---
title: AquilaChat2-34B-16K
canonical_url: "https://www.modelscope.cn/models/BAAI/AquilaChat2-34B-16K"
md_url: "https://www.modelscope.cn/models/BAAI/AquilaChat2-34B-16K.md"
repository: BAAI/AquilaChat2-34B-16K
last_updated: 2026-07-14
license: other
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - aquila
architectures:
  - AquilaForCausalLM
library_name:
  - pytorch
  - transformer
frameworks:
  - pytorch
downloads: 2123
stars: 1
---

# AquilaChat2-34B-16K

> AquilaChat2-34B-16K - BAAI 在 ModelScope 开源的模型。We opensource our Aquila2 series, now including Aquila2, the base language models, namely Aquila2-7B and Aquila2-34B, as well as AquilaChat2, the chat models, namely AquilaChat2-7B and AquilaChat2-34B, as well as…

BAAI/AquilaChat2-34B-16K 是 ModelScope 魔搭社区上的text-generation模型，采用 other 许可。

- **Repository**: BAAI/AquilaChat2-34B-16K
- **License**: other
- **Tasks**: text-generation
- **Downloads**: 2123
- **Stars**: 1
- **Last updated**: 2026-07-14

Source: https://www.modelscope.cn/models/BAAI/AquilaChat2-34B-16K

---

![Aquila_logo](./log.jpeg)


<h4 align="center">
    <p>
        <b>English</b> |
        <a href="https://huggingface.co/BAAI/AquilaChat2-34B-16K/blob/main/README_zh.md">简体中文</a> 
    </p>
</h4>


<p align="center">
  <a href="https://github.com/FlagAI-Open/Aquila2" target="_blank">Github</a> • <a href="https://github.com/FlagAI-Open/Aquila2/blob/main/assets/wechat-qrcode.jpg" target="_blank">WeChat</a> <br>
</p>


We opensource our **Aquila2** series, now including **Aquila2**, the base language models, namely **Aquila2-7B** and **Aquila2-34B**, as well as **AquilaChat2**, the chat models, namely **AquilaChat2-7B** and **AquilaChat2-34B**, as well as the long-text chat models, namely **AquilaChat2-7B-16k** and **AquilaChat2-34B-16k**


2023.10.25 🔥 **AquilaChat2-34B-16K v1.2** is based on the previous **AquilaChat2-34B-16K**. The AquilaChat2-34B-16K-V1.2 has significantly improved long-text synthesis capabilities compared to the V1 version, 
approaching the level of GPT-3.5-16K. Additionally, the V1.2 version incorporates more conventional instruction fine-tuning corpora, enhancing its performance in non-long-text scenarios compared to the V1 version.

The additional details of the Aquila model will be presented in the official technical report. Please stay tuned for updates on official channels.


## Quick Start  AquilaChat2-34B-16K（Chat model）

### 1. Inference

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

device = torch.device("cuda:0")
model_info = "BAAI/AquilaChat2-34B-16k"
tokenizer = AutoTokenizer.from_pretrained(model_info, trust_remote_code=True)
quantization_config=BitsAndBytesConfig(
                        load_in_4bit=True,
                        bnb_4bit_use_double_quant=True,
                        bnb_4bit_quant_type="nf4",
                        bnb_4bit_compute_dtype=torch.bfloat16,
                    )
model = AutoModelForCausalLM.from_pretrained(model_info, trust_remote_code=True, torch_dtype=torch.bfloat16,
                                                # quantization_config=quantization_config, # Uncomment this line for 4bit quantization
                                                )
model.eval()
model.to(device)
text = "请给出10个要到北京旅游的理由。"
from predict import predict
out = predict(model, text, tokenizer=tokenizer, max_gen_len=200, top_p=0.9,
              seed=123, topk=15, temperature=1.0, sft=True, device=device,
              model_name="AquilaChat2-34B-16K")
print(out)
```


## License

Aquila2 series open-source model is licensed under [ BAAI Aquila Model Licence Agreement](https://huggingface.co/BAAI/AquilaChat2-34B-16K/blob/main/BAAI-Aquila-Model-License%20-Agreement.pdf)

## Citation
Feel free to cite the repo if you think Aquila2 is useful.

```python
@misc{zhang2024aquila2technicalreport,
      title={Aquila2 Technical Report}, 
      author={Bo-Wen Zhang and Liangdong Wang and Jijie Li and Shuhao Gu and Xinya Wu and Zhengduo Zhang and Boyan Gao and Yulong Ao and Guang Liu},
      year={2024},
      eprint={2408.07410},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2408.07410}, 
}
```


# Acknowledgements

This work is supported by the National Science and Technology Major Project (No. 2022ZD0116300).
本项目受新一代人工智能国家科技重大专项（No. 2022ZD0116300）支持。