license: apache-2.0
Fine-tuning on Intel Gaudi2
This model is a fine-tuned model based on Intel/neural-chat-7b-v3-1 on the meta-math/MetaMathQA dataset. And make a alignment using DPO method with Intel/orca_dpo_pairs. For more details about Intel/neural-chat-7b-v3-1, you can refer the model card and our blog The Practice of Supervised Fine-tuning and Direct Preference Optimization on Intel Gaudi2
Model date
Neural-chat-7b-v3-1 was trained at December, 2023.
Training sample code
Here is the sample code to reproduce the model: Sample Code.
Prompt Template
### System:
{system}
### User:
{usr}
### Assistant:
Ethical Considerations and Limitations
neural-chat-7b-v3-2 can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
Therefore, before deploying any applications of neural-chat-7b-v3-2, developers should perform safety testing.
Disclaimer
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes.
Organizations developing the model
The NeuralChat team with members from Intel/DCAI/AISE/AIPT. Core team members: Kaokao Lv, Liang Lv, Chang Wang, Wenxin Zhang, Xuhui Ren, and Haihao Shen.
Useful links
Open LLM Leaderboard Evaluation Results
Detailed results can be found here (note: the leaderboard removes drop task)
| Metric | Value |
|---|---|
| Avg. | 68.29 |
| ARC (25-shot) | 67.49 |
| HellaSwag (10-shot) | 83.92 |
| MMLU (5-shot) | 63.55 |
| TruthfulQA (0-shot) | 59.68 |
| Winogrande (5-shot) | 79.95 |
| GSM8K (5-shot) | 55.12 |