---
language:
- en
tags:
- upstage
- llama-2
- instruct
- instruction
pipeline_tag: text-generation
---
# LLaMa-2-70b-instruct-v2 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-v2/discussions)
* **Contact**: For questions and comments about the model, please email [contact@upstage.ai](mailto:contact@upstage.ai)

## Dataset Details

### Used Datasets
- Orca-style dataset
- Alpaca-Style Dataset


### Prompt Template
```
### System:
{System}
### User:
{User}
### Assistant:
{Assistant}
```
### Usage

*Tested on A100 80GB*

```python
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](https://github.com/microsoft/DeepSpeed) and the [HuggingFace trainer](https://huggingface.co/docs/transformers/main_classes/trainer) / [HuggingFace Accelerate](https://huggingface.co/docs/accelerate/index)

## 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                                         | 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.

## 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](https://www.upstage.ai/private-llm?utm_source=huggingface&utm_medium=link&utm_campaign=privatellm).