metadata

language:
  - en
tags:
  - upstage
  - llama-2
  - instruct
  - instruction
pipeline_tag: text-generation

LLaMa-2-70b-instruct-v2 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 [email protected]

Dataset Details

Used Datasets

Orca-style dataset
Alpaca-Style Dataset

Prompt Template

### System:
{System}
### User:
{User}
### Assistant:
{Assistant}

Usage

Tested on A100 80GB

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
tokenizer = AutoTokenizer.from_pretrained("upstage/Llama-2-70b-instruct-v2")
model = AutoModelForCausalLM.from_pretrained(
    "upstage/Llama-2-70b-instruct-v2",
    device_map='auto',
    torch_dtype=torch.float16,
    load_in_8bit=True,
    rope_scaling={'type': 'dynamic', 'factor': 2} # longer inputs possible
)
prompt = "### User:\nThomas is very healthy, but he has to go to the hospital every day. What could be the reasons?\n\n### Assistant:\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
del inputs['token_type_ids']
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
output = model.generate(**inputs, streamer=streamer, use_cache=True, max_new_tokens=float('inf'))
output_text = tokenizer.decode(output[0], skip_special_tokens=True)

Our model can handle >10k input tokens thanks to the rope_scaling option.

Hardware and Software

Hardware: We utilized an A100x8 * 4 for training our model
Training Factors: We fine-tuned this model using a combination of the DeepSpeed library and the HuggingFace trainer / HuggingFace Accelerate

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	H4 Average	ARC	HellaSwag	MMLU	TruthfulQA	MT_Bench
Llama-2-70b-instruct-v2 (Ours, Local Reproduction)	72.7	71.6	87.7	69.7	61.6	7.440625
Llama-2-70b-instruct (Ours, Local Reproduction)	72.0	70.7	87.4	69.3	60.7	7.24375
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-30b-instruct-2048 (Ours, Local Reproduction)	67.0	64.9	85.0	61.9	56.0	6.88125
llama-30b-instruct (Ours, Open LLM Leaderboard)	65.2	62.5	86.2	59.4	52.8
llama-65b	64.2	63.5	86.1	63.9	43.4
falcon-40b-instruct	63.4	61.6	84.3	55.4	52.5

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.

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