rixty/KoboldAI-Client
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New download machanism

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Automatically converts Huggingface cache models to full models on (down)load.
WARNING: Does wipe old cache/ dir inside the KoboldAI folder, make a backup before you run these models if you are bandwith constraint.
This commit is contained in:
henk717 2021-12-16 01:40:04 +01:00
parent 5e3e3f3578
commit e3d9c2d690
1 changed files with 18 additions and 7 deletions
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25
aiserver.py
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@ -850,25 +850,36 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransforme
# If base HuggingFace model was chosen
else:
# Is CUDA available? If so, use GPU, otherwise fall back to CPU
tokenizer = GPT2TokenizerFast.from_pretrained(vars.model, cache_dir="cache/")
if(os.path.isdir(vars.model.replace('/', '_'))):
with(maybe_use_float16()):
tokenizer = GPT2TokenizerFast.from_pretrained(vars.model.replace('/', '_'), cache_dir="cache/")
model = AutoModelForCausalLM.from_pretrained(vars.model.replace('/', '_'), cache_dir="cache/", **maybe_low_cpu_mem_usage())
else:
print("Model does not exist locally, attempting to download from Huggingface...")
tokenizer = GPT2TokenizerFast.from_pretrained(vars.model, cache_dir="cache/")
with(maybe_use_float16()):
model = AutoModelForCausalLM.from_pretrained(vars.model, cache_dir="cache/", **maybe_low_cpu_mem_usage())
model = model.half()
import shutil
shutil.rmtree("cache/")
model.save_pretrained(vars.model.replace('/', '_'))
tokenizer.save_pretrained(vars.model.replace('/', '_'))
if(vars.hascuda):
if(vars.usegpu):
with(maybe_use_float16()):
model = AutoModelForCausalLM.from_pretrained(vars.model, cache_dir="cache/", **maybe_low_cpu_mem_usage())
vars.modeldim = get_hidden_size_from_model(model)
model = model.half().to(0)
generator = model.generate
elif(vars.breakmodel): # Use both RAM and VRAM (breakmodel)
with(maybe_use_float16()):
model = AutoModelForCausalLM.from_pretrained(vars.model, cache_dir="cache/", **maybe_low_cpu_mem_usage())
vars.modeldim = get_hidden_size_from_model(model)
device_config(model)
else:
model = AutoModelForCausalLM.from_pretrained(vars.model, cache_dir="cache/", **maybe_low_cpu_mem_usage())
model = model.to('cpu').float()
vars.modeldim = get_hidden_size_from_model(model)
generator = model.generate
else:
model = AutoModelForCausalLM.from_pretrained(vars.model, cache_dir="cache/", **maybe_low_cpu_mem_usage())
model.to('cpu').float()
vars.modeldim = get_hidden_size_from_model(model)
generator = model.generate