|
import csv |
|
from datetime import datetime |
|
import os |
|
from typing import Optional |
|
import gradio as gr |
|
|
|
import quantize |
|
from huggingface_hub import HfApi, Repository |
|
|
|
|
|
DATASET_REPO_URL = "https://huggingface.co/datasets/safetensors/conversions" |
|
DATA_FILENAME = "data.csv" |
|
DATA_FILE = os.path.join("data", DATA_FILENAME) |
|
|
|
HF_TOKEN = os.environ.get("HF_TOKEN") |
|
|
|
repo: Optional[Repository] = None |
|
|
|
if False and HF_TOKEN: |
|
repo = Repository(local_dir="data", clone_from=DATASET_REPO_URL, token=HF_TOKEN) |
|
|
|
|
|
def run(model_id: str, is_private: bool, token: Optional[str] = None) -> str: |
|
if model_id == "": |
|
return """ |
|
### Invalid input 🐞 |
|
|
|
Please fill a token and model_id. |
|
""" |
|
try: |
|
if is_private: |
|
api = HfApi(token=token) |
|
else: |
|
api = HfApi(token=HF_TOKEN) |
|
hf_is_private = api.model_info(repo_id=model_id).private |
|
if is_private and not hf_is_private: |
|
|
|
|
|
api = HfApi(token=HF_TOKEN) |
|
|
|
print("is_private", is_private) |
|
|
|
commit_info, errors = quantize.quantize(api=api, model_id=model_id) |
|
print("[commit_info]", commit_info) |
|
|
|
|
|
string = f""" |
|
### Success 🔥 |
|
Yay! This model was successfully converted and a PR was open using your token, here: |
|
[{commit_info.pr_url}]({commit_info.pr_url}) |
|
""" |
|
if errors: |
|
string += "\nErrors during conversion:\n" |
|
string += "\n".join(f"Error while converting {filename}: {e}, skipped conversion" for filename, e in errors) |
|
return string |
|
except Exception as e: |
|
return f""" |
|
### Error 😢😢😢 |
|
|
|
{e} |
|
""" |
|
|
|
|
|
DESCRIPTION = """ |
|
The steps are the following: |
|
- Paste a read-access token from hf.co/settings/tokens. Read access is enough given that we will open a PR against the source repo. |
|
- Input a model id from the Hub |
|
- Click "Submit" |
|
- That's it! You'll get feedback if it works or not, and if it worked, you'll get the URL of the opened PR 🔥 |
|
⚠️ For now only `pytorch_model.bin` files are supported but we'll extend in the future. |
|
""" |
|
|
|
title="Quantize model and convert to CoreML" |
|
allow_flagging="never" |
|
|
|
def token_text(visible=False): |
|
return gr.Text(max_lines=1, label="your_hf_token", visible=True, value="") |
|
|
|
with gr.Blocks(title=title) as demo: |
|
description = gr.Markdown(f"""# {title}""") |
|
description = gr.Markdown(DESCRIPTION) |
|
|
|
with gr.Row() as r: |
|
with gr.Column() as c: |
|
model_id = gr.Text(max_lines=1, label="model_id", value="jblalock30/coreml") |
|
is_private = gr.Checkbox(label="Private model") |
|
token = token_text() |
|
with gr.Row() as c: |
|
clean = gr.ClearButton() |
|
submit = gr.Button("Submit", variant="primary") |
|
|
|
with gr.Column() as d: |
|
output = gr.Markdown(value="hi") |
|
|
|
is_private.change(lambda s: token_text(s), inputs=is_private, outputs=token) |
|
submit.click(run, inputs=[model_id, is_private, token], outputs=output, concurrency_limit=1) |
|
|
|
demo.queue(max_size=10).launch(show_api=True) |
