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116 lines
3.5 KiB
Python
116 lines
3.5 KiB
Python
## This script simply help pull off some info from the HF api
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## to speed up addition of new models to the gallery.
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## It accepts as input a repo_id and returns part of the YAML data
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## Use it as:
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## OPENAI_BASE_URL="<api_url>" OPENAI_MODEL="" python .github/add_model.py mradermacher/HaloMaidRP-v1.33-15B-L3-i1-GGUF
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## Example:
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# local-ai run hermes-2-theta-llama-3-8b
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# OPENAI_BASE_URL="http://192.168.xx.xx:8080" OPENAI_MODEL="hermes-2-theta-llama-3-8b" python scripts/model_gallery_info.py mradermacher/HaloMaidRP-v1.33-15B-L3-i1-GGUF
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import sys
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import os
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from openai import OpenAI
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from huggingface_hub import HfFileSystem, get_paths_info
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templated_yaml = """
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- !!merge <<: *llama3
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name: "{model_name}"
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urls:
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- https://huggingface.co/{repo_id}
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description: |
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{description}
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overrides:
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parameters:
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model: {file_name}
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files:
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- filename: {file_name}
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sha256: {checksum}
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uri: huggingface://{repo_id}/{file_name}
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"""
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client = OpenAI()
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model = os.environ.get("OPENAI_MODEL", "hermes-2-theta-llama-3-8b")
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def summarize(text: str) -> str:
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chat_completion = client.chat.completions.create(
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messages=[
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{
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"role": "user",
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"content": "You are a bot which extracts the description of the LLM model from the following text. Return ONLY the description of the model, and nothing else.\n" + text,
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},
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],
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model=model,
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)
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return chat_completion.choices[0].message.content
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def format_description(description):
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return '\n '.join(description.split('\n'))
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# Example usage
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if __name__ == "__main__":
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# Get repoid from argv[0]
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repo_id = sys.argv[1]
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token = "" # Replace with your Hugging Face token if needed
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fs = HfFileSystem()
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all_files = fs.ls(repo_id, detail=False)
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print(all_files)
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# Find a file that has Q4_K in the name
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file_path = None
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file_name = None
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readmeFile = None
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for file in all_files:
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print(f"File found: {file}")
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if "readme" in file.lower():
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readmeFile = file
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print(f"Found README file: {readmeFile}")
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if "q4_k_m" in file.lower():
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file_path = file
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if file_path is None:
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print("No file with Q4_K_M found, using the first file in the list.")
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exit(1)
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# Extract file from full path (is the last element)
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if file_path is not None:
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file_name = file_path.split("/")[-1]
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model_name = repo_id.split("/")[-1]
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checksum = None
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for file in get_paths_info(repo_id, [file_name], repo_type='model'):
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try:
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checksum = file.lfs.sha256
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break
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except Exception as e:
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print(f'Error from Hugging Face Hub: {str(e)}', file=sys.stderr)
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sys.exit(2)
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print(checksum)
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print(file_name)
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print(file_path)
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summarized_readme = ""
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if readmeFile:
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# If there is a README file, read it
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readme = fs.read_text(readmeFile)
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try:
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summarized_readme = summarize(readme)
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except Exception as e:
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print(f"Error summarizing the README: {str(e)}", file=sys.stderr)
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summarized_readme = format_description(summarized_readme)
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print("Model correctly processed")
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## Append to the result YAML file
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with open("result.yaml", "a") as f:
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f.write(templated_yaml.format(model_name=model_name.lower().replace("-GGUF","").replace("-gguf",""), repo_id=repo_id, description=summarized_readme, file_name=file_name, checksum=checksum, file_path=file_path))
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