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Make AutoGPTQ the default again

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Purely for compatibility with more models.
You should still use ExLlama_HF for LLaMA models.
This commit is contained in:
oobabooga 2023-07-15 22:29:23 -07:00
parent 5e3f7e00a9
commit 27a84b4e04
2 changed files with 3 additions and 3 deletions
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2
modules/models_settings.py
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@ -23,7 +23,7 @@ def infer_loader(model_name):
if not path_to_model.exists(): if not path_to_model.exists():
loader = None loader = None
elif Path(f'{shared.args.model_dir}/{model_name}/quantize_config.json').exists() or ('wbits' in model_settings and type(model_settings['wbits']) is int and model_settings['wbits'] > 0): elif Path(f'{shared.args.model_dir}/{model_name}/quantize_config.json').exists() or ('wbits' in model_settings and type(model_settings['wbits']) is int and model_settings['wbits'] > 0):
loader = 'ExLlama_HF' loader = 'AutoGPTQ'
elif len(list(path_to_model.glob('*ggml*.bin'))) > 0: elif len(list(path_to_model.glob('*ggml*.bin'))) > 0:
loader = 'llama.cpp' loader = 'llama.cpp'
elif re.match('.*ggml.*\.bin', model_name.lower()): elif re.match('.*ggml.*\.bin', model_name.lower()):

4
server.py
View File

@ -204,7 +204,7 @@ def create_model_menus():
with gr.Row(): with gr.Row():
with gr.Column(): with gr.Column():
shared.gradio['loader'] = gr.Dropdown(label="Model loader", choices=["Transformers", "ExLlama_HF", "AutoGPTQ", "llama.cpp", "ExLlama", "llama.cpp_HF", "GPTQ-for-LLaMa"], value=None) shared.gradio['loader'] = gr.Dropdown(label="Model loader", choices=["Transformers", "ExLlama_HF", "ExLlama", "AutoGPTQ", "GPTQ-for-LLaMa", "llama.cpp", "llamacpp_HF"], value=None)
with gr.Box(): with gr.Box():
with gr.Row(): with gr.Row():
with gr.Column(): with gr.Column():
@ -223,7 +223,7 @@ def create_model_menus():
shared.gradio['groupsize'] = gr.Dropdown(label="groupsize", choices=["None", 32, 64, 128, 1024], value=str(shared.args.groupsize) if shared.args.groupsize > 0 else "None") shared.gradio['groupsize'] = gr.Dropdown(label="groupsize", choices=["None", 32, 64, 128, 1024], value=str(shared.args.groupsize) if shared.args.groupsize > 0 else "None")
shared.gradio['model_type'] = gr.Dropdown(label="model_type", choices=["None", "llama", "opt", "gptj"], value=shared.args.model_type or "None") shared.gradio['model_type'] = gr.Dropdown(label="model_type", choices=["None", "llama", "opt", "gptj"], value=shared.args.model_type or "None")
shared.gradio['pre_layer'] = gr.Slider(label="pre_layer", minimum=0, maximum=100, value=shared.args.pre_layer[0] if shared.args.pre_layer is not None else 0) shared.gradio['pre_layer'] = gr.Slider(label="pre_layer", minimum=0, maximum=100, value=shared.args.pre_layer[0] if shared.args.pre_layer is not None else 0)
shared.gradio['autogptq_info'] = gr.Markdown('On some systems, AutoGPTQ can be 2x slower than GPTQ-for-LLaMa. You can manually select the GPTQ-for-LLaMa loader above.') shared.gradio['autogptq_info'] = gr.Markdown('* ExLlama_HF is recommended over AutoGPTQ for models derived from LLaMA.')
shared.gradio['gpu_split'] = gr.Textbox(label='gpu-split', info='Comma-separated list of VRAM (in GB) to use per GPU. Example: 20,7,7') shared.gradio['gpu_split'] = gr.Textbox(label='gpu-split', info='Comma-separated list of VRAM (in GB) to use per GPU. Example: 20,7,7')
shared.gradio['max_seq_len'] = gr.Slider(label='max_seq_len', minimum=2048, maximum=16384, step=256, info='Maximum sequence length.', value=shared.args.max_seq_len) shared.gradio['max_seq_len'] = gr.Slider(label='max_seq_len', minimum=2048, maximum=16384, step=256, info='Maximum sequence length.', value=shared.args.max_seq_len)
shared.gradio['compress_pos_emb'] = gr.Slider(label='compress_pos_emb', minimum=1, maximum=8, step=1, info='Positional embeddings compression factor. Should typically be set to max_seq_len / 2048.', value=shared.args.compress_pos_emb) shared.gradio['compress_pos_emb'] = gr.Slider(label='compress_pos_emb', minimum=1, maximum=8, step=1, info='Positional embeddings compression factor. Should typically be set to max_seq_len / 2048.', value=shared.args.compress_pos_emb)