File size: 2,111 Bytes
c0f13e0 42ccc37 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
---
datasets:
- stingning/ultrachat
---
# UltraLM-65b
<!-- Provide a quick summary of what the model is/does. -->
This is UltraLM-65b delta weights, a chat language model trained upon [UltraChat](https://github.com/thunlp/UltraChat)
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
The model is fine-tuned based on LLaMA-65b with a multi-turn chat-format template as below
```
User: instruction 1
Assistant: response 1<eos_token>
User: instruction 2
Assistant: response 2<eos_token>
...
```
- **License:** UltraLM is based on LLaMA and should be used under LLaMA's [model license](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md).
- **Finetuned from model:** LLaMA-65b
- **Finetuned on data:** [UltraChat](https://github.com/thunlp/UltraChat)
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** [UltraChat](https://github.com/thunlp/UltraChat)
- **Paper:** [arxiv](https://arxiv.org/abs/2305.14233)
- **Demo:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
To use this model, you need to [recover](https://github.com/thunlp/UltraChat/tree/main/UltraLM) the full model from the delta weights and perform inference following the template below:
```
[Optional]User: system prompt
User: user input
Assistant:
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_openbmb__UltraLM-65b)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 58.99 |
| ARC (25-shot) | 67.06 |
| HellaSwag (10-shot) | 84.98 |
| MMLU (5-shot) | 63.48 |
| TruthfulQA (0-shot) | 53.51 |
| Winogrande (5-shot) | 81.14 |
| GSM8K (5-shot) | 32.75 |
| DROP (3-shot) | 30.0 |
|