| from huggingface_hub import HfApi, HfFileSystem | |
| import re | |
| from tqdm import tqdm | |
| import concurrent.futures | |
| import gradio as gr | |
| import datetime | |
| import pandas as pd | |
| import os | |
| import threading | |
| import time | |
| HF_TOKEN = os.getenv('HF_TOKEN') | |
| api = HfApi() | |
| fs = HfFileSystem() | |
| def restart_space(): | |
| time.sleep(36000) | |
| api.restart_space(repo_id="Weyaxi/thebloke-quantized-models", token=HF_TOKEN) | |
| text = f""" | |
| 🎯 The Leaderboard aims to track TheBloke's quantized models. | |
| ## 📚 Type Of Models | |
| - GPTQ | |
| - GGUF | |
| - AWQ | |
| - GGML | |
| ## 🛠️ Backend | |
| The leaderboard's backend mainly runs on the [Hugging Face Hub API](https://huggingface.co/docs/huggingface_hub/v0.5.1/en/package_reference/hf_api). | |
| ## 🔍 Searching | |
| You can search for author or a spesific model using the search bar. | |
| ## ⌛ Last Update | |
| This space is last updated in **{str(datetime.datetime.now().strftime("%Y-%m-%d %H:%M"))}**. | |
| ## 📒 Important Note | |
| This space potentially includes incorrectly quantized models for a model. | |
| If you find any incorrectly quantized model, please report it to me. | |
| """ | |
| quant_models = [i.__dict__['id'] for i in api.list_models(author="TheBloke") if "GPTQ" in i.__dict__['id'] or "GGUF" in i.__dict__['id'] or "AWQ" in i.__dict__['id'] or "GGML" in i.__dict__['id']] | |
| pattern = r'\(https://huggingface\.co/([^/]+)/([^/]+)\)' | |
| liste = {} | |
| def process_model(i, pattern, liste): | |
| text = fs.read_text(i + "/README.md") | |
| matches = re.search(pattern, text) | |
| if matches: | |
| author = matches.group(1) | |
| model_name = matches.group(2) | |
| full_id = (author + "/" + model_name).split(")")[0] | |
| try: | |
| liste[full_id].append(i) | |
| except KeyError: | |
| liste[full_id] = [i] | |
| num_threads = 64 | |
| with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor: | |
| futures = [] | |
| for i in quant_models: | |
| future = executor.submit(process_model, i, pattern, liste) | |
| futures.append(future) | |
| concurrent.futures.wait(futures) | |
| authors, models, gptq, gguf, awq, ggml = [], [], [], [], [], [] | |
| for model, values in liste.items(): | |
| models.append(model) | |
| gptq_value, gguf_value, awq_value, ggml_value = None, None, None, None | |
| for value in values: | |
| if "-GPTQ" in value: | |
| gptq_value = value | |
| elif "-GGUF" in value: | |
| gguf_value = value | |
| elif "-AWQ" in value: | |
| awq_value = value | |
| elif "-GGML" in value: | |
| ggml_value = value | |
| authors.append(model.split('/')[0]) | |
| gptq.append(gptq_value) | |
| gguf.append(gguf_value) | |
| awq.append(awq_value) | |
| ggml.append(ggml_value) | |
| df = pd.DataFrame({'👤 Author Name': authors, '🤖 Model Name': models, '👍 GPTQ': gptq, '📥 GGUF': gguf, '🤷♂️ AWQ': awq, '😭 GGML': ggml}) | |
| def search(search_text): | |
| if not search_text: | |
| return df | |
| if len(search_text.split('/'))>1: | |
| return df[df['🤖 Model Name'] == clickable(search_text)] | |
| else: | |
| return df[df['👤 Author Name'] == clickable(search_text)] | |
| def clickable(x): | |
| return None if not x else f'<a target="_blank" href="https://huggingface.co/{x}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{x}</a>' | |
| def to_clickable(df): | |
| for column in list(df.columns): | |
| df[column] = df[column].apply(lambda x: clickable(x)) | |
| return df | |
| with gr.Blocks() as demo: | |
| gr.Markdown("""<center><img src = "https://cdn-uploads.huggingface.co/production/uploads/6426d3f3a7723d62b53c259b/tvPikpAzKTKGN5wrpadOJ.jpeg" width=200 height=200></center>""") | |
| gr.Markdown("""<h1 align="center" id="space-title">The Bloke Quantized Models</h1>""") | |
| gr.Markdown(text) | |
| with gr.Column(min_width=320): | |
| search_bar = gr.Textbox(placeholder="🔍 Search for a author or a specific model", show_label=False) | |
| df_clickable = to_clickable(df) | |
| gr_df = gr.Dataframe(df_clickable, interactive=False, datatype=["markdown"]*len(df.columns)) | |
| search_bar.submit(fn=search, inputs=search_bar, outputs=gr_df) | |
| threading.Thread(target=restart_space).start() | |
| demo.launch() |