---
title: chinese-llama-2-7b-16k
canonical_url: "https://www.modelscope.cn/models/ChineseAlpacaGroup/chinese-llama-2-7b-16k"
md_url: "https://www.modelscope.cn/models/ChineseAlpacaGroup/chinese-llama-2-7b-16k.md"
repository: ChineseAlpacaGroup/chinese-llama-2-7b-16k
last_updated: 2024-04-24
license: apache-2.0
pipeline_tag: fill-mask
tasks:
  - fill-mask
model_type:
  - llama
architectures:
  - LlamaForCausalLM
library_name:
  - pytorch
frameworks:
  - Pytorch
language:
  - zh
  - en
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 32
stars: 0
---

# chinese-llama-2-7b-16k

> chinese-llama-2-7b-16k - ChineseAlpacaGroup 在 ModelScope 开源的模型。Chinese-LLaMA-2-7B-16K

ChineseAlpacaGroup/chinese-llama-2-7b-16k 是 ModelScope 魔搭社区上的fill-mask模型，采用 apache-2.0 许可，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: ChineseAlpacaGroup/chinese-llama-2-7b-16k
- **License**: apache-2.0
- **Tasks**: fill-mask
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Downloads**: 32
- **Stars**: 0
- **Last updated**: 2024-04-24

Source: https://www.modelscope.cn/models/ChineseAlpacaGroup/chinese-llama-2-7b-16k

---

# Chinese-LLaMA-2-7B-16K

**This is the full Chinese-LLaMA-2-7B-16K (context size 16K)，model，which can be loaded directly for inference and full-parameter training.**

**Related models👇**
* Long context base models (16K)
  * [Chinese-LLaMA-2-7B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-7b-16k)
  * [Chinese-LLaMA-2-LoRA-7B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b-16k)
  * [Chinese-LLaMA-2-13B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-13b-16k)
  * [Chinese-LLaMA-2-LoRA-13B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b-16k)
* Long context Instruction/Chat models
  * [Chinese-Alpaca-2-7B-16K (full model)](https://huggingface.co/hfl/chinese-alpaca-2-7b-16k)
  * [Chinese-Alpaca-2-LoRA-7B-16K (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-7b-16k)
  * [Chinese-Alpaca-2-13B-16K (full model)](https://huggingface.co/hfl/chinese-alpaca-2-13b-16k)
  * [Chinese-Alpaca-2-LoRA-13B-16K (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-13b-16k)
* Base models
  * [Chinese-LLaMA-2-7B (full model)](https://huggingface.co/hfl/chinese-llama-2-7b)
  * [Chinese-LLaMA-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b)
  * [Chinese-LLaMA-2-13B (full model)](https://huggingface.co/hfl/chinese-llama-2-13b)
  * [Chinese-LLaMA-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b)
* Instruction/Chat models
  * [Chinese-Alpaca-2-7B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-7b)
  * [Chinese-Alpaca-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-7b)
  * [Chinese-Alpaca-2-13B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-13b)
  * [Chinese-Alpaca-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-13b)

# Description of Chinese-LLaMA-Alpaca-2 
This project is based on the Llama-2, released by Meta, and it is the second generation of the Chinese LLaMA & Alpaca LLM project. We open-source Chinese LLaMA-2 (foundation model) and Alpaca-2 (instruction-following model). These models have been expanded and optimized with Chinese vocabulary beyond the original Llama-2. We used large-scale Chinese data for incremental pre-training, which further improved the fundamental semantic understanding of the Chinese language, resulting in a significant performance improvement compared to the first-generation models. The relevant models support a 4K context and can be expanded up to 18K+ using the NTK method.

The main contents of this project include:

* 🚀 New extended Chinese vocabulary beyond Llama-2, open-sourcing the Chinese LLaMA-2 and Alpaca-2 LLMs.
* 🚀 Open-sourced the pre-training and instruction finetuning (SFT) scripts for further tuning on user's data
* 🚀 Quickly deploy and experience the quantized LLMs on CPU/GPU of personal PC
* 🚀 Support for LLaMA ecosystems like 🤗transformers, llama.cpp, text-generation-webui, LangChain, vLLM etc.

Please refer to [https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/) for details.