# OSError: Error while deserializing header: HeaderTooLarge

- **ID:** `huggingface/cache-corruption-safetensors`
- **Domain:** huggingface
- **Category:** system_error
- **Verification:** ai_generated
- **Fix Rate:** 80%

## Root Cause

The safetensors file in the Hugging Face cache is corrupted, often due to incomplete download, disk write errors, or concurrent access by multiple processes.

## Version Compatibility

| Version | Status | Introduced | Deprecated |
|---------|--------|------------|------------|
| safetensors 0.4.0 | active | — | — |
| transformers 4.42.0 | active | — | — |
| huggingface-hub 0.23.0 | active | — | — |

## Workarounds

1. **Clear the Hugging Face cache for the specific model and re-download:

import shutil
from pathlib import Path
cache_dir = Path.home() / '.cache' / 'huggingface' / 'hub'
# Find and remove the corrupted model folder (e.g., models--bert-base-uncased)
shutil.rmtree(cache_dir / 'models--bert-base-uncased')
# Then reload the model
from transformers import AutoModel
model = AutoModel.from_pretrained('bert-base-uncased')** (85% success)
   ```
   Clear the Hugging Face cache for the specific model and re-download:

import shutil
from pathlib import Path
cache_dir = Path.home() / '.cache' / 'huggingface' / 'hub'
# Find and remove the corrupted model folder (e.g., models--bert-base-uncased)
shutil.rmtree(cache_dir / 'models--bert-base-uncased')
# Then reload the model
from transformers import AutoModel
model = AutoModel.from_pretrained('bert-base-uncased')
   ```
2. **Use `huggingface-cli` to delete the model from cache: `huggingface-cli delete-cache` and select the corrupted model, then re-download with `from_pretrained`.** (80% success)
   ```
   Use `huggingface-cli` to delete the model from cache: `huggingface-cli delete-cache` and select the corrupted model, then re-download with `from_pretrained`.
   ```

## Dead Ends

- **** — The file on disk is corrupted, not the library; reinstalling doesn't fix the cache. (95% fail)
- **** — The error is about a corrupted file header, not resource exhaustion. (90% fail)