LLaMa-65b-instruct model card

Model Details

• Developed by: Upstage
• Backbone Model: LLaMA
• Variations: It has different model parameter sizes and sequence lengths: 30B/1024, 30B/2048, 65B/1024
• Language(s): English
• Library: HuggingFace Transformers
• 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, 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
• Contact: For questions and comments about the model, please email [email protected]

Dataset Details

Used Datasets

• Orca-style dataset
• No other data was used except for the dataset mentioned above

Prompt Template

### System:
{System}

### User:
{User}

### Assistant:
{Assistant}

Usage

• Tested on A100 80GB
• Our model can handle up to 10k+ input tokens, thanks to the rope_scaling option

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

tokenizer = AutoTokenizer.from_pretrained("upstage/llama-65b-instruct")
model = AutoModelForCausalLM.from_pretrained(
    "upstage/llama-65b-instruct",
    device_map="auto",
    torch_dtype=torch.float16,
    load_in_8bit=True,
    rope_scaling={"type": "dynamic", "factor": 2} # allows handling of longer inputs
)

prompt = "### User:\nThomas is healthy, but he has to go to the hospital. 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)

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

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
• We used MT-bench, a set of challenging multi-turn open-ended questions, to evaluate the models

Main Results

Model	H4(Avg)	ARC	HellaSwag	MMLU	TruthfulQA		MT_Bench
Llama-2-70b-instruct-v2(Ours, Open LLM Leaderboard)	73	71.1	87.9	70.6	62.2		7.44063
Llama-2-70b-instruct (Ours, Open LLM Leaderboard)	72.3	70.9	87.5	69.8	61		7.24375
llama-65b-instruct (Ours, Open LLM Leaderboard)	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 (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 for H4 Score Reproduction

• 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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