeat; }
.swagger-ui select {
background: url("data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 20 20\"><path d=\"M13.418 7.859a.695.695 0 01.978 0 .68.68 0 010 .969l-3.908 3.83a.697.697 0 01-.979 0l-3.908-3.83a.68.68 0 010-.969.695.695 0 01.978 0L10 11l3.418-3.141z\"/></svg>") right 10px center/20px no-repeat #212121;
background: url(data:image/svg+xml;base64,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) right 10px center/20px no-repeat #1c1c21;
border: 2px solid #41444e;
}
.swagger-ui select[multiple] { background: #212121; }
.swagger-ui button.invalid, .swagger-ui input[type=email].invalid, .swagger-ui input[type=file].invalid, .swagger-ui input[type=password].invalid, .swagger-ui input[type=search].invalid, .swagger-ui input[type=text].invalid, .swagger-ui select.invalid, .swagger-ui textarea.invalid {
background: #390e0e;
border-color: #c83232;
}
.swagger-ui input[type=email], .swagger-ui input[type=file], .swagger-ui input[type=password], .swagger-ui input[type=search], .swagger-ui input[type=text], .swagger-ui textarea {
background: #1c1c21;
border: 1px solid #404040;
}
.swagger-ui textarea {
background: rgba(28, 28, 33, .8);
color: #b5bac9;
}
.swagger-ui input[disabled], .swagger-ui select[disabled] {
background-color: #1f1f1f;
color: #bfbfbf;
}
.swagger-ui textarea[disabled] {
background-color: #41444e;
color: #fff;
}
.swagger-ui select[disabled] { border-color: #878787; }
.swagger-ui textarea:focus { border: 2px solid #2a69a7; }
.swagger-ui .checkbox input[type=checkbox] + label > .item {
background: #303030;
box-shadow: #303030 0 0 0 2px;
}
.swagger-ui .checkbox input[type=checkbox]:checked + label > .item { background: url("data:image/svg+xml;charset=utf-8,<svg width=\"10\" height=\"8\" viewBox=\"3 7 10 8\" xmlns=\"http://www.w3.org/2000/svg\"><path fill=\"%2341474E\" fill-rule=\"evenodd\" d=\"M6.333 15L3 11.667l1.333-1.334 2 2L11.667 7 13 8.333z\"/></svg>") 50% center no-repeat #303030; }
.swagger-ui .dialog-ux .backdrop-ux { background: rgba(0, 0, 0, .8); }
.swagger-ui .dialog-ux .modal-ux {
background: #1c1c21;
border: 1px solid #2e2e2e;
box-shadow: rgba(0, 0, 0, .2) 0 10px 30px 0;
}
.swagger-ui .dialog-ux .modal-ux-header .close-modal { background: 0 0; }
.swagger-ui .model .deprecated span, .swagger-ui .model .deprecated td { color: #bfbfbf !important; }
.swagger-ui .model-toggle::after { background: url("data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"24\" height=\"24\"><path d=\"M10 6L8.59 7.41 13.17 12l-4.58 4.59L10 18l6-6z\"/></svg>") 50% center/100% no-repeat; }
.swagger-ui .model-hint {
background: rgba(0, 0, 0, .7);
color: #ebebeb;
}
.swagger-ui section.models { border: 1px solid rgba(58, 64, 80, .3); }
.swagger-ui section.models.is-open h4 { border-bottom: 1px solid rgba(58, 64, 80, .3); }
.swagger-ui section.models .model-container { background: rgba(0, 0, 0, .05); }
.swagger-ui section.models .model-container:hover { background: rgba(0, 0, 0, .07); }
.swagger-ui .model-box { background: rgba(0, 0, 0, .1); }
.swagger-ui .prop-type { color: #aaaad4; }
.swagger-ui table thead tr td, .swagger-ui table thead tr th {
border-bottom: 1px solid rgba(58, 64, 80, .2);
color: #b5bac9;
}
.swagger-ui .parameter__name.required::after { color: rgba(230, 153, 153, .6); }
.swagger-ui .topbar .download-url-wrapper .select-label { color: #f0f0f0; }
.swagger-ui .topbar .download-url-wrapper .download-url-button {
background: #63a040;
color: #fff;
}
.swagger-ui .info .title small { background: #7c8492; }
.swagger-ui .info .title small.version-stamp { background-color: #7a9b27; }
.swagger-ui .auth-container .errors {
background-color: #350d0d;
color: #b5bac9;
}
.swagger-ui .errors-wrapper {
background: rgba(200, 50, 50, .1);
border: 2px solid #c83232;
}
.swagger-ui .markdown code, .swagger-ui .renderedmarkdown code {
background: rgba(0, 0, 0, .05);
color: #c299e6;
}
.swagger-ui .model-toggle:after { background: url(data:image/svg+xml;base64,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) 50% no-repeat; }
.swagger-ui .expand-operation svg, .swagger-ui section.models h4 svg { fill: #fff; }
::-webkit-scrollbar-track { background-color: #646464 !important; }
::-webkit-scrollbar-thumb {
background-color: #242424 !important;
border: 2px solid #3e4346 !important;
}
::-webkit-scrollbar-button:vertical:start:decrement {
background: linear-gradient(130deg, #696969 40%, rgba(255, 0, 0, 0) 41%), linear-gradient(230deg, #696969 40%, transparent 41%), linear-gradient(0deg, #696969 40%, transparent 31%);
background-color: #b6b6b6;
}
::-webkit-scrollbar-button:vertical:end:increment {
background: linear-gradient(310deg, #696969 40%, transparent 41%), linear-gradient(50deg, #696969 40%, transparent 41%), linear-gradient(180deg, #696969 40%, transparent 31%);
background-color: #b6b6b6;
}
::-webkit-scrollbar-button:horizontal:end:increment {
background: linear-gradient(210deg, #696969 40%, transparent 41%), linear-gradient(330deg, #696969 40%, transparent 41%), linear-gradient(90deg, #696969 30%, transparent 31%);
background-color: #b6b6b6;
}
::-webkit-scrollbar-button:horizontal:start:decrement {
background: linear-gradient(30deg, #696969 40%, transparent 41%), linear-gradient(150deg, #696969 40%, transparent 41%), linear-gradient(270deg, #696969 30%, transparent 31%);
background-color: #b6b6b6;
}
::-webkit-scrollbar-button, ::-webkit-scrollbar-track-piece { background-color: #3e4346 !important; }
.swagger-ui .black, .swagger-ui .checkbox, .swagger-ui .dark-gray, .swagger-ui .download-url-wrapper .loading, .swagger-ui .errors-wrapper .errors small, .swagger-ui .fallback, .swagger-ui .filter .loading, .swagger-ui .gray, .swagger-ui .hover-black:focus, .swagger-ui .hover-black:hover, .swagger-ui .hover-dark-gray:focus, .swagger-ui .hover-dark-gray:hover, .swagger-ui .hover-gray:focus, .swagger-ui .hover-gray:hover, .swagger-ui .hover-light-silver:focus, .swagger-ui .hover-light-silver:hover, .swagger-ui .hover-mid-gray:focus, .swagger-ui .hover-mid-gray:hover, .swagger-ui .hover-near-black:focus, .swagger-ui .hover-near-black:hover, .swagger-ui .hover-silver:focus, .swagger-ui .hover-silver:hover, .swagger-ui .light-silver, .swagger-ui .markdown pre, .swagger-ui .mid-gray, .swagger-ui .model .property, .swagger-ui .model .property.primitive, .swagger-ui .model-title, .swagger-ui .near-black, .swagger-ui .parameter__extension, .swagger-ui .parameter__in, .swagger-ui .prop-format, .swagger-ui .renderedmarkdown pre, .swagger-ui .response-col_links .response-undocumented, .swagger-ui .response-col_status .response-undocumented, .swagger-ui .silver, .swagger-ui section.models h4, .swagger-ui section.models h5, .swagger-ui span.token-not-formatted, .swagger-ui span.token-string, .swagger-ui table.headers .header-example, .swagger-ui table.model tr.description, .swagger-ui table.model tr.extension { color: #bfbfbf; }
.swagger-ui .hover-white:focus, .swagger-ui .hover-white:hover, .swagger-ui .info .title small pre, .swagger-ui .topbar a, .swagger-ui .white { color: #fff; }
.swagger-ui .bg-black-10, .swagger-ui .hover-bg-black-10:focus, .swagger-ui .hover-bg-black-10:hover, .swagger-ui .stripe-dark:nth-child(2n + 1) { background-color: rgba(0, 0, 0, .1); }
.swagger-ui .bg-white-10, .swagger-ui .hover-bg-white-10:focus, .swagger-ui .hover-bg-white-10:hover, .swagger-ui .stripe-light:nth-child(2n + 1) { background-color: rgba(28, 28, 33, .1); }
.swagger-ui .bg-light-silver, .swagger-ui .hover-bg-light-silver:focus, .swagger-ui .hover-bg-light-silver:hover, .swagger-ui .striped--light-silver:nth-child(2n + 1) { background-color: #6e6e6e; }
.swagger-ui .bg-moon-gray, .swagger-ui .hover-bg-moon-gray:focus, .swagger-ui .hover-bg-moon-gray:hover, .swagger-ui .striped--moon-gray:nth-child(2n + 1) { background-color: #4d4d4d; }
.swagger-ui .bg-light-gray, .swagger-ui .hover-bg-light-gray:focus, .swagger-ui .hover-bg-light-gray:hover, .swagger-ui .striped--light-gray:nth-child(2n + 1) { background-color: #2b2b2b; }
.swagger-ui .bg-near-white, .swagger-ui .hover-bg-near-white:focus, .swagger-ui .hover-bg-near-white:hover, .swagger-ui .striped--near-white:nth-child(2n + 1) { background-color: #242424; }
.swagger-ui .opblock-tag:hover, .swagger-ui section.models h4:hover { background: rgba(0, 0, 0, .02); }
.swagger-ui .checkbox p, .swagger-ui .dialog-ux .modal-ux-content h4, .swagger-ui .dialog-ux .modal-ux-content p, .swagger-ui .dialog-ux .modal-ux-header h3, .swagger-ui .errors-wrapper .errors h4, .swagger-ui .errors-wrapper hgroup h4, .swagger-ui .info .base-url, .swagger-ui .info .title, .swagger-ui .info h1, .swagger-ui .info h2, .swagger-ui .info h3, .swagger-ui .info h4, .swagger-ui .info h5, .swagger-ui .info li, .swagger-ui .info p, .swagger-ui .info table, .swagger-ui .loading-container .loading::after, .swagger-ui .model, .swagger-ui .opblock .opblock-section-header h4, .swagger-ui .opblock .opblock-section-header > label, .swagger-ui .opblock .opblock-summary-description, .swagger-ui .opblock .opblock-summary-operation-id, .swagger-ui .opblock .opblock-summary-path, .swagger-ui .opblock .opblock-summary-path__deprecated, .swagger-ui .opblock-description-wrapper, .swagger-ui .opblock-description-wrapper h4, .swagger-ui .opblock-description-wrapper p, .swagger-ui .opblock-external-docs-wrapper, .swagger-ui .opblock-external-docs-wrapper h4, .swagger-ui .opblock-external-docs-wrapper p, .swagger-ui .opblock-tag small, .swagger-ui .opblock-title_normal, .swagger-ui .opblock-title_normal h4, .swagger-ui .opblock-title_normal p, .swagger-ui .parameter__name, .swagger-ui .parameter__type, .swagger-ui .response-col_links, .swagger-ui .response-col_status, .swagger-ui .responses-inner h4, .swagger-ui .responses-inner h5, .swagger-ui .scheme-container .schemes > label, .swagger-ui .scopes h2, .swagger-ui .servers > label, .swagger-ui .tab li, .swagger-ui label, .swagger-ui select, .swagger-ui table.headers td { color: #b5bac9; }
.swagger-ui .download-url-wrapper .failed, .swagger-ui .filter .failed, .swagger-ui .model-deprecated-warning, .swagger-ui .parameter__deprecated, .swagger-ui .parameter__name.required span, .swagger-ui table.model tr.property-row .star { color: #e69999; }
.swagger-ui .opblock-body pre.microlight, .swagger-ui textarea.curl {
background: #41444e;
border-radius: 4px;
color: #fff;
}
.swagger-ui .expand-methods svg, .swagger-ui .expand-methods:hover svg { fill: #bfbfbf; }
.swagger-ui .auth-container, .swagger-ui .dialog-ux .modal-ux-header { border-bottom: 1px solid #2e2e2e; }
.swagger-ui .topbar .download-url-wrapper .select-label select, .swagger-ui .topbar .download-url-wrapper input[type=text] { border: 2px solid #63a040; }
.swagger-ui .info a, .swagger-ui .info a:hover, .swagger-ui .scopes h2 a { color: #99bde6; }
/* Dark Scrollbar */
::-webkit-scrollbar {
width: 14px;
height: 14px;
}
::-webkit-scrollbar-button {
background-color: #3e4346 !important;
}
::-webkit-scrollbar-track {
background-color: #646464 !important;
}
::-webkit-scrollbar-track-piece {
background-color: #3e4346 !important;
}
::-webkit-scrollbar-thumb {
height: 50px;
background-color: #242424 !important;
border: 2px solid #3e4346 !important;
}
::-webkit-scrollbar-corner {}
::-webkit-resizer {}
::-webkit-scrollbar-button:vertical:start:decrement {
background:
linear-gradient(130deg, #696969 40%, rgba(255, 0, 0, 0) 41%),
linear-gradient(230deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
linear-gradient(0deg, #696969 40%, rgba(0, 0, 0, 0) 31%);
background-color: #b6b6b6;
}
::-webkit-scrollbar-button:vertical:end:increment {
background:
linear-gradient(310deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
linear-gradient(50deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
linear-gradient(180deg, #696969 40%, rgba(0, 0, 0, 0) 31%);
background-color: #b6b6b6;
}
::-webkit-scrollbar-button:horizontal:end:increment {
background:
linear-gradient(210deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
linear-gradient(330deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
linear-gradient(90deg, #696969 30%, rgba(0, 0, 0, 0) 31%);
background-color: #b6b6b6;
}
::-webkit-scrollbar-button:horizontal:start:decrement {
background:
linear-gradient(30deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
linear-gradient(150deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
linear-gradient(270deg, #696969 30%, rgba(0, 0, 0, 0) 31%);
background-color: #b6b6b6;
}

17
static/swagger-ui/index.css vendored Normal file
View File

@@ -0,0 +1,17 @@
/*! Swagger UI 4.13.2 | https://swagger.io/tools/swagger-ui/ | Apache License 2.0 (license file can be found at ./LICENSE) */
html {
box-sizing: border-box;
overflow: -moz-scrollbars-vertical;
overflow-y: scroll;
}
*,
*:before,
*:after {
box-sizing: inherit;
}
body {
margin: 0;
background: #fafafa;
}

79
static/swagger-ui/oauth2-redirect.html vendored Normal file
View File

@@ -0,0 +1,79 @@
<!doctype html>
<html lang="en-US">
<head>
<title>Swagger UI: OAuth2 Redirect</title>
</head>
<body>
<script>
'use strict';
function run () {
var oauth2 = window.opener.swaggerUIRedirectOauth2;
var sentState = oauth2.state;
var redirectUrl = oauth2.redirectUrl;
var isValid, qp, arr;
if (/code|token|error/.test(window.location.hash)) {
qp = window.location.hash.substring(1);
} else {
qp = location.search.substring(1);
}
arr = qp.split("&");
arr.forEach(function (v,i,_arr) { _arr[i] = '"' + v.replace('=', '":"') + '"';});
qp = qp ? JSON.parse('{' + arr.join() + '}',
function (key, value) {
return key === "" ? value : decodeURIComponent(value);
}
) : {};
isValid = qp.state === sentState;
if ((
oauth2.auth.schema.get("flow") === "accessCode" ||
oauth2.auth.schema.get("flow") === "authorizationCode" ||
oauth2.auth.schema.get("flow") === "authorization_code"
) && !oauth2.auth.code) {
if (!isValid) {
oauth2.errCb({
authId: oauth2.auth.name,
source: "auth",
level: "warning",
message: "Authorization may be unsafe, passed state was changed in server. The passed state wasn't returned from auth server."
});
}
if (qp.code) {
delete oauth2.state;
oauth2.auth.code = qp.code;
oauth2.callback({auth: oauth2.auth, redirectUrl: redirectUrl});
} else {
let oauthErrorMsg;
if (qp.error) {
oauthErrorMsg = "["+qp.error+"]: " +
(qp.error_description ? qp.error_description+ ". " : "no accessCode received from the server. ") +
(qp.error_uri ? "More info: "+qp.error_uri : "");
}
oauth2.errCb({
authId: oauth2.auth.name,
source: "auth",
level: "error",
message: oauthErrorMsg || "[Authorization failed]: no accessCode received from the server."
});
}
} else {
oauth2.callback({auth: oauth2.auth, token: qp, isValid: isValid, redirectUrl: redirectUrl});
}
window.close();
}
if (document.readyState !== 'loading') {
run();
} else {
document.addEventListener('DOMContentLoaded', function () {
run();
});
}
</script>
</body>
</html>

2
static/swagger-ui/swagger-ui-bundle.js vendored Normal file
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3
static/swagger-ui/swagger-ui.css vendored Normal file
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2
static/swagger-ui/swagger-ui.js vendored Normal file
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57
templates/index.html
View File

@@ -9,7 +9,8 @@
<link rel="stylesheet" href="static/bootstrap.min.css">
<link rel="stylesheet" href="static/bootstrap-toggle.min.css">
<link rel="stylesheet" href="static/open-iconic-bootstrap.min.css">
<link rel="stylesheet" href="static/custom.css?ver=1.18.1b">
<link href="static/open-iconic/css/open-iconic.css" rel="stylesheet">
<link rel="stylesheet" href="static/custom.css?ver=1.18.1c">
<script src="static/jquery-3.6.0.min.js"></script>
<script src="static/jquery-ui.sortable.min.js"></script>
@@ -17,7 +18,7 @@
<script src="static/bootstrap.min.js"></script>
<script src="static/bootstrap-toggle.min.js"></script>
<script src="static/rangy-core.min.js"></script>
<script src="static/application.js?ver=1.18.1c"></script>
<script src="static/application.js?ver=1.18.1e"></script>
<script src="static/favicon.js"></script>
{% if flaskwebgui %}
<script src="static/flask_web_gui.js"></script>
@@ -38,12 +39,8 @@
<div class="collapse navbar-collapse" id="navbarNavDropdown">
<ul class="nav navbar-nav">
{% if not hide_ai_menu %}
<li class="nav-item dropdown">
<a class="nav-link dropdown-toggle" href="#" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">AI</a>
<div class="dropdown-menu">
<a class="dropdown-item" href="#" id="btn_loadmodel">Load Model</a>
<a class="dropdown-item" href="#" id="btn_showmodel">Model Info</a>
</div>
<li class="nav-item">
<a class="nav-link" href="#" id="btn_loadmodel">AI</a>
</li>
{% endif %}
<li class="nav-item dropdown">
@@ -75,7 +72,7 @@
<div class="dropdown-menu">
<a class="dropdown-item" href="#" id="btn_import">AI Dungeon Adventure</a>
<a class="dropdown-item" href="#" id="btn_importwi">AI Dungeon World Info</a>
<a class="dropdown-item" href="#" id="btn_impaidg">aidg.club Prompt</a>
<a class="dropdown-item" href="#" id="btn_impaidg">aetherroom.club Prompt</a>
</div>
</li>
<li class="nav-item">
@@ -123,6 +120,11 @@
</div>
<div class="row" id="formatmenu">
</div>
<div id="token_prob_menu" class="row hidden">
<div id="token_prob_container"></div>
</div>
<div class="layer-container">
<div class="layer-bottom row" id="gamescreen">
<span id="gametext" contenteditable="true"><p>...</p></span>
@@ -156,7 +158,7 @@
<div id="inputrowmode">
<button type="button" class="btn btn-secondary hidden" id="btnmode">Mode:<br/><b id="btnmode_label">Story</b></button>
</div>
<div id="inputrowleft">
<div id="inputrowleft" class="tokens-counted">
<textarea class="form-control" id="input_text" placeholder="Enter text here"></textarea>
</div>
<div id="inputrowright">
@@ -169,7 +171,7 @@
<div class="anotelabel no-padding">
Author's Note
</div>
<div class="anotefield">
<div class="anotefield tokens-counted">
<textarea class="form-control" placeholder="Author's Note" id="anoteinput"></textarea>
</div>
</div>
@@ -210,7 +212,7 @@
</div>
</div>
<div class="hidden" id="popupcontainer">
<div id="popup">
<div id="popup_old">
<div id="popuptitlebar">
<div id="popuptitletext">Select an Adventure to Import</div>
</div>
@@ -233,7 +235,7 @@
<div class="popuptitletext">Enter the Prompt Number</div>
</div>
<div class="aidgpopuplistheader">
(4-digit number at the end of aidg.club URL)
(4-digit number at the end of aetherroom.club URL)
</div>
<div class="aidgpopupcontent">
<input class="form-control" type="text" placeholder="Prompt Number" id="aidgpromptnum">
@@ -291,7 +293,7 @@
<div id="loadmodellistbreadcrumbs">
</div>
<div id="loadmodellistcontent" style="overflow: scroll; height: 300px;">
<div id="loadmodellistcontent" style="overflow: auto; height: 300px;">
</div>
<div class="popupfooter">
<input class="form-control hidden" type="text" placeholder="key" id="modelkey" onblur="socket.send({'cmd': 'OAI_Key_Update', 'key': $('#modelkey')[0].value});">
@@ -340,6 +342,7 @@
<div id="sppopup">
<div class="popuptitlebar">
<div class="popuptitletext">Select A Soft Prompt To Use</div>
<button class="btn btn-primary" onclick="socket.emit('show_folder_soft_prompt', {});"><span class="oi" style="color: white;" data-glyph="folder"></span></button>
</div>
<div id="splistcontent">
</div>
@@ -353,6 +356,7 @@
<div id="uspopup">
<div class="popuptitlebar">
<div class="popuptitletext">Select userscripts to load; drag-and-drop to reorder</div>
<button class="btn btn-primary" onclick="socket.emit('show_folder_usersripts', {});"><span class="oi" style="color: white;" data-glyph="folder"></span></button>
</div>
<div class="usheadergrid">
<div>[AVAILABLE]</div>
@@ -441,10 +445,10 @@
<div class="popuptitletext">Model Info</div>
</div>
<div id=showmodelnamecontent style="width:50%;">
Read Only
Model Info Missing
</div>
<div class="popupfooter" style="width:50% center;">
<button type="button" class="btn btn-primary" onclick='$("#showmodelnamecontainer").addClass("hidden");'>OK</button>
</div>
</div>
</div>
@@ -474,5 +478,26 @@
</div>
</div>
</div>
<!------------- Pop-Up ------------------------------->
<div class="popupcontainer hidden" id="popup">
<div class="new_popup">
<div style="height:100%;">
<div class="title" id="popup_title">
Popup Title
</div>
<div id="popup_breadcrumbs"></div>
<div class="popup_list_area" id="popup_list"></div>
<div class="popup_load_cancel hidden" id="popup_upload">
<input type=file id="popup_upload_file">
</div>
<div style="background-color: black">Drag file(s) above or click here to Upload File<input id="popup_upload_input" type=file onchange="upload_file(this)"></div>
<div class="popup_load_cancel" id="popup_load_cancel">
<button class="btn btn-secondary popup_load_cancel_button" id="popup_accept">Load</button>
<button class="btn btn-primary popup_load_cancel_button" id="popup_cancel" onclick='document.getElementById("popup").classList.add("hidden");'>Cancel</button>
</div>
</div>
</div>
</div>
</body>
</html>

35
templates/swagger-ui.html Normal file
View File

@@ -0,0 +1,35 @@
{# This is the HTML template for Swagger UI (the GUI for the API documentation at /api/latest/docs) #}
<!DOCTYPE html>
<html lang="en">
<head>
<title>KoboldAI API</title>
<meta charset="UTF-8">
<link rel="stylesheet" type="text/css" href="/static/swagger-ui/swagger-ui.css" />
<link rel="stylesheet" type="text/css" href="/static/swagger-ui/index.css" />
<script>
if (window.matchMedia && window.matchMedia("(prefers-color-scheme: dark)").matches) document.write('<link rel="stylesheet" type="text/css" href="/static/swagger-ui/SwaggerDark.css" />');
</script>
</head>
<body>
<div id="swagger-ui"></div>
<script src="/static/swagger-ui/swagger-ui-bundle.js" charset="UTF-8"></script>
<script>
window.onload = function() {
window.ui = SwaggerUIBundle({
url: "{{ url }}",
oauth2RedirectUrl: "/static/swagger-ui/oauth2-redirect.html",
dom_id: "#swagger-ui",
deepLinking: true,
defaultModelsExpandDepth: 0, // Causes the "Schemas" section at the bottom to be collapsed by default
presets: [
SwaggerUIBundle.presets.apis
],
plugins: [
SwaggerUIBundle.plugins.DownloadUrl
],
layout: "BaseLayout"
});
};
</script>
</body>
</html>

4
torch_lazy_loader.py
View File

@@ -52,7 +52,7 @@ import pickle
import torch
import utils
from torch.nn import Module
from typing import Any, Callable, Dict, Optional, Tuple, Type, Union
from typing import Any, Callable, Dict, Optional, Tuple, Union
_EXTRA_STATE_KEY_SUFFIX = '_extra_state'
@@ -73,7 +73,7 @@ STORAGE_TYPE_MAP = {
class LazyTensor:
def __init__(self, storage_type: Type[torch._StorageBase], key: str, location: str, dtype: Optional[torch.dtype] = None, seek_offset: Optional[int] = None, shape: Optional[Tuple[int, ...]] = None, stride: Optional[Tuple[int, ...]] = None, requires_grad=False, backward_hooks: Any = None):
def __init__(self, storage_type, key: str, location: str, dtype: Optional[torch.dtype] = None, seek_offset: Optional[int] = None, shape: Optional[Tuple[int, ...]] = None, stride: Optional[Tuple[int, ...]] = None, requires_grad=False, backward_hooks: Any = None):
self.storage_type = storage_type
self.key = key
self.location = location

33
tpu_mtj_backend.py
View File

@@ -56,6 +56,22 @@ from mesh_transformer.util import to_bf16
params: Dict[str, Any] = {}
__seed = random.randrange(sys.maxsize)
rng = random.Random(__seed)
def get_rng_seed():
return __seed
def set_rng_seed(seed: int):
global __seed, rng
rng = random.Random(seed)
__seed = seed
return seed
def randomize_rng_seed():
return set_rng_seed(random.randrange(sys.maxsize))
def warper_callback(logits) -> np.array:
raise NotImplementedError("`tpu_mtj_backend.warper_callback()` needs to be defined")
@@ -547,7 +563,7 @@ class PenalizingCausalTransformer(CausalTransformer):
compiling_callback()
numseqs = numseqs_aux.shape[0]
# These are the tokens that we don't want the AI to ever write
self.badwords = jnp.array(koboldai_vars.badwordsids).squeeze()
badwords = jnp.array(koboldai_vars.badwordsids).squeeze()
@hk.transform
def generate_sample(context, ctx_length):
# Give the initial context to the transformer
@@ -605,7 +621,7 @@ class PenalizingCausalTransformer(CausalTransformer):
# Remove any tokens in the badwords list by setting
# their logits to negative infinity which effectively
# makes their probabilities of being chosen zero
logits = logits.at[self.badwords].set(-jnp.inf)
logits = logits.at[badwords].set(-jnp.inf)
# Use the sampler (kobold_sample_static) to pick one token
# based on the logits array as a 0D uint32 array
# (higher logit means higher probability of being
@@ -728,7 +744,7 @@ class PenalizingCausalTransformer(CausalTransformer):
assert not return_logits
assert gen_length.ndim == 1
assert soft_embeddings is not None
key = hk.PRNGSequence(random.randint(0, 2 ** 60))
key = hk.PRNGSequence(rng.randint(0, 2 ** 60))
batch_size = ctx.shape[0]
self.batch_size = batch_size
_numseqs_aux = jnp.empty((batch_size, numseqs), dtype=np.uint32)
@@ -776,7 +792,7 @@ class PenalizingCausalTransformer(CausalTransformer):
return sample_data, n_generated, regeneration_required, halt
def generate_static(self, ctx, ctx_length, gen_length, numseqs, sampler_options, return_logits=False, soft_embeddings=None):
assert not return_logits
key = hk.PRNGSequence(random.randint(0, 2 ** 60))
key = hk.PRNGSequence(rng.randint(0, 2 ** 60))
batch_size = ctx.shape[0]
self.batch_size = batch_size
started_compiling_callback()
@@ -1025,7 +1041,7 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
elif "eos_token_id" in kwargs:
pad_token_id = kwargs["eos_token_id"]
if not hasattr(vars, "sampler_order") or not koboldai_vars.sampler_order:
if not hasattr(koboldai_vars, "sampler_order") or not koboldai_vars.sampler_order:
koboldai_vars.sampler_order = utils.default_sampler_order.copy()
default_params = {
@@ -1145,9 +1161,9 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
tpu_address = tpu_address.replace("grpc://", "")
tpu_address_without_port = tpu_address.split(':', 1)[0]
url = f'http://{tpu_address_without_port}:8475/requestversion/{driver_version}'
requests.post(url)
config.FLAGS.jax_xla_backend = "tpu_driver"
config.FLAGS.jax_backend_target = "grpc://" + tpu_address
requests.post(url)
spinner.terminate()
print()
@@ -1230,13 +1246,14 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
if utils.num_shards is not None:
utils.current_shard += 1
for key in sorted(model_dict.keys(), key=lambda k: (model_dict[k].key, model_dict[k].seek_offset)):
model_spec_key = max((k for k in model_spec.keys() if key.endswith(k)), key=len, default=None)
# Some model weights are used by transformers but not by MTJ.
# We have to materialize these weights anyways because
# transformers will throw a tantrum otherwise. To attain
# the least possible memory usage, we create them as meta
# tensors, which don't take up any actual CPU or TPU memory.
if key not in model_spec:
if model_spec_key is None:
model_dict[key] = torch.empty(model_dict[key].shape, dtype=model_dict[key].dtype, device="meta")
utils.bar.update(1)
continue
@@ -1251,7 +1268,7 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
if current_offset != model_dict[key].seek_offset:
f.read(model_dict[key].seek_offset - current_offset)
current_offset = model_dict[key].seek_offset
spec = model_spec[key]
spec = model_spec[model_spec_key]
transforms = set(spec.get("transforms", ()))
if not isinstance(model_dict[key], torch_lazy_loader.LazyTensor):
error = f"Duplicate key {repr(key)}"

8
update-koboldai.bat
View File

@@ -1,6 +1,5 @@
@echo off
%~d0
cd %~dp0
cd /d %~dp0
TITLE KoboldAI - Updater
SET /P M=<loader.settings
IF %M%==1 GOTO drivemap
@@ -50,4 +49,9 @@ git remote add origin %origin%
git fetch --all
git checkout %branch% -f
git reset --hard origin/%branch%
IF %M%==1 umamba.exe install --no-shortcuts -r K:\python\ -n base -f "%~dp0\environments\huggingface.yml" -y --always-copy
IF %M%==2 umamba.exe install --no-shortcuts -r miniconda3 -n base -f environments\huggingface.yml -y --always-copy
IF %M%==3 umamba.exe install --no-shortcuts -r B:\python\ -n base -f "%~dp0\environments\huggingface.yml" -y --always-copy
%windir%\system32\timeout -t 10

8
userscripts/api_documentation.html vendored
View File

@@ -470,11 +470,11 @@
<pre class=" language-lua"><code class="prism language-lua"><span class="token keyword">local</span> entry <span class="token operator">=</span> kobold<span class="token punctuation">.</span>worldinfo<span class="token punctuation">[</span><span class="token number">5</span><span class="token punctuation">]</span> <span class="token comment">-- Retrieves fifth entry from top as a KoboldWorldInfoEntry</span>
</code></pre>
<p>You can use <code>ipairs</code> or a numeric loop to iterate from top to bottom:</p>
<pre class=" language-lua"><code class="prism language-lua"><span class="token keyword">for</span> index<span class="token punctuation">,</span> entry <span class="token keyword">in</span> <span class="token function">ipairs</span><span class="token punctuation">(</span>kobold<span class="token punctuation">.</span>worldinfo<span class="token punctuation">)</span><span class="token punctuation">:</span>
<pre class=" language-lua"><code class="prism language-lua"><span class="token keyword">for</span> index<span class="token punctuation">,</span> entry <span class="token keyword">in</span> <span class="token function">ipairs</span><span class="token punctuation">(</span>kobold<span class="token punctuation">.</span>worldinfo<span class="token punctuation">)</span> <span class="token keyword">do</span>
<span class="token function">print</span><span class="token punctuation">(</span>index<span class="token punctuation">,</span> entry<span class="token punctuation">.</span>content<span class="token punctuation">)</span>
<span class="token keyword">end</span>
</code></pre>
<pre class=" language-lua"><code class="prism language-lua"><span class="token keyword">for</span> index <span class="token operator">=</span> <span class="token number">1</span><span class="token punctuation">,</span> <span class="token operator">#</span>kobold<span class="token punctuation">.</span>worldinfo <span class="token keyword">do</span><span class="token punctuation">:</span>
<pre class=" language-lua"><code class="prism language-lua"><span class="token keyword">for</span> index <span class="token operator">=</span> <span class="token number">1</span><span class="token punctuation">,</span> <span class="token operator">#</span>kobold<span class="token punctuation">.</span>worldinfo <span class="token keyword">do</span>
<span class="token function">print</span><span class="token punctuation">(</span>index<span class="token punctuation">,</span> kobold<span class="token punctuation">.</span>worldinfo<span class="token punctuation">[</span>index<span class="token punctuation">]</span><span class="token punctuation">.</span>content<span class="token punctuation">)</span>
<span class="token keyword">end</span>
</code></pre>
@@ -531,11 +531,11 @@
<p>Can be indexed in amortized constant worst-case time and iterated over and has a <code>finduid</code> method just like <code>kobold.worldinfo</code>, but gets folders (as <code>KoboldWorldInfoFolder</code> objects) instead.</p>
<pre class=" language-lua"><code class="prism language-lua"><span class="token keyword">local</span> folder <span class="token operator">=</span> kobold<span class="token punctuation">.</span>worldinfo<span class="token punctuation">.</span>folders<span class="token punctuation">[</span><span class="token number">5</span><span class="token punctuation">]</span> <span class="token comment">-- Retrieves fifth folder from top as a KoboldWorldInfoFolder</span>
</code></pre>
<pre class=" language-lua"><code class="prism language-lua"><span class="token keyword">for</span> index<span class="token punctuation">,</span> folder <span class="token keyword">in</span> <span class="token function">ipairs</span><span class="token punctuation">(</span>kobold<span class="token punctuation">.</span>worldinfo<span class="token punctuation">.</span>folders<span class="token punctuation">)</span><span class="token punctuation">:</span>
<pre class=" language-lua"><code class="prism language-lua"><span class="token keyword">for</span> index<span class="token punctuation">,</span> folder <span class="token keyword">in</span> <span class="token function">ipairs</span><span class="token punctuation">(</span>kobold<span class="token punctuation">.</span>worldinfo<span class="token punctuation">.</span>folders<span class="token punctuation">)</span> <span class="token keyword">do</span>
<span class="token function">print</span><span class="token punctuation">(</span>index<span class="token punctuation">,</span> folder<span class="token punctuation">.</span>name<span class="token punctuation">)</span>
<span class="token keyword">end</span>
</code></pre>
<pre class=" language-lua"><code class="prism language-lua"><span class="token keyword">for</span> index <span class="token operator">=</span> <span class="token number">1</span><span class="token punctuation">,</span> <span class="token operator">#</span>kobold<span class="token punctuation">.</span>worldinfo<span class="token punctuation">.</span>folders <span class="token keyword">do</span><span class="token punctuation">:</span>
<pre class=" language-lua"><code class="prism language-lua"><span class="token keyword">for</span> index <span class="token operator">=</span> <span class="token number">1</span><span class="token punctuation">,</span> <span class="token operator">#</span>kobold<span class="token punctuation">.</span>worldinfo<span class="token punctuation">.</span>folders <span class="token keyword">do</span>
<span class="token function">print</span><span class="token punctuation">(</span>index<span class="token punctuation">,</span> kobold<span class="token punctuation">.</span>worldinfo<span class="token punctuation">.</span>folders<span class="token punctuation">[</span>index<span class="token punctuation">]</span><span class="token punctuation">.</span>name<span class="token punctuation">)</span>
<span class="token keyword">end</span>
</code></pre>

8
userscripts/api_documentation.md
View File

@@ -503,13 +503,13 @@ local entry = kobold.worldinfo[5] -- Retrieves fifth entry from top as a Kobold
You can use `ipairs` or a numeric loop to iterate from top to bottom:
```lua
for index, entry in ipairs(kobold.worldinfo):
for index, entry in ipairs(kobold.worldinfo) do
print(index, entry.content)
end
```
```lua
for index = 1, #kobold.worldinfo do:
for index = 1, #kobold.worldinfo do
print(index, kobold.worldinfo[index].content)
end
```
@@ -587,13 +587,13 @@ local folder = kobold.worldinfo.folders[5] -- Retrieves fifth folder from top a
```
```lua
for index, folder in ipairs(kobold.worldinfo.folders):
for index, folder in ipairs(kobold.worldinfo.folders) do
print(index, folder.name)
end
```
```lua
for index = 1, #kobold.worldinfo.folders do:
for index = 1, #kobold.worldinfo.folders do
print(index, kobold.worldinfo.folders[index].name)
end
```

4
userscripts/kaipreset_basic_phrase_bias.lua
View File

@@ -183,8 +183,8 @@ function userscript.genmod()
max_overlap[i] = 0
local s = {}
local z = {[0] = 0}
local l = 1
local r = 1
local l = 0
local r = 0
local n_s = math.min(n_tokens, bias_entry.n_tokens)
local j = 0
for k = 1, n_s do

3
utils.py
View File

@@ -175,10 +175,9 @@ def num_layers(config):
from flask_socketio import emit
class Send_to_socketio(object):
def write(self, bar):
#print("should be emitting: ", bar, end="")
time.sleep(0.01)
try:
emit('from_server', {'cmd': 'model_load_status', 'data': bar.replace(" ", "&nbsp;")}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'model_load_status', 'data': bar.replace(" ", "&nbsp;")}, broadcast=True)
except:
pass