Create README.md

README.md

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+---
+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 `[email protected]`
+
+## 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***) | **72.02** | **70.73** | **87.41** | **69.27** | **60.68** |
+| llama-65b-instruct (***Ours***, ***Local Reproduction***) | 69.4 | 67.6 | 86.5 | 64.9 | 58.8 |
+| 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:[email protected]