README.md

---
language:
- en
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
- inferentia2
- neuron
extra_gated_heading: Access Llama 2 on Hugging Face
extra_gated_description: This is a form to enable access to Llama 2 on Hugging Face
  after you have been granted access from Meta. Please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads)
  and accept our license terms and acceptable use policy before submitting this form.
  Requests will be processed in 1-2 days.
extra_gated_prompt: '**Your Hugging Face account email address MUST match the email
  you provide on the Meta website, or your request will not be approved.**'
extra_gated_button_content: Submit
extra_gated_fields:
  ? I agree to share my name, email address and username with Meta and confirm that
    I have already been granted download access on the Meta website
  : checkbox
pipeline_tag: text-generation
inference: false
arxiv: 2307.09288
---
# Neuronx model for [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)

This repository contains are  [**AWS Inferentia2**](https://aws.amazon.com/ec2/instance-types/inf2/) and [`neuronx`](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/) compatible checkpoint for [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf). You can find detailed information about the base model on its [Model Card](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf).

## Usage on Amazon SageMaker

_coming soon_

## Usage with optimum-neuron

```python

from optimum.neuron import pipeline

# Load pipeline from Hugging Face repository
pipe = pipeline("text-generation", "meta-llama/Llama-2-7b-chat-hf")

# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
    {"role": "user", "content": "What is 2+2?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
# Run generation
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

```

## Compilation Arguments

**compilation arguments**

```json
{
  "num_cores": 2,
  "auto_cast_type": "fp16"
}
```

**input_shapes**

```json
{
  "sequence_length": 2048,
  "batch_size": 4
}
```