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Add ctransformers support (#3313)

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Co-authored-by: cal066 <cal066@users.noreply.github.com>
Co-authored-by: oobabooga <112222186+oobabooga@users.noreply.github.com>
Co-authored-by: randoentity <137087500+randoentity@users.noreply.github.com>
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cal066 2023-08-11 17:41:33 +00:00 • committed by GitHub
parent 8dbaa20ca8
commit 7a4fcee069
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9 changed files with 188 additions and 43 deletions
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8
modules/ui_model_menu.py
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@ -63,7 +63,7 @@ def create_ui():
with gr.Row():
with gr.Column():
shared.gradio['loader'] = gr.Dropdown(label="Model loader", choices=["Transformers", "ExLlama_HF", "ExLlama", "AutoGPTQ", "GPTQ-for-LLaMa", "llama.cpp", "llamacpp_HF"], value=None)
shared.gradio['loader'] = gr.Dropdown(label="Model loader", choices=loaders.loaders_and_params.keys(), value=None)
with gr.Box():
with gr.Row():
with gr.Column():
@ -84,7 +84,7 @@ def create_ui():
shared.gradio['wbits'] = gr.Dropdown(label="wbits", choices=["None", 1, 2, 3, 4, 8], value=str(shared.args.wbits) if shared.args.wbits > 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"], 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['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')
@ -127,7 +127,9 @@ def create_ui():
def create_event_handlers():
shared.gradio['loader'].change(loaders.make_loader_params_visible, gradio('loader'), gradio(loaders.get_all_params()))
shared.gradio['loader'].change(
loaders.make_loader_params_visible, gradio('loader'), gradio(loaders.get_all_params())).then(
lambda value: gr.update(choices=loaders.get_model_types(value)), gradio('loader'), gradio('model_type'))
# In this event handler, the interface state is read and updated
# with the model defaults (if any), and then the model is loaded