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| 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 generate_importance_matrix(model_path, train_data_path): | |
| imatrix_command = f"./llama-imatrix -m ../{model_path} -f {train_data_path} -ngl 99 --output-frequency 10" | |
| os.chdir("llama.cpp") | |
| print(f"Current working directory: {os.getcwd()}") | |
| print(f"Files in the current directory: {os.listdir('.')}") | |
| if not os.path.isfile(f"../{model_path}"): | |
| raise Exception(f"Model file not found: {model_path}") | |
| print("Running imatrix command...") | |
| process = subprocess.Popen(imatrix_command, shell=True) | |
| try: | |
| process.wait(timeout=60) # added wait | |
| except subprocess.TimeoutExpired: | |
| print("Imatrix computation timed out. Sending SIGINT to allow graceful termination...") | |
| process.send_signal(signal.SIGINT) | |
| try: | |
| process.wait(timeout=5) # grace period | |
| except subprocess.TimeoutExpired: | |
| print("Imatrix proc still didn't term. Forecfully terming process...") | |
| process.kill() | |
| os.chdir("..") | |
| print("Importance matrix generation completed.") | |
| 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/llama-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, use_imatrix, imatrix_q_method, private_repo, train_data_file, 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}") | |
| imatrix_path = "llama.cpp/imatrix.dat" | |
| if use_imatrix: | |
| if train_data_file: | |
| train_data_path = train_data_file.name | |
| else: | |
| train_data_path = "groups_merged.txt" #fallback calibration dataset | |
| print(f"Training data file path: {train_data_path}") | |
| if not os.path.isfile(train_data_path): | |
| raise Exception(f"Training data file not found: {train_data_path}") | |
| generate_importance_matrix(fp16, train_data_path) | |
| else: | |
| print("Not using imatrix quantization.") | |
| 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 | |
| if use_imatrix: | |
| quantise_ggml = f"./llama.cpp/llama-quantize --imatrix {imatrix_path} {fp16} {quantized_gguf_path} {imatrix_q_method}" | |
| else: | |
| 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} | |
| Asalamu Alaikum! 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. | |
| ## Description (per [TheBloke](https://huggingface.co/TheBloke)) | |
| This repo contains GGUF format model files. | |
| These files were quantised using ggml-org/gguf-my-repo [https://huggingface.co/spaces/ggml-org/gguf-my-repo] | |
| <!-- description end --> | |
| <!-- README_GGUF.md-about-gguf start --> | |
| ### About GGUF (per [TheBloke](https://huggingface.co/TheBloke)) | |
| GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. | |
| Here is an incomplete list of clients and libraries that are known to support GGUF: | |
| * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. | |
| * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. | |
| * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. | |
| * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. | |
| * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. | |
| * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. | |
| * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. | |
| * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. | |
| * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. | |
| * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. | |
| <!-- README_GGUF.md-about-gguf end --> | |
| <!-- compatibility_gguf start --> | |
| ## Compatibility | |
| These quantised GGUFv2 files are compatible with llama.cpp from August 27th 2023 onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) | |
| They are also compatible with many third party UIs and libraries - please see the list at the top of this README. | |
| ## Explanation of quantisation methods | |
| <details> | |
| <summary>Click to see details</summary> | |
| The new methods available are: | |
| * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) | |
| * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. | |
| * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. | |
| * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw | |
| * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw | |
| Refer to the Provided Files table below to see what files use which methods, and how. | |
| </details> | |
| <!-- compatibility_gguf end --> | |
| <!-- README_GGUF.md-provided-files start --> | |
| ## Provided Files (Not Including iMatrix Quantization) | |
| | Quant method | Bits | Example Size | Max RAM required | Use case | | |
| | ---- | ---- | ---- | ---- | ----- | | |
| | Q2_K | 2 | 2.72 GB| 5.22 GB | significant quality loss - not recommended for most purposes | | |
| | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss | | |
| | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss | | |
| | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss | | |
| | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | |
| | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss | | |
| | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended | | |
| | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | |
| | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended | | |
| | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended | | |
| | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss | | |
| | Q8_0 | 8 | 7.70 GB| 10.20 GB | very large, extremely low quality loss - not recommended | | |
| **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. | |
| <!-- README_GGUF.md-provided-files end --> | |
| <!-- repositories-available start --> | |
| --- | |
| ## 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-cli --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. | |
| ``` | |
| ./llama-cli --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -p "The meaning to life and the universe is" | |
| ``` | |
| or | |
| ``` | |
| ./llama-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}") | |
| imatrix_path = "llama.cpp/imatrix.dat" | |
| if os.path.isfile(imatrix_path): | |
| try: | |
| print(f"Uploading imatrix.dat: {imatrix_path}") | |
| api.upload_file( | |
| path_or_fileobj=imatrix_path, | |
| path_in_repo="imatrix.dat", | |
| repo_id=new_repo_id, | |
| ) | |
| except Exception as e: | |
| raise Exception(f"Error uploading imatrix.dat: {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 <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!") | |
| 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 GGUF-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( | |
| ["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="Q8_0", | |
| filterable=False, | |
| visible=True | |
| ) | |
| imatrix_q_method = gr.Dropdown( | |
| ["IQ3_M", "IQ3_XXS", "Q4_K_M", "Q4_K_S", "IQ4_NL", "IQ4_XS", "Q5_K_M", "Q5_K_S"], | |
| label="Imatrix Quantization Method", | |
| info="GGML imatrix quants type", | |
| value="IQ4_NL", | |
| filterable=False, | |
| visible=False | |
| ) | |
| use_imatrix = gr.Checkbox( | |
| value=False, | |
| label="Use Imatrix Quantization", | |
| info="Use importance matrix for quantization." | |
| ) | |
| private_repo = gr.Checkbox( | |
| value=True, | |
| label="Private Repo", | |
| info="Create a private repo under your username." | |
| ) | |
| train_data_file = gr.File( | |
| label="Training Data File", | |
| file_types=["txt"], | |
| visible=False | |
| ) | |
| split_model = gr.Checkbox( | |
| value=False, | |
| label="Split Model", | |
| info="Shard the model using gguf-split." | |
| ) | |
| split_max_tensors = gr.Number( | |
| value=256, | |
| label="Max Tensors per File", | |
| info="Maximum number of tensors per file when splitting model.", | |
| visible=False | |
| ) | |
| split_max_size = 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 | |
| ) | |
| def update_visibility(use_imatrix): | |
| return gr.update(visible=not use_imatrix), gr.update(visible=use_imatrix), gr.update(visible=use_imatrix) | |
| use_imatrix.change( | |
| fn=update_visibility, | |
| inputs=use_imatrix, | |
| outputs=[q_method, imatrix_q_method, train_data_file] | |
| ) | |
| iface = gr.Interface( | |
| fn=process_model, | |
| inputs=[ | |
| model_id, | |
| q_method, | |
| use_imatrix, | |
| imatrix_q_method, | |
| private_repo, | |
| train_data_file, | |
| split_model, | |
| split_max_tensors, | |
| split_max_size, | |
| ], | |
| outputs=[ | |
| gr.Markdown(label="output"), | |
| gr.Image(show_label=False), | |
| ], | |
| title="Asalamu Alaikum! Create your own GGUF Quantizations, B̶L̶A̶Z̶I̶N̶G̶L̶Y̶ ̶F̶A̶S̶T̶ ⚡! (Hey it's free!)", | |
| description="The space takes a HuggingFace repo as an input, quantizes it and creates a private repo containing the selected quant under your HF user namespace.", | |
| api_name=False | |
| ) | |
| def update_split_visibility(split_model): | |
| return gr.update(visible=split_model), gr.update(visible=split_model) | |
| split_model.change( | |
| fn=update_split_visibility, | |
| inputs=split_model, | |
| outputs=[split_max_tensors, split_max_size] | |
| ) | |
| 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, show_api=False) |