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Allow downloading single file from UI (#3737)

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missionfloyd 2023-08-29 20:32:36 -06:00 committed by GitHub
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3 changed files with 33 additions and 24 deletions
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2
README.md
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@ -164,8 +164,6 @@ text-generation-webui
│   ├── llama-13b.ggmlv3.q4_K_M.bin
```
Those models must be downloaded manually, as they are not currently supported by the automated downloader.
#### GPT-4chan
<details>

12
download-model.py
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@ -73,7 +73,7 @@ class ModelDownloader:
for i in range(len(dict)):
fname = dict[i]['path']
if specific_file is not None and fname != specific_file:
if specific_file not in [None, ''] and fname != specific_file:
continue
if not is_lora and fname.endswith(('adapter_config.json', 'adapter_model.bin')):
@ -175,16 +175,18 @@ class ModelDownloader:
f.write(data)
if total_size != 0 and self.progress_bar is not None:
count += len(data)
self.progress_bar(float(count) / float(total_size), f"Downloading {filename}")
self.progress_bar(float(count) / float(total_size), f"{filename}")
def start_download_threads(self, file_list, output_folder, start_from_scratch=False, threads=1):
thread_map(lambda url: self.get_single_file(url, output_folder, start_from_scratch=start_from_scratch), file_list, max_workers=threads, disable=True)
def download_model_files(self, model, branch, links, sha256, output_folder, progress_bar=None, start_from_scratch=False, threads=1, specific_file=None):
def download_model_files(self, model, branch, links, sha256, output_folder, progress_bar=None, start_from_scratch=False, threads=1, specific_file=None, is_llamacpp=False):
self.progress_bar = progress_bar
# Creating the folder and writing the metadata
# Create the folder and writing the metadata
output_folder.mkdir(parents=True, exist_ok=True)
if not is_llamacpp:
metadata = f'url: https://huggingface.co/{model}\n' \
f'branch: {branch}\n' \
f'download date: {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}\n'
@ -270,4 +272,4 @@ if __name__ == '__main__':
downloader.check_model_files(model, branch, links, sha256, output_folder)
else:
# Download files
downloader.download_model_files(model, branch, links, sha256, output_folder, specific_file=specific_file, threads=args.threads)
downloader.download_model_files(model, branch, links, sha256, output_folder, specific_file=specific_file, threads=args.threads, is_llamacpp=is_llamacpp)

27
modules/ui_model_menu.py
View file

@ -3,6 +3,7 @@ import math
import re
import traceback
from functools import partial
from pathlib import Path
import gradio as gr
import psutil
@ -119,14 +120,17 @@ def create_ui():
shared.gradio['gptq_for_llama_info'] = gr.Markdown('GPTQ-for-LLaMa support is currently only kept for compatibility with older GPUs. AutoGPTQ or ExLlama is preferred when compatible. GPTQ-for-LLaMa is installed by default with the webui on supported systems. Otherwise, it has to be installed manually following the instructions here: [instructions](https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md#installation-1).')
shared.gradio['exllama_info'] = gr.Markdown('For more information, consult the [docs](https://github.com/oobabooga/text-generation-webui/blob/main/docs/ExLlama.md).')
shared.gradio['exllama_HF_info'] = gr.Markdown('ExLlama_HF is a wrapper that lets you use ExLlama like a Transformers model, which means it can use the Transformers samplers. It\'s a bit slower than the regular ExLlama.')
shared.gradio['llamacpp_HF_info'] = gr.Markdown('llamacpp_HF is a wrapper that lets you use llama.cpp like a Transformers model, which means it can use the Transformers samplers. To use it, make sure to first download oobabooga/llama-tokenizer under "Download custom model or LoRA".')
shared.gradio['llamacpp_HF_info'] = gr.Markdown('llamacpp_HF is a wrapper that lets you use llama.cpp like a Transformers model, which means it can use the Transformers samplers. To use it, make sure to first download oobabooga/llama-tokenizer under "Download model or LoRA".')
with gr.Column():
with gr.Row():
shared.gradio['autoload_model'] = gr.Checkbox(value=shared.settings['autoload_model'], label='Autoload the model', info='Whether to load the model as soon as it is selected in the Model dropdown.')
shared.gradio['custom_model_menu'] = gr.Textbox(label="Download custom model or LoRA", info="Enter the Hugging Face username/model path, for instance: facebook/galactica-125m. To specify a branch, add it at the end after a \":\" character like this: facebook/galactica-125m:main")
shared.gradio['download_model_button'] = gr.Button("Download")
shared.gradio['custom_model_menu'] = gr.Textbox(label="Download model or LoRA", info="Enter the Hugging Face username/model path, for instance: facebook/galactica-125m. To specify a branch, add it at the end after a \":\" character like this: facebook/galactica-125m:main. To download a single file, enter its name in the second box.")
shared.gradio['download_specific_file'] = gr.Textbox(placeholder="File name (for GGUF/GGML)", show_label=False, max_lines=1)
with gr.Row():
shared.gradio['download_model_button'] = gr.Button("Download", variant='primary')
shared.gradio['get_file_list'] = gr.Button("Get file list")
with gr.Row():
shared.gradio['model_status'] = gr.Markdown('No model is loaded' if shared.model_name == 'None' else 'Ready')
@ -170,7 +174,8 @@ def create_event_handlers():
save_model_settings, gradio('model_menu', 'interface_state'), gradio('model_status'), show_progress=False)
shared.gradio['lora_menu_apply'].click(load_lora_wrapper, gradio('lora_menu'), gradio('model_status'), show_progress=False)
shared.gradio['download_model_button'].click(download_model_wrapper, gradio('custom_model_menu'), gradio('model_status'), show_progress=True)
shared.gradio['download_model_button'].click(download_model_wrapper, gradio('custom_model_menu', 'download_specific_file'), gradio('model_status'), show_progress=True)
shared.gradio['get_file_list'].click(partial(download_model_wrapper, return_links=True), gradio('custom_model_menu', 'download_specific_file'), gradio('model_status'), show_progress=True)
shared.gradio['autoload_model'].change(lambda x: gr.update(visible=not x), gradio('autoload_model'), gradio('load_model'))
@ -206,7 +211,7 @@ def load_lora_wrapper(selected_loras):
yield ("Successfuly applied the LoRAs")
def download_model_wrapper(repo_id, progress=gr.Progress()):
def download_model_wrapper(repo_id, specific_file, progress=gr.Progress(), return_links=False):
try:
downloader_module = importlib.import_module("download-model")
downloader = downloader_module.ModelDownloader()
@ -220,11 +225,15 @@ def download_model_wrapper(repo_id, progress=gr.Progress()):
model, branch = downloader.sanitize_model_and_branch_names(model, branch)
yield ("Getting the download links from Hugging Face")
links, sha256, is_lora = downloader.get_download_links_from_huggingface(model, branch, text_only=False)
links, sha256, is_lora, is_llamacpp = downloader.get_download_links_from_huggingface(model, branch, text_only=False, specific_file=specific_file)
if return_links:
yield '\n\n'.join([f"`{Path(link).name}`" for link in links])
return
yield ("Getting the output folder")
base_folder = shared.args.lora_dir if is_lora else shared.args.model_dir
output_folder = downloader.get_output_folder(model, branch, is_lora, base_folder=base_folder)
output_folder = downloader.get_output_folder(model, branch, is_lora, is_llamacpp=is_llamacpp, base_folder=base_folder)
if check:
progress(0.5)
@ -232,8 +241,8 @@ def download_model_wrapper(repo_id, progress=gr.Progress()):
downloader.check_model_files(model, branch, links, sha256, output_folder)
progress(1.0)
else:
yield (f"Downloading files to {output_folder}")
downloader.download_model_files(model, branch, links, sha256, output_folder, progress_bar=progress, threads=1)
yield (f"Downloading file{'s' if len(links) > 1 else ''} to `{output_folder}/`")
downloader.download_model_files(model, branch, links, sha256, output_folder, progress_bar=progress, threads=1, is_llamacpp=is_llamacpp)
yield ("Done!")
except:
progress(1.0)