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* examples : add model conversion tool/example This commit adds an "example/tool" that is intended to help in the process of converting models to GGUF. Currently it supports normal causal models and embedding models. The readme contains instructions and command to guide through the process. The motivation for this to have a structured and repeatable process for model conversions and hopefully with time improve upon it to make the process easier and more reliable. We have started to use this for new model conversions internally and will continue doing so and improve it as we go along. Perhaps with time this should be placed in a different directory than the examples directory, but for now it seems like a good place to keep it while we are still developing it. * squash! examples : add model conversion tool/example Remove dependency on scikit-learn in model conversion example. * squash! examples : add model conversion tool/example Update transformer dep to use non-dev version. And also import `AutoModelForCausalLM` instead of `AutoModel` to ensure compatibility with the latest version. * squash! examples : add model conversion tool/example Remove the logits requirements file from the all requirements file.
58 lines
1.9 KiB
Python
Executable file
58 lines
1.9 KiB
Python
Executable file
#!/usr/bin/env python3
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from huggingface_hub import HfApi
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import argparse
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import os
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def upload_gguf_file(local_file_path, repo_id, filename_in_repo=None):
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"""
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Upload a GGUF file to a Hugging Face model repository
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Args:
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local_file_path: Path to your local GGUF file
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repo_id: Your repository ID (e.g., "username/model-name")
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filename_in_repo: Optional custom name for the file in the repo
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"""
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if not os.path.exists(local_file_path):
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print(f"❌ File not found: {local_file_path}")
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return False
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if filename_in_repo is None:
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filename_in_repo = os.path.basename(local_file_path)
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if filename_in_repo is None or filename_in_repo == "":
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filename_in_repo = os.path.basename(local_file_path)
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print(f"📤 Uploading {local_file_path} to {repo_id}/{filename_in_repo}")
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api = HfApi()
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try:
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api.upload_file(
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path_or_fileobj=local_file_path,
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path_in_repo=filename_in_repo,
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repo_id=repo_id,
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repo_type="model",
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commit_message=f"Upload {filename_in_repo}"
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)
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print("✅ Upload successful!")
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print(f"🔗 File available at: https://huggingface.co/{repo_id}/blob/main/{filename_in_repo}")
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return True
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except Exception as e:
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print(f"❌ Upload failed: {e}")
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return False
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# This script requires that the environment variable HF_TOKEN is set with your
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# Hugging Face API token.
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api = HfApi()
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parser = argparse.ArgumentParser(description='Upload a GGUF model to a Huggingface model repository')
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parser.add_argument('--gguf-model-path', '-m', help='The GGUF model file to upload', required=True)
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parser.add_argument('--repo-id', '-r', help='The repository to upload to', required=True)
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parser.add_argument('--name', '-o', help='The name in the model repository', required=False)
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args = parser.parse_args()
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upload_gguf_file(args.gguf_model_path, args.repo_id, args.name)
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