
# MOLAR 10.7B

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/63da3d7ae697e5898cb86854/qx9EYVNdVRD87bRqRqRlS.jpeg)

Q-LoRA fine-tuned on **kyujinpy/Sakura-SOLAR-Instruct** with **meta-math/MetaMathQA**

Adapter model and normal model is available, you can download with both styles.

You must use this prompt style.

```yaml
### Query: <query>
### Response: <response>
```

# Loss Curve:
![image/png](https://cdn-uploads.huggingface.co/production/uploads/63da3d7ae697e5898cb86854/5OW8k59b_3rlqWlZfYkWx.png)

# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results will be found [Here]()

| Metric                | Value                     |
|-----------------------|---------------------------|
| Avg.                  |                |
| ARC (25-shot)         |                |
| HellaSwag (10-shot)   |                |
| MMLU (5-shot)         |                |
| TruthfulQA (0-shot)   |                |
| Winogrande (5-shot)   |                |
| GSM8K (5-shot)        |                |
{"language": ["en"], "license": "apache-2.0", "library_name": "peft", "tags": ["SOLAR", "MetaMathQA", "llama"], "datasets": ["meta-math/MetaMathQA"], "base_model": "kyujinpy/Sakura-SOLAR-Instruct", "pipeline_tag": "text-generation"}
peft safetensors llama SOLAR MetaMathQA text-generation conversational en dataset:meta-math/MetaMathQA base_model:kyujinpy/Sakura-SOLAR-Instruct license:apache-2.0 region:us