Intel's Neural Chat v3-3 8x7B Mixtral MOE
Original Model Details: Neural-Chat-v3-3
This model is a fine-tuned 7B parameter LLM on the Intel Gaudi 2 processor from the Intel/neural-chat-7b-v3-1 on the meta-math/MetaMathQA dataset. The model was aligned using the Direct Performance Optimization (DPO) method with Intel/orca_dpo_pairs. The Intel/neural-chat-7b-v3-1 was originally fine-tuned from mistralai/Mistral-7B-v-0.1. For more information, refer to our blog The Practice of Supervised Fine-tuning and Direct Preference Optimization on Intel Gaudi2.
Note: Adjust lora modules to trade off truthfulqa and gsm8k performance on DPO stage.
Model Detail | Description |
---|---|
Model Authors - Company | Intel. 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. |
Date | December, 2023 |
Version | v3-3 |
Type | 7B Large Language Model |
Paper or Other Resources | Medium Blog |
License | Apache 2.0 |
Questions or Comments | Community Tab and Intel Developers Discord |
Intended Use | Description |
---|---|
Primary intended uses | You can use the fine-tuned model for several language-related tasks. Checkout the LLM Leaderboard to see how this model and others from Intel are doing. |
Primary intended users | Anyone doing inference on language-related tasks. |
Out-of-scope uses | This model in most cases will need to be fine-tuned for your particular task. The model should not be used to intentionally create hostile or alienating environments for people. |
How to use and Sample Code
Here is the sample code to reproduce the model: Sample Code.
Prompt Template
### System:
{system}
### User:
{usr}
### Assistant:
Quantitative Analyses: Open LLM Leaderboard Evaluation Results
Detailed results can be found here (note: the leaderboard removed drop task)
Metric | Value |
---|---|
Avg. | 69.83 |
ARC (25-shot) | 66.89 |
HellaSwag (10-shot) | 85.26 |
MMLU (5-shot) | 63.07 |
TruthfulQA (0-shot) | 63.01 |
Winogrande (5-shot) | 79.64 |
GSM8K (5-shot) | 61.11 |
Useful links
Ethical Considerations and Limitations
neural-chat-7b-v3-3 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-v3-3, 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.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 71.17 |
AI2 Reasoning Challenge (25-Shot) | 66.64 |
HellaSwag (10-Shot) | 85.43 |
MMLU (5-Shot) | 62.22 |
TruthfulQA (0-shot) | 63.20 |
Winogrande (5-shot) | 79.72 |
GSM8k (5-shot) | 69.83 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard66.640
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard85.430
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard62.220
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard63.200
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard79.720
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard69.830