|
--- |
|
datasets: |
|
- sciq |
|
- metaeval/ScienceQA_text_only |
|
- GAIR/lima |
|
- Open-Orca/OpenOrca |
|
- openbookqa |
|
language: |
|
- en |
|
tags: |
|
- upstage |
|
- llama |
|
- instruct |
|
- instruction |
|
pipeline_tag: text-generation |
|
--- |
|
# LLaMa-2-70b-instruct-1024 model card |
|
|
|
## Model Details |
|
|
|
* **Developed by**: [Upstage](https://en.upstage.ai) |
|
* **Backbone Model**: [LLaMA-2](https://github.com/facebookresearch/llama/tree/main) |
|
* **Language(s)**: English |
|
* **Library**: [HuggingFace Transformers](https://github.com/huggingface/transformers) |
|
* **License**: Fine-tuned checkpoints is licensed under the Non-Commercial Creative Commons license ([CC BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/)) |
|
* **Where to send comments**: Instructions on how to provide feedback or comments on a model can be found by opening an issue in the [Hugging Face community's model repository](https://huggingface.co/upstage/Llama-2-70b-instruct-1024/discussions) |
|
* **Contact**: For questions and comments about the model, please email `contact@upstage.ai` |
|
|
|
## Dataset Details |
|
|
|
### Used Datasets |
|
|
|
- [openbookqa](https://huggingface.co/datasets/openbookqa) |
|
- [sciq](https://huggingface.co/datasets/sciq) |
|
- [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) |
|
- [metaeval/ScienceQA_text_only](https://huggingface.co/datasets/metaeval/ScienceQA_text_only) |
|
- [GAIR/lima](https://huggingface.co/datasets/GAIR/lima) |
|
|
|
> No other data was used except for the dataset mentioned above |
|
|
|
### Prompt Template |
|
``` |
|
### System: |
|
{System} |
|
|
|
### User: |
|
{User} |
|
|
|
### Assistant: |
|
{Assistant} |
|
``` |
|
|
|
## Hardware and Software |
|
|
|
* **Hardware**: We utilized an A100x8 for training our model |
|
* **Training Factors**: We fine-tuned this model using a combination of the [DeepSpeed library](https://github.com/microsoft/DeepSpeed) and the [HuggingFace trainer](https://huggingface.co/docs/transformers/main_classes/trainer) |
|
|
|
## Evaluation Results |
|
|
|
### Overview |
|
- We conducted a performance evaluation based on the tasks being evaluated on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). |
|
We evaluated our model on four benchmark datasets, which include `ARC-Challenge`, `HellaSwag`, `MMLU`, and `TruthfulQA`. |
|
We used the [lm-evaluation-harness repository](https://github.com/EleutherAI/lm-evaluation-harness), specifically commit [b281b0921b636bc36ad05c0b0b0763bd6dd43463](https://github.com/EleutherAI/lm-evaluation-harness/tree/b281b0921b636bc36ad05c0b0b0763bd6dd43463). |
|
|
|
### Main Results |
|
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | |
|
|-----------------------------------------------|---------|-------|-----------|-------|------------| |
|
| Llama-2-70b-instruct-1024 (***Ours***, ***Local Reproduction***) | **72.0** | **70.7** | **87.4** | **69.3** | **60.7** | |
|
| llama-65b-instruct (***Ours***, ***Local Reproduction***) | 69.4 | 67.6 | 86.5 | 64.9 | 58.8 | |
|
| Llama-2-70b-hf | 67.3 | 67.3 | 87.3 | 69.8 | 44.9 | |
|
| llama-30b-instruct-2048 (***Ours***, ***Open LLM Leaderboard***) | 67.0 | 64.9 | 84.9 | 61.9 | 56.3 | |
|
| Llama-2-70b-chat-hf | 66.8 | 64.6 | 85.9 | 63.9 | 52.8 | |
|
| llama-30b-instruct (***Ours***, ***Open LLM Leaderboard***) | 65.2 | 62.5 | 86.2 | 59.4 | 52.8 | |
|
| falcon-40b-instruct | 63.4 | 61.6 | 84.3 | 55.4 | 52.5 | |
|
| llama-65b | 62.1 | 57.6 | 84.3 | 63.4 | 43.0 | |
|
|
|
### Scripts |
|
- Prepare evaluation environments: |
|
``` |
|
# clone the repository |
|
git clone https://github.com/EleutherAI/lm-evaluation-harness.git |
|
|
|
# check out the specific commit |
|
git checkout b281b0921b636bc36ad05c0b0b0763bd6dd43463 |
|
|
|
# change to the repository directory |
|
cd lm-evaluation-harness |
|
``` |
|
|
|
## Ethical Issues |
|
|
|
### Ethical Considerations |
|
- There were no ethical issues involved, as we did not include the benchmark test set or the training set in the model's training process. |
|
|
|
## Contact Us |
|
|
|
### Why Upstage LLM? |
|
- [Upstage](https://en.upstage.ai)'s LLM research has yielded remarkable results. Our 30B model **outperforms all models around the world**, positioning itself as the leading performer. Recognizing the immense potential in implementing private LLM to actual businesses, we invite you to easily apply private LLM and fine-tune it with your own data. For a seamless and tailored solution, please do not hesitate to reach out to us. ► [click here to contact]. |
|
|
|
[click here to contact]: mailto:contact@upstage.ai |
