Hf_hub_~repack~ Download Timeout ★ Trending
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For very large repositories, Hugging Face has migrated to high-performance backends like . This backend is designed for better throughput and concurrency. If your downloads are stalling, ensure Xet is properly configured or try disabling it if it misbehaves in your specific environment.
Massive models (70B+ parameters) require maintaining stable connections for extended periods, making them susceptible to silent TCP drops or server-side resets.
Corporate networks or firewalls can sometimes terminate idle connections or block the CDN endpoints used by Hugging Face. Key Solutions to Fix Timeouts 1. Increase Timeout Environment Variables hf_hub_download timeout
Encountering a hf_hub_download timeout is a common hurdle when working with large machine learning models, especially on unstable or slow internet connections. This error typically triggers when the client fails to receive metadata or file chunks from the Hugging Face Hub within the library's default time limit. Understanding the Causes
High-traffic environments, such as multi-worker training jobs, may cause workers to compete for bandwidth, leading to individual connection failures. For very large repositories, Hugging Face has migrated
# In your terminal export HF_HUB_DOWNLOAD_TIMEOUT=60 export HF_HUB_ETAG_TIMEOUT=60 Use code with caution. Or within a Python script:
export HF_XET_NUM_CONCURRENT_RANGE_GETS=32 . Disable if needed: export HF_HUB_DISABLE_XET=1 . 3. Leverage the Hugging Face CLI GitHubhttps://github.com such as multi-worker training jobs
Several factors can lead to a timeout during the download process: