---
title: LLaMA-Pro-8B-Instruct
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/LLaMA-Pro-8B-Instruct"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/LLaMA-Pro-8B-Instruct.md"
repository: AI-ModelScope/LLaMA-Pro-8B-Instruct
last_updated: 2024-03-08
license: llama2
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - llama
architectures:
  - LlamaForCausalLM
parameters: 8.4B
tensor_type:
  - BF16
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 4951
stars: 1
---

# LLaMA-Pro-8B-Instruct

> LLaMA-Pro-8B-Instruct - AI-ModelScope 在 ModelScope 开源的模型。LLaMA-PRO-Instruct Model Card

AI-ModelScope/LLaMA-Pro-8B-Instruct 是 ModelScope 魔搭社区上的 8.4B 参数text-generation模型，采用 llama2 许可，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: AI-ModelScope/LLaMA-Pro-8B-Instruct
- **License**: llama2
- **Tasks**: text-generation
- **Parameters**: 8.4B
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Downloads**: 4951
- **Stars**: 1
- **Last updated**: 2024-03-08

Source: https://www.modelscope.cn/models/AI-ModelScope/LLaMA-Pro-8B-Instruct

---

# LLaMA-PRO-Instruct Model Card

## Model Description
LLaMA-PRO-Instruct is a transformative expansion of the LLaMA2-7B model, now boasting 8.3 billion parameters. It uniquely specializes in programming, coding, and mathematical reasoning, maintaining versatility in general language tasks.

## Development and Training
This model, developed by Tencent ARC team, extends LLaMA2-7B using innovative block expansion techniques. It's meticulously trained on a diverse blend of coding and mathematical data, encompassing over 80 billion tokens.

## Intended Use
LLaMA-PRO-Instruct is ideal for complex NLP challenges, excelling in programming, mathematical reasoning, and general language processing, suitable for both specialized and broad applications.

## Performance
It surpasses its predecessors in the LLaMA series, especially in code domains, demonstrating exceptional competence as a comprehensive language model.

## Limitations
Despite advancements, it may encounter difficulties in highly niche or nuanced tasks.

## Ethical Considerations
Users are advised to consider inherent biases and responsibly manage its application across various fields.