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| # name: Create HuggingFace Model Repository | |
| # on: | |
| # workflow_dispatch: | |
| # inputs: | |
| # model_name: | |
| # description: "Name of the model to create (will be used in repo name and files)" | |
| # required: true | |
| # type: string | |
| # model_variant: | |
| # description: "Model variant (e.g., 8b, 13b) - will be used in default field" | |
| # required: true | |
| # type: string | |
| # default: "8b" | |
| # prompt_template: | |
| # description: "Prompt template for the model" | |
| # required: true | |
| # type: string | |
| # default: | | |
| # <|im_start|>system | |
| # {system_message}<|im_end|> | |
| # <|im_start|>user | |
| # {prompt}<|im_end|> | |
| # <|im_start|>assistant | |
| # stop_tokens: | |
| # description: "Stop tokens for the model (comma-separated, e.g., <|im_end|>,</s>)" | |
| # required: true | |
| # type: string | |
| # default: "<|im_end|>" | |
| # engine: | |
| # description: "Engine to run the model (e.g., llama-cpp)" | |
| # required: true | |
| # type: string | |
| # default: "llama-cpp" | |
| # env: | |
| # USER_NAME: cortexso | |
| # MODEL_NAME: ${{ inputs.model_name }} | |
| # MODEL_VARIANT: ${{ inputs.model_variant }} | |
| # PROMPT_TEMPLATE: ${{ inputs.prompt_template }} | |
| # STOP_TOKENS: ${{ inputs.stop_tokens }} | |
| # ENGINE: ${{ inputs.engine }} | |
| # jobs: | |
| # create-repo: | |
| # runs-on: ubuntu-20-04-gguf | |
| # timeout-minutes: 7200 | |
| # steps: | |
| # - name: Checkout | |
| # uses: actions/checkout@v4 | |
| # - name: Set up Python | |
| # uses: actions/setup-python@v5 | |
| # with: | |
| # python-version: '3.10' | |
| # - name: Cache Python packages | |
| # uses: actions/cache@v4 | |
| # with: | |
| # path: | | |
| # ~/.cache/pip | |
| # ~/.local/share/pip | |
| # .venv | |
| # key: ${{ runner.os }}-pip-${{ github.sha }} | |
| # restore-keys: | | |
| # ${{ runner.os }}-pip- | |
| # - name: Install dependencies | |
| # run: | | |
| # pip install huggingface_hub PyYAML | |
| # git lfs install | |
| # - name: Create YAML files | |
| # run: | | |
| # python3 - << EOF | |
| # import yaml | |
| # import os | |
| # from yaml.representer import SafeRepresenter | |
| # class CustomDumper(yaml.SafeDumper): | |
| # pass | |
| # def custom_str_representer(dumper, data): | |
| # if isinstance(data, str) and '\n' in data: | |
| # return dumper.represent_scalar('tag:yaml.org,2002:str', data, style='|') | |
| # return SafeRepresenter.represent_str(dumper, data) | |
| # CustomDumper.add_representer(str, custom_str_representer) | |
| # def dict_representer(dumper, data): | |
| # return dumper.represent_dict(data.items()) | |
| # CustomDumper.add_representer(dict, dict_representer) | |
| # # Process stop tokens | |
| # stop_tokens = os.environ['STOP_TOKENS'].split(',') | |
| # stop_tokens = [token.strip() for token in stop_tokens if token.strip()] | |
| # # Create model.yml content with sections | |
| # def write_section(file, content, section_name='', subsection=''): | |
| # if section_name: | |
| # file.write(f"# BEGIN {section_name}\n") | |
| # if subsection: | |
| # file.write(f"# BEGIN {subsection}\n") | |
| # yaml_content = yaml.dump(content, Dumper=CustomDumper, default_flow_style=False, sort_keys=False) | |
| # file.write(yaml_content) | |
| # if subsection: | |
| # file.write(f"# END {subsection}\n") | |
| # if section_name: | |
| # file.write(f"# END {section_name}\n") | |
| # with open('model.yml', 'w') as f: | |
| # # General metadata section | |
| # general_metadata = { | |
| # 'id': os.environ['MODEL_NAME'], | |
| # 'model': os.environ['MODEL_NAME'], | |
| # 'name': os.environ['MODEL_NAME'], | |
| # 'version': 1 | |
| # } | |
| # write_section(f, general_metadata, 'GENERAL GGUF METADATA') | |
| # f.write('\n') | |
| # # Inference parameters section | |
| # f.write("# BEGIN INFERENCE PARAMETERS\n") | |
| # # Required subsection | |
| # required_inference = {'stop': stop_tokens} | |
| # write_section(f, required_inference, '', 'REQUIRED') | |
| # f.write('\n') | |
| # # Optional subsection | |
| # optional_inference = { | |
| # 'stream': True, | |
| # 'top_p': 0.9, | |
| # 'temperature': 0.7, | |
| # 'frequency_penalty': 0, | |
| # 'presence_penalty': 0, | |
| # 'max_tokens': 4096, | |
| # 'seed': -1, | |
| # 'dynatemp_range': 0, | |
| # 'dynatemp_exponent': 1, | |
| # 'top_k': 40, | |
| # 'min_p': 0.05, | |
| # 'tfs_z': 1, | |
| # 'typ_p': 1, | |
| # 'repeat_last_n': 64, | |
| # 'repeat_penalty': 1, | |
| # 'mirostat': False, | |
| # 'mirostat_tau': 5, | |
| # 'mirostat_eta': 0.100000001, | |
| # 'penalize_nl': False, | |
| # 'ignore_eos': False, | |
| # 'n_probs': 0, | |
| # 'min_keep': 0 | |
| # } | |
| # write_section(f, optional_inference, '', 'OPTIONAL') | |
| # f.write("# END INFERENCE PARAMETERS\n\n") | |
| # # Model load parameters section | |
| # f.write("# BEGIN MODEL LOAD PARAMETERS\n") | |
| # required_load = { | |
| # 'engine': os.environ['ENGINE'], | |
| # 'prompt_template': os.environ['PROMPT_TEMPLATE'], | |
| # 'ctx_len': 4096, | |
| # 'ngl': 34 | |
| # } | |
| # write_section(f, required_load, '', 'REQUIRED') | |
| # f.write("# END MODEL LOAD PARAMETERS\n") | |
| # # Create metadata.yml with dynamic default field | |
| # metadata_content = { | |
| # 'version': 1, | |
| # 'name': os.environ['MODEL_NAME'], | |
| # 'default': f"{os.environ['MODEL_VARIANT']}-gguf-q4-km" | |
| # } | |
| # with open('metadata.yml', 'w') as f: | |
| # write_section(f, metadata_content) | |
| # EOF | |
| # - name: Create HuggingFace Repository and Upload Files | |
| # env: | |
| # HF_TOKEN: ${{ secrets.HUGGINGFACE_TOKEN_WRITE }} | |
| # run: | | |
| # python3 - << EOF | |
| # from huggingface_hub import HfApi, create_repo | |
| # import os | |
| # # Initialize the Hugging Face API | |
| # api = HfApi(token=os.environ['HF_TOKEN']) | |
| # # Create the repository | |
| # repo_id = f"${{ env.USER_NAME }}/${{ env.MODEL_NAME }}" | |
| # try: | |
| # create_repo(repo_id, private=False, token=os.environ['HF_TOKEN']) | |
| # print(f"Created repository: {repo_id}") | |
| # except Exception as e: | |
| # print(f"Repository might already exist or error occurred: {e}") | |
| # # Upload the files | |
| # api.upload_file( | |
| # path_or_fileobj="model.yml", | |
| # path_in_repo="model.yml", | |
| # repo_id=repo_id, | |
| # token=os.environ['HF_TOKEN'] | |
| # ) | |
| # api.upload_file( | |
| # path_or_fileobj="metadata.yml", | |
| # path_in_repo="metadata.yml", | |
| # repo_id=repo_id, | |
| # token=os.environ['HF_TOKEN'] | |
| # ) | |
| # print("Files uploaded successfully") | |
| # EOF | |
| # - name: Cleanup | |
| # run: | | |
| # rm -f model.yml metadata.yml | |
| name: Create HuggingFace Model Repository | |
| on: | |
| push: | |
| branches: | |
| - chore/fix | |
| env: | |
| USER_NAME: cortexso | |
| MODEL_NAME: "test-model" # You'll need to set a default value | |
| MODEL_VARIANT: "8b" # Default model variant | |
| PROMPT_TEMPLATE: | | |
| <|im_start|>system | |
| {system_message}<|im_end|> | |
| <|im_start|>user | |
| {prompt}<|im_end|> | |
| <|im_start|>assistant | |
| STOP_TOKENS: "<|im_end|>" | |
| ENGINE: "llama-cpp" | |
| jobs: | |
| create-repo: | |
| runs-on: ubuntu-20-04-gguf | |
| timeout-minutes: 7200 | |
| steps: | |
| - name: Checkout | |
| uses: actions/checkout@v4 | |
| - name: Set up Python | |
| uses: actions/setup-python@v5 | |
| with: | |
| python-version: '3.10' | |
| - name: Cache Python packages | |
| uses: actions/cache@v4 | |
| with: | |
| path: | | |
| ~/.cache/pip | |
| ~/.local/share/pip | |
| .venv | |
| key: ${{ runner.os }}-pip-${{ github.sha }} | |
| restore-keys: | | |
| ${{ runner.os }}-pip- | |
| - name: Install dependencies | |
| run: | | |
| pip install huggingface_hub PyYAML | |
| git lfs install | |
| - name: Create YAML files | |
| run: | | |
| python3 - << EOF | |
| import yaml | |
| import os | |
| from yaml.representer import SafeRepresenter | |
| class CustomDumper(yaml.SafeDumper): | |
| pass | |
| def custom_str_representer(dumper, data): | |
| if isinstance(data, str) and '\n' in data: | |
| return dumper.represent_scalar('tag:yaml.org,2002:str', data, style='|') | |
| return SafeRepresenter.represent_str(dumper, data) | |
| CustomDumper.add_representer(str, custom_str_representer) | |
| def dict_representer(dumper, data): | |
| return dumper.represent_dict(data.items()) | |
| CustomDumper.add_representer(dict, dict_representer) | |
| # Process stop tokens | |
| stop_tokens = os.environ['STOP_TOKENS'].split(',') | |
| stop_tokens = [token.strip() for token in stop_tokens if token.strip()] | |
| # Create model.yml content with sections | |
| def write_section(file, content, section_name='', subsection=''): | |
| if section_name: | |
| file.write(f"# BEGIN {section_name}\n") | |
| if subsection: | |
| file.write(f"# BEGIN {subsection}\n") | |
| yaml_content = yaml.dump(content, Dumper=CustomDumper, default_flow_style=False, sort_keys=False) | |
| file.write(yaml_content) | |
| if subsection: | |
| file.write(f"# END {subsection}\n") | |
| if section_name: | |
| file.write(f"# END {section_name}\n") | |
| with open('model.yml', 'w') as f: | |
| # General metadata section | |
| general_metadata = { | |
| 'id': os.environ['MODEL_NAME'], | |
| 'model': os.environ['MODEL_NAME'], | |
| 'name': os.environ['MODEL_NAME'], | |
| 'version': 1 | |
| } | |
| write_section(f, general_metadata, 'GENERAL GGUF METADATA') | |
| f.write('\n') | |
| # Inference parameters section | |
| f.write("# BEGIN INFERENCE PARAMETERS\n") | |
| # Required subsection | |
| required_inference = {'stop': stop_tokens} | |
| write_section(f, required_inference, '', 'REQUIRED') | |
| f.write('\n') | |
| # Optional subsection | |
| optional_inference = { | |
| 'stream': True, | |
| 'top_p': 0.9, | |
| 'temperature': 0.7, | |
| 'frequency_penalty': 0, | |
| 'presence_penalty': 0, | |
| 'max_tokens': 4096, | |
| 'seed': -1, | |
| 'dynatemp_range': 0, | |
| 'dynatemp_exponent': 1, | |
| 'top_k': 40, | |
| 'min_p': 0.05, | |
| 'tfs_z': 1, | |
| 'typ_p': 1, | |
| 'repeat_last_n': 64, | |
| 'repeat_penalty': 1, | |
| 'mirostat': False, | |
| 'mirostat_tau': 5, | |
| 'mirostat_eta': 0.100000001, | |
| 'penalize_nl': False, | |
| 'ignore_eos': False, | |
| 'n_probs': 0, | |
| 'min_keep': 0 | |
| } | |
| write_section(f, optional_inference, '', 'OPTIONAL') | |
| f.write("# END INFERENCE PARAMETERS\n\n") | |
| # Model load parameters section | |
| f.write("# BEGIN MODEL LOAD PARAMETERS\n") | |
| required_load = { | |
| 'engine': os.environ['ENGINE'], | |
| 'prompt_template': os.environ['PROMPT_TEMPLATE'], | |
| 'ctx_len': 4096, | |
| 'ngl': 34 | |
| } | |
| write_section(f, required_load, '', 'REQUIRED') | |
| f.write("# END MODEL LOAD PARAMETERS\n") | |
| # Create metadata.yml with dynamic default field | |
| metadata_content = { | |
| 'version': 1, | |
| 'name': os.environ['MODEL_NAME'], | |
| 'default': f"{os.environ['MODEL_VARIANT']}-gguf-q4-km" | |
| } | |
| with open('metadata.yml', 'w') as f: | |
| write_section(f, metadata_content) | |
| EOF | |
| - name: Create HuggingFace Repository and Upload Files | |
| env: | |
| HF_TOKEN: ${{ secrets.HUGGINGFACE_TOKEN_WRITE }} | |
| run: | | |
| python3 - << EOF | |
| from huggingface_hub import HfApi, create_repo | |
| import os | |
| # Initialize the Hugging Face API | |
| api = HfApi(token=os.environ['HF_TOKEN']) | |
| # Create the repository | |
| repo_id = f"${{ env.USER_NAME }}/${{ env.MODEL_NAME }}" | |
| try: | |
| create_repo(repo_id, private=False, token=os.environ['HF_TOKEN']) | |
| print(f"Created repository: {repo_id}") | |
| except Exception as e: | |
| print(f"Repository might already exist or error occurred: {e}") | |
| # Upload the files | |
| api.upload_file( | |
| path_or_fileobj="model.yml", | |
| path_in_repo="model.yml", | |
| repo_id=repo_id, | |
| token=os.environ['HF_TOKEN'] | |
| ) | |
| api.upload_file( | |
| path_or_fileobj="metadata.yml", | |
| path_in_repo="metadata.yml", | |
| repo_id=repo_id, | |
| token=os.environ['HF_TOKEN'] | |
| ) | |
| print("Files uploaded successfully") | |
| EOF | |
| - name: Cleanup | |
| run: | | |
| rm -f model.yml metadata.yml |