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| import os | |
| import shutil | |
| import subprocess | |
| 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 textwrap import dedent | |
| LLAMA_LIKE_ARCHS = ["MistralForCausalLM", "LlamaForCausalLM"] | |
| def script_to_use(model_id, api): | |
| info = api.model_info(model_id) | |
| if info.config is None: | |
| return None | |
| arch = info.config.get("architectures", None) | |
| if arch is None: | |
| return None | |
| arch = arch[0] | |
| return "convert.py" if arch in LLAMA_LIKE_ARCHS else "convert-hf-to-gguf.py" | |
| def process_model(model_id, q_method, hf_token, private_repo): | |
| model_name = model_id.split('/')[-1] | |
| fp16 = f"{model_name}/{model_name.lower()}.fp16.bin" | |
| try: | |
| api = HfApi(token=hf_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 | |
| snapshot_download(repo_id=model_id, local_dir=model_name, local_dir_use_symlinks=False, token=hf_token, allow_patterns=dl_pattern) | |
| print("Model downloaded successully!") | |
| conversion_script = script_to_use(model_id, api) | |
| 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 successully!") | |
| qtype = f"{model_name}/{model_name.lower()}.{q_method.upper()}.gguf" | |
| quantise_ggml = f"./llama.cpp/quantize {fp16} {qtype} {q_method}" | |
| result = subprocess.run(quantise_ggml, shell=True, capture_output=True) | |
| if result.returncode != 0: | |
| raise Exception(f"Error quantizing: {result.stderr}") | |
| print("Quantised successfully!") | |
| # Create empty repo | |
| new_repo_url = api.create_repo(repo_id=f"{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,) | |
| except: | |
| card = ModelCard("") | |
| card.data.tags = ["llama-cpp"] if card.data.tags is None else card.data.tags + ["llama-cpp"] | |
| card.data.tags += ["gguf-my-repo"] | |
| 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. | |
| ```bash | |
| brew install ggerganov/ggerganov/llama.cpp | |
| ``` | |
| Invoke the llama.cpp server or the CLI. | |
| CLI: | |
| ```bash | |
| llama-cli --hf-repo {new_repo_id} --model {qtype.split("/")[-1]} -p "The meaning to life and the universe is" | |
| ``` | |
| Server: | |
| ```bash | |
| llama-server --hf-repo {new_repo_id} --model {qtype.split("/")[-1]} -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. | |
| ``` | |
| git clone https://github.com/ggerganov/llama.cpp && \ | |
| cd llama.cpp && \ | |
| make && \ | |
| ./main -m {qtype.split("/")[-1]} -n 128 | |
| ``` | |
| """ | |
| ) | |
| card.save(os.path.join(model_name, "README-new.md")) | |
| api.upload_file( | |
| path_or_fileobj=qtype, | |
| path_in_repo=qtype.split("/")[-1], | |
| repo_id=new_repo_id, | |
| ) | |
| api.upload_file( | |
| path_or_fileobj=f"{model_name}/README-new.md", | |
| path_in_repo="README.md", | |
| repo_id=new_repo_id, | |
| ) | |
| print("Uploaded successfully!") | |
| return ( | |
| f'Find your repo <a href=\'{new_repo_url}\' target="_blank" style="text-decoration:underline">here</a>', | |
| "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 | |
| iface = gr.Interface( | |
| fn=process_model, | |
| inputs=[ | |
| gr.Textbox( | |
| lines=1, | |
| label="Hub Model ID", | |
| info="Model repo ID", | |
| ), | |
| 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 quantisation type", | |
| value="Q4_K_M", | |
| filterable=False | |
| ), | |
| gr.Textbox( | |
| lines=1, | |
| label="HF Write Token", | |
| info="https://hf.co/settings/token", | |
| type="password", | |
| ), | |
| gr.Checkbox( | |
| value=False, | |
| label="Private Repo", | |
| info="Create a private repo under your username." | |
| ) | |
| ], | |
| 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, quantises it and creates a Public repo containing the selected quant under your HF user namespace. You need to specify a write token obtained in https://hf.co/settings/tokens.", | |
| article="<p>Find your write token at <a href='https://huggingface.co/settings/tokens' target='_blank'>token settings</a></p>", | |
| ) | |
| # Launch the interface | |
| iface.launch(debug=True) | |