tensorboard license:apache-2.0 model-index region:us
---
model-index:
- name: MetaMath-LoRA-LLaMA-7B
  results:
  - task:
      type: text-generation
    dataset:
      name: meta-math/MetaMathQA
      type: meta-math/MetaMathQA
    metrics:
    - name: Accuracy (zero-shot)
      type: Accuracy (zero-shot)
      value: 0.61
      verified: true
    source:
      name: Arithmetic Reasoning on GSM8K
      url: https://paperswithcode.com/sota/arithmetic-reasoning-on-gsm8k
license: apache-2.0
---

# Fine-tune LLaMA 2 (7B) with LoRA on meta-math/MetaMathQA
Fine-tune for one epoch

## Result:
**After the pre-training**:
Invalid output length: 8, Testing length: 1319, **Accuracy: 0.610**

**Reload the saved adapter**:
Invalid output length: 9, Testing length: 1319, **Accuracy: 0.609**

## Comparison
The official report **accuracy is 0.665** by fine-tuning the whole LLaMA 2 7B model for 3 epochs.

**Note**: The LoRA adapter is being used for future research purposes.

## Deployment
```python
# Load the Pre-trained LoRA Adapter
model.load_adapter("shuyuej/metamath_lora_llama2_7b")
model.enable_adapters()
```
