i cant put the whole model becuase its too big

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i just made a fine tuned pytorch model, but its too big to be in github, what are the ways to put it in the project?

[AnuragDubey007]

In addition to Git LFS, many projects avoid committing large model files directly and instead:

• Host models on external storage (Hugging Face, cloud buckets)
• Download the model at runtime or via a setup script
• Version only metadata or configuration files in GitHub

This keeps repositories lightweight while still making the project reproducible.

[JOYBOY-3]

🚀 Options for Handling Large PyTorch Models

1. Use GitHub Releases or Git LFS

• GitHub Releases: Upload the model file as an asset in a release. Your code can then download it programmatically.
• Git LFS (Large File Storage): GitHub supports Git LFS for files >100MB. It stores large files outside the main repo but keeps version control.

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2. External Storage + Download Script

• Store the model in Google Drive, Dropbox, OneDrive, or AWS S3.
• Add a small Python script in your repo that downloads the model when needed.

  import requests
  
  url = "https://example.com/my_model.pt"
  r = requests.get(url)
  with open("model.pt", "wb") as f:
      f.write(r.content)

• This keeps your repo lightweight while ensuring reproducibility.

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3. Model Hubs

• Hugging Face Hub: Perfect for sharing ML models. Easy integration with transformers or torch.

  from huggingface_hub import hf_hub_download
  model_path = hf_hub_download("username/model-repo", "model.pt")

• PyTorch Hub: You can publish models for direct loading via torch.hub.load.

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4. Compression & Splitting

• Compress the model (.zip, .tar.gz) to reduce size.
• Split into chunks if needed (e.g., split -b 100M model.pt model.part).
• Provide a script to reassemble before use.

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5. Docker Images

• Package the model inside a Docker image. This way, anyone pulling the image gets the model pre-baked.

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6. Private Artifact Repositories

• Use tools like DVC (Data Version Control) or MLflow to manage large artifacts.
• These integrate well with GitHub and allow versioning of models without bloating the repo.

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✅ Recommended Approach

For open-source projects:
👉 Use Hugging Face Hub or GitHub Releases. They’re developer-friendly, widely adopted, and make collaboration easy.

For internal/enterprise projects:
👉 Use S3 + DVC/MLflow for robust artifact management.