Update README.md

README.md

@@ -196,15 +196,84 @@ Thank you to all my generous patrons and donaters!
 
 # Original model card: Upstage's Llama 30B Instruct 2048
 
-# LLaMa-30b-instruct-2048 model card
+## Model Details
 
-## Contact Us, Why Upstage LLM?
-- [Upstage](https://en.upstage.ai)'s LLM research has yielded remarkable results. Our 30B model size **outperforms all models worldwide**, establishing itself as the leading performer. Recognizing the immense potential for private LLM adoption within companies, we invite you to effortlessly implement a private LLM and fine-tune it with your own data. For a seamless and tailored solution, please don't hesitate to reach out to us [(click here to mail)].
+### Model Developers
+- [Upstage](https://en.upstage.ai)
+
+### Backbone Model
+- [LLaMA](https://github.com/facebookresearch/llama/tree/llama_v1)
+
+### Variations
+- It has different model parameter sizes and sequence lengths: [30B/1024](https://huggingface.co/upstage/llama-30b-instruct), [30B/2048](https://huggingface.co/upstage/llama-30b-instruct-2048), [65B/1024](https://huggingface.co/upstage/llama-65b-instruct).
 
-## Model and Dataset Details
-- Please refer to the model card of [upstage/llama-30b-instruct](https://huggingface.co/upstage/llama-30b-instruct) as this one is almost the same.
+### Input
+- Models solely process textual input.
 
-## License
+### Output
+- Models solely generate textual output.
+
+### License
 - This model is under a **Non-commercial** Bespoke License and governed by the Meta license. You should only use this repository if you have been granted access to the model by filling out [this form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform), but have either lost your copy of the weights or encountered issues converting them to the Transformers format.
 
+### 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-30b-instruct-2048/discussions).
+
+## 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)
+
+## Hardware and Software
+
+### Hardware
+- We utilized an A100 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-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 size **outperforms all models worldwide**, establishing itself as the leading performer. Recognizing the immense potential for private LLM adoption within companies, we invite you to effortlessly implement a private LLM and fine-tune it with your own data. For a seamless and tailored solution, please don't hesitate to reach out to us [(click here to mail)].
+
 [(click here to mail)]: mailto:contact@upstage.ai