Qwen1.5-1.8B-Chat

Introduction 介绍

Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:

Qwen1.5是Qwen2的beta版本，Qwen2是在大量数据上预训练的基于transformer的仅解码器的语言模型。与之前发布的Qwen相比，改进包括:

6 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, and 72B;
6种型号，包括0.5B、1.8B、4B、7B、14B、72B;
Significant performance improvement in human preference for chat models;
人类对聊天模型偏好的显著提高;
Multilingual support of both base and chat models;
基础模型和聊天模型的多语言支持;
Stable support of 32K context length for models of all sizes
稳定支持32K上下文长度的所有大小的模型
No need of trust_remote_code.不
需要 trust_remote_code 。

For more details, please refer to our blog post and GitHub repo.

更多细节，请参阅我们的博客文章和GitHub repo。

Model Details 模型的细节

Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA and the mixture of SWA and full attention.

Qwen1.5是一个语言模型系列，包括不同模型大小的解码器语言模型。对于每个大小，我们发布了基本语言模型和对齐的聊天模型。它基于具有SwiGLU激活、注意力QKV偏差、组查询注意力、滑动窗口注意力和全注意力混合等的Transformer架构。此外，我们有一个改进的分词器，可以适应多种自然语言和代码。对于beta版本，我们暂时不包含GQA以及SWA和full attention的混合。

Training details 培训细节

We pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization. However, DPO leads to improvements in human preference evaluation but degradation in benchmark evaluation. In the very near future, we will fix both problems.

我们用大量数据对模型进行了预训练，并用监督微调和直接偏好优化对模型进行了后训练。然而，DPO会导致人类偏好评价的改善，但基准评价的下降。在不久的将来，我们将解决这两个问题。

Requirements 需求

The code of Qwen1.5 has been in the latest Hugging face transformers and we advise you to install transformers>=4.37.0, or you might encounter the following error:

Qwen1.5的代码已经在最新的huggingface transformers中，我们建议您安装 transformers>=4.37.0 ，否则您可能会遇到以下错误:

KeyError: 'qwen2'

Quickstart 快速入门

Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.

这里提供了 apply_chat_template 的代码片段，向您展示如何加载标记赋予器和模型以及如何生成内容。

from modelscope import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained(
    "qwen/Qwen1.5-1.8B-Chat",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("qwen/Qwen1.5-1.8B-Chat")

prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

For quantized models, we advise you to use the GPTQ, AWQ, and GGUF correspondents, namely Qwen-beta-1_8B-Chat-GPTQ, Qwen-beta-1_8B-Chat-AWQ, and Qwen-beta-1_8B-Chat-GGUF.

对于量化模型，我们建议使用GPTQ、AWQ和GGUF模型，即 Qwen-beta-1_8B-Chat-GPTQ 、 Qwen-beta-1_8B-Chat-AWQ 、 Qwen-beta-1_8B-Chat-GGUF 。

Limitations 限制

Reduced capabilities in agent;
agent能力下降;
Codeswitch sometimes happen. We advise you to use our provided hyperparameters in generation_config.json.
代码切换有时会发生。我们建议您使用我们提供的超参数 generation_config.json 。

Citation 引用

If you find our work helpful, feel free to give us a cite.

如果你发现我们的工作有帮助，请随时给我们一个引用。

@article{qwen,
  title={Qwen Technical Report},
  author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu},
  journal={arXiv preprint arXiv:2309.16609},
  year={2023}
}

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