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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
api = HfApi()
fs = HfFileSystem()
text = f"""
๐ฏ The Leaderboard aims to track TheBloke's quantized models.
## ๐ Type Of Models
- GPTQ
- GGUF
- AWQ
- GGML
## ๐ ๏ธ 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).
## ๐ Searching
You can search for author or a spesific model using the search bar.
## โ Last Update
This space is last updated in **{str(datetime.datetime.now().strftime("%Y-%m-%d %H:%M"))}**.
## ๐ 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({'๐ค Author Name': authors, '๐ค Model Name': models, '๐ GPTQ': gptq, '๐ฅ GGUF': gguf, '๐คทโโ๏ธ AWQ': awq, '๐ญ GGML': ggml})
def search(search_text):
if not search_text:
return df
if len(search_text.split('/'))>1:
return df[df['๐ค Model Name'] == clickable(search_text)]
else:
return df[df['๐ค 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="๐ 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)
demo.launch() |