README.md

metadata

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
• Backbone Model: LLaMA-2
• Language(s): English
• Library: HuggingFace Transformers
• License: Fine-tuned checkpoints is licensed under the Non-Commercial Creative Commons license (CC 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
• Contact: For questions and comments about the model, please email contact@upstage.ai

Dataset Details

Used Datasets

• openbookqa
• sciq
• Open-Orca/OpenOrca
• metaeval/ScienceQA_text_only
• 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 and the HuggingFace trainer

Evaluation Results

Overview

• We conducted a performance evaluation based on the tasks being evaluated on the 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, specifically commit b281b0921b636bc36ad05c0b0b0763bd6dd43463.

Main Results

Model	Average	ARC	HellaSwag	MMLU	TruthfulQA
Llama-2-70b-instruct-1024 (Ours, Local Reproduction)	72.0	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

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