# ValueError: You passed `quantization_config` with `bnb_4bit_compute_dtype=torch.float16` but the model weights are loaded in torch.float32. This may cause unexpected behavior.

- **ID:** `huggingface/quantization-config-bnb-compute-dtype-mismatch`
- **Domain:** huggingface
- **Category:** config_error
- **Verification:** ai_generated
- **Fix Rate:** 85%

## Root Cause

Mismatch between the compute dtype specified in quantization config and the actual dtype of the loaded model weights, causing precision inconsistencies.

## Version Compatibility

| Version | Status | Introduced | Deprecated |
|---------|--------|------------|------------|
| transformers>=4.36.0 | active | — | — |
| bitsandbytes>=0.41.0 | active | — | — |
| torch>=2.0.0 | active | — | — |

## Workarounds

1. **Set torch_dtype='auto' in from_pretrained to match the quantization config: model = AutoModel.from_pretrained('model', quantization_config=quant_config, torch_dtype='auto')** (90% success)
   ```
   Set torch_dtype='auto' in from_pretrained to match the quantization config: model = AutoModel.from_pretrained('model', quantization_config=quant_config, torch_dtype='auto')
   ```
2. **Explicitly set bnb_4bit_compute_dtype to match the model's dtype: quant_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.float32)** (85% success)
   ```
   Explicitly set bnb_4bit_compute_dtype to match the model's dtype: quant_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.float32)
   ```

## Dead Ends

- **** — If quantization config specifies float16 but model weights are float32, the error persists. (70% fail)
- **** — The mismatch causes incorrect computations, especially in mixed-precision training. (90% fail)