To successfully load a Sentence Transformer model when downloads from Hugging Face are slow or failing, focus on optimizing the download process and ensuring reliable access to model files. Here’s a structured approach:
1. Use Direct Download or Mirror Sources
Hugging Face models are stored as Git repositories with Large File Storage (LFS), which can cause issues if Git LFS isn’t properly configured. Instead of relying on automated downloads via transformers or sentence-transformers, manually download the model files. Visit the model’s Hugging Face repository page (e.g., sentence-transformers/all-mpnet-base-v2), download the pytorch_model.bin, config.json, and other required files using your browser or tools like wget, and place them in your local cache directory (default: ~/.cache/huggingface/hub). You can also use a mirror like the HF Mirror to bypass network restrictions. For example, prefix your download URL with https://hf-mirror.com instead of https://huggingface.co.
2. Leverage Resumable Downloads and Retries
Network interruptions often cause failures. Use the transformers library’s built-in retry logic by setting environment variables like HF_HUB_ENABLE_HF_TRANSFER=1 to enable faster transfers or HTTP_PROXY/HTTPS_PROXY if behind a firewall. For command-line downloads, tools like wget -c (resume partial downloads) or aria2c (multi-threaded downloading) can improve reliability. If using Python, implement custom retry logic in your code with exponential backoff (e.g., using the tenacity library) when calling SentenceTransformer.from_pretrained().
3. Preload Models in Controlled Environments
If you’re working in a restricted environment (e.g., corporate network), pre-download the model on a machine with stable internet and transfer it locally. Use snapshot_download from the huggingface_hub library to fetch all files at once:
from huggingface_hub import snapshot_download
snapshot_download(repo_id="sentence-transformers/all-mpnet-base-v2", local_dir="./model")
Then load the model with SentenceTransformer("./model"). For CI/CD pipelines, pre-cache the model in a Docker image or use a shared network drive. If using cloud services, check if Hugging Face Hub integration (e.g., AWS S3 sync) is available.
Additional Tips:
- Disable safetensors conversion with
model = SentenceTransformer('model_name', use_safetensors=False)if you encounter file format issues. - Verify disk space and file permissions in the cache directory.
- Monitor network latency with tools like
mtrto diagnose connectivity problems tohuggingface.co(IP: 3.98.193.235).
By combining these strategies, you reduce dependency on unstable network conditions and gain full control over the model loading process.