import os import shutil import subprocess 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 split_upload_model(model_path, repo_id, oauth_token: gr.OAuthToken | None, split_max_tensors=256, split_max_size=None): if oauth_token.token is None: raise ValueError("You have to be logged in.") split_cmd = f"llama.cpp/gguf-split --split --split-max-tensors {split_max_tensors}" if split_max_size: split_cmd += f" --split-max-size {split_max_size}" split_cmd += f" {model_path} {model_path.split('.')[0]}" print(f"Split command: {split_cmd}") result = subprocess.run(split_cmd, shell=True, capture_output=True, text=True) print(f"Split command stdout: {result.stdout}") print(f"Split command stderr: {result.stderr}") if result.returncode != 0: raise Exception(f"Error splitting the model: {result.stderr}") print("Model split successfully!") sharded_model_files = [f for f in os.listdir('.') if f.startswith(model_path.split('.')[0])] if sharded_model_files: print(f"Sharded model files: {sharded_model_files}") api = HfApi(token=oauth_token.token) for file in sharded_model_files: file_path = os.path.join('.', file) print(f"Uploading file: {file_path}") try: api.upload_file( path_or_fileobj=file_path, path_in_repo=file, repo_id=repo_id, ) except Exception as e: raise Exception(f"Error uploading file {file_path}: {e}") else: raise Exception("No sharded files found.") print("Sharded model has been uploaded successfully!") def process_model(model_id, q_method, private_repo, split_model, split_max_tensors, split_max_size, 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()}-{q_method.lower()}.gguf" quantized_gguf_path = quantized_gguf_name quantise_ggml = f"./llama.cpp/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 {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}-{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} This model was converted to GGUF format from [`{model_id}`](https://huggingface.co/{model_id}) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/{model_id}) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./main --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -p "The meaning to life and the universe is" ``` or ``` ./server --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -c 2048 ``` """ ) card.save(f"README.md") if split_model: split_upload_model(quantized_gguf_path, new_repo_id, oauth_token, split_max_tensors, split_max_size) else: 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 {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!") # Create Gradio interface with gr.Blocks() as demo: gr.Markdown("You must be logged in to use GGUF-my-repo.") gr.LoginButton(min_width=250) model_id_input = HuggingfaceHubSearch( label="Hub Model ID", placeholder="Search for model id on Huggingface", search_type="model", ) q_method_input = gr.Dropdown( ["Q2_K", "Q3_K_S", "Q3_K_M", "Q3_K_L", "Q4_0", "Q4_K_S", "Q4_K_M", "Q5_0", "Q5_K_S", "Q5_K_M", "Q6_K", "Q8_0"], label="Quantization Method", info="GGML quantization type", value="Q4_K_M", filterable=False ) private_repo_input = gr.Checkbox( value=False, label="Private Repo", info="Create a private repo under your username." ) split_model_input = gr.Checkbox( value=False, label="Split Model", info="Shard the model using gguf-split." ) split_max_tensors_input = gr.Number( value=256, label="Max Tensors per File", info="Maximum number of tensors per file when splitting model.", visible=False ) split_max_size_input = gr.Textbox( label="Max File Size", info="Maximum file size when splitting model (--split-max-size). May leave empty to use the default.", visible=False ) iface = gr.Interface( fn=process_model, inputs=[ model_id_input, q_method_input, private_repo_input, split_model_input, split_max_tensors_input, split_max_size_input, ], outputs=[ gr.Markdown(label="output"), gr.Image(show_label=False), ], title="Create your own GGUF Quants, blazingly fast ⚡!", description="The space takes an HF repo as an input, quantizes it and creates a Public repo containing the selected quant under your HF user namespace.", ) def update_visibility(split_model): return gr.update(visible=split_model), gr.update(visible=split_model) split_model_input.change( fn=update_visibility, inputs=split_model_input, outputs=[split_max_tensors_input, split_max_size_input] ) def restart_space(): HfApi().restart_space(repo_id="ggml-org/gguf-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)