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Add AutoAWQ to README
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@ -11,7 +11,7 @@ Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github.
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## Features
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* 3 interface modes: default (two columns), notebook, and chat
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* Multiple model backends: [transformers](https://github.com/huggingface/transformers), [llama.cpp](https://github.com/ggerganov/llama.cpp), [ExLlama](https://github.com/turboderp/exllama), [ExLlamaV2](https://github.com/turboderp/exllamav2), [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ), [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa), [CTransformers](https://github.com/marella/ctransformers)
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* Multiple model backends: [transformers](https://github.com/huggingface/transformers), [llama.cpp](https://github.com/ggerganov/llama.cpp), [ExLlama](https://github.com/turboderp/exllama), [ExLlamaV2](https://github.com/turboderp/exllamav2), [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ), [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa), [CTransformers](https://github.com/marella/ctransformers), [AutoAWQ](https://github.com/casper-hansen/AutoAWQ)
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* Dropdown menu for quickly switching between different models
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* LoRA: load and unload LoRAs on the fly, train a new LoRA using QLoRA
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* Precise instruction templates for chat mode, including Llama-2-chat, Alpaca, Vicuna, WizardLM, StableLM, and many others
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@ -107,14 +107,12 @@ def infer_loader(model_name, model_settings):
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loader = None
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elif (path_to_model / 'quantize_config.json').exists() or ('wbits' in model_settings and type(model_settings['wbits']) is int and model_settings['wbits'] > 0):
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loader = 'AutoGPTQ'
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elif (path_to_model / 'quant_config.json').exists():
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elif (path_to_model / 'quant_config.json').exists() or re.match(r'.*-awq', model_name.lower()):
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loader = 'AutoAWQ'
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elif len(list(path_to_model.glob('*.gguf'))) > 0:
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loader = 'llama.cpp'
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elif re.match(r'.*\.gguf', model_name.lower()):
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loader = 'llama.cpp'
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elif re.match(r'.*-awq', model_name.lower()):
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loader = 'AutoAWQ'
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elif re.match(r'.*rwkv.*\.pth', model_name.lower()):
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loader = 'RWKV'
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elif re.match(r'.*exl2', model_name.lower()):
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