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Add gpt_bigcode support, fix 8-bit GPTQ incoherence
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@@ -11,3 +11,6 @@
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<a href="https://github.com/0cc4m/GPTQ-for-LLaMa/releases/download/2023-05-19-2/gptq_koboldai-0.0.4-cp38-cp38-linux_x86_64.whl">gptq_koboldai-0.0.4-cp38-cp38-linux_x86_64.whl</a>
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<a href="https://github.com/0cc4m/GPTQ-for-LLaMa/releases/download/2023-05-19-2/gptq_koboldai-0.0.4-cp38-cp38-win_amd64.whl">gptq_koboldai-0.0.4-cp38-cp38-win_amd64.whl</a>
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<a href="https://github.com/0cc4m/GPTQ-for-LLaMa/releases/download/2023-05-23/gptq_koboldai-0.0.5-cp38-cp38-linux_x86_64.whl">gptq_koboldai-0.0.5-cp38-cp38-linux_x86_64.whl</a>
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<a href="https://github.com/0cc4m/GPTQ-for-LLaMa/releases/download/2023-05-23/gptq_koboldai-0.0.5-cp38-cp38-win_amd64.whl">gptq_koboldai-0.0.5-cp38-cp38-win_amd64.whl</a>
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@@ -47,6 +47,6 @@ dependencies:
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- diffusers
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- git+https://github.com/0cc4m/hf_bleeding_edge/
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- --find-links=https://0cc4m.github.io/KoboldAI/gptq-whl-links.html
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- gptq_koboldai==0.0.4
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- gptq_koboldai==0.0.5
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- einops
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- peft==0.3.0
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@@ -35,6 +35,7 @@ from gptq.gptj import load_quant as gptj_load_quant
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from gptq.gptneox import load_quant as gptneox_load_quant
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from gptq.llama import load_quant as llama_load_quant
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from gptq.opt import load_quant as opt_load_quant
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from gptq.bigcode import load_quant as bigcode_load_quant
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from gptq.mpt import load_quant as mpt_load_quant
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from gptq.offload import load_quant_offload
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@@ -220,6 +221,8 @@ class model_backend(HFTorchInferenceModel):
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model = load_quant_offload(opt_load_quant, location, gptq_file, gptq_bits, gptq_groupsize, self.gpu_layers_list)
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elif model_type == "mpt":
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model = load_quant_offload(mpt_load_quant, location, gptq_file, gptq_bits, gptq_groupsize, self.gpu_layers_list)
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elif model_type == "gpt_bigcode":
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model = load_quant_offload(bigcode_load_quant, location, path_4bit, utils.koboldai_vars.gptq_bits, groupsize, self.gpu_layers_list).half()
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elif autogptq_support:
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# Monkey patch in hf_bleeding_edge to avoid having to trust remote code
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auto_gptq.modeling._utils.AutoConfig = hf_bleeding_edge.AutoConfig
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