import os import shutil import subprocess import signal os.environ["GRADIO_ANALYTICS_ENABLED"] = "False" import gradio as gr from huggingface_hub import create_repo, HfApi from huggingface_hub import snapshot_download from huggingface_hub import whoami from huggingface_hub import ModelCard from gradio_huggingfacehub_search import HuggingfaceHubSearch from apscheduler.schedulers.background import BackgroundScheduler from textwrap import dedent HF_TOKEN = os.environ.get("HF_TOKEN") def process_model(model_id, q_method, private_repo, oauth_token: gr.OAuthToken | None): if oauth_token.token is None: raise ValueError("You must be logged in to use GGUF-my-repo") model_name = model_id.split('/')[-1] fp16 = f"{model_name}.fp16.gguf" try: api = HfApi(token=oauth_token.token) dl_pattern = ["*.md", "*.json", "*.model"] pattern = ( "*.safetensors" if any( file.path.endswith(".safetensors") for file in api.list_repo_tree( repo_id=model_id, recursive=True, ) ) else "*.bin" ) dl_pattern += pattern api.snapshot_download(repo_id=model_id, local_dir=model_name, local_dir_use_symlinks=False, allow_patterns=dl_pattern) print("Model downloaded successfully!") print(f"Current working directory: {os.getcwd()}") print(f"Model directory contents: {os.listdir(model_name)}") conversion_script = "convert_hf_to_gguf.py" fp16_conversion = f"python llama.cpp/{conversion_script} {model_name} --outtype f16 --outfile {fp16}" result = subprocess.run(fp16_conversion, shell=True, capture_output=True) print(result) if result.returncode != 0: raise Exception(f"Error converting to fp16: {result.stderr}") print("Model converted to fp16 successfully!") print(f"Converted model path: {fp16}") username = whoami(oauth_token.token)["name"] quantized_gguf_name = f"{model_name.lower()}-{imatrix_q_method.lower()}-imat.gguf" if use_imatrix else f"{model_name.lower()}-{q_method.lower()}.gguf" quantized_gguf_path = quantized_gguf_name quantise_ggml = f"./llama.cpp/llama-quantize {fp16} {quantized_gguf_path} {q_method}" result = subprocess.run(quantise_ggml, shell=True, capture_output=True) if result.returncode != 0: raise Exception(f"Error quantizing: {result.stderr}") print(f"Quantized successfully with {imatrix_q_method if use_imatrix else q_method} option!") print(f"Quantized model path: {quantized_gguf_path}") # Create empty repo new_repo_url = api.create_repo(repo_id=f"{username}/{model_name}-{imatrix_q_method if use_imatrix else q_method}-GGUF", exist_ok=True, private=private_repo) new_repo_id = new_repo_url.repo_id print("Repo created successfully!", new_repo_url) try: card = ModelCard.load(model_id, token=oauth_token.token) except: card = ModelCard("") if card.data.tags is None: card.data.tags = [] card.data.tags.append("llama-cpp") card.data.tags.append("gguf-my-repo") card.data.base_model = model_id card.text = dedent( f""" # {new_repo_id} """ ) card.save(f"README.md") try: print(f"Uploading quantized model: {quantized_gguf_path}") api.upload_file( path_or_fileobj=quantized_gguf_path, path_in_repo=quantized_gguf_name, repo_id=new_repo_id, ) except Exception as e: raise Exception(f"Error uploading quantized model: {e}") api.upload_file( path_or_fileobj=f"README.md", path_in_repo=f"README.md", repo_id=new_repo_id, ) print(f"Uploaded successfully with {imatrix_q_method if use_imatrix else q_method} option!") return ( f'Find your repo here', "llama.png", ) except Exception as e: return (f"Error: {e}", "error.png") finally: shutil.rmtree(model_name, ignore_errors=True) print("Folder cleaned up successfully!") css="""/* Custom CSS to allow scrolling */ .gradio-container {overflow-y: auto;} """ # Create Gradio interface with gr.Blocks(css=css) as demo: gr.Markdown("You must be logged in to use MLX-my-repo.") gr.LoginButton(min_width=250) model_id = HuggingfaceHubSearch( label="Hub Model ID", placeholder="Search for model id on Huggingface", search_type="model", ) q_method = gr.Dropdown( ["Q4", "Q8"], label="Quantization Method", info="MLX quantization type", value="Q4", filterable=False, visible=True ) private_repo = gr.Checkbox( value=False, label="Private Repo", info="Create a private repo under your username." ) iface = gr.Interface( fn=process_model, inputs=[ model_id, q_method, private_repo, ], outputs=[ gr.Markdown(label="output"), gr.Image(show_label=False), ], title="Create your own MLX Quants, blazingly fast ⚡!", description="The space takes an HF repo as an input, quantizes it and creates a Public/ Private repo containing the selected quant under your HF user namespace.", api_name=False ) def restart_space(): HfApi().restart_space(repo_id="reach-vb/mlx-my-repo", token=HF_TOKEN, factory_reboot=True) scheduler = BackgroundScheduler() scheduler.add_job(restart_space, "interval", seconds=21600) scheduler.start() # Launch the interface demo.queue(default_concurrency_limit=1, max_size=5).launch(debug=True, show_api=False)