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Qwen1.5

🤗 Hugging Face   |   🤖 ModelScope   |    📑 Blog    ｜   🖥️ Demo
WeChat (微信)   |   Discord   ｜   API

Visit our Hugging Face or ModelScope organization (click links above), search checkpoints with names starting with Qwen1.5-, and you will find all you need! Enjoy!

Introduction

This time, we upgrade Qwen to Qwen1.5, the beta version of Qwen1.5. Similar to Qwen, it is still a decoder-only transformer model with SwiGLU activation, RoPE, multi-head attention. At this moment, we have achieved:

  • 6 model sizes: 0.5B, 1.8B, 4B, 7B, 14B, and 72B;
  • Significant model quality improvements in chat models;
  • Strengthened multilingual capabilities in both base and chat models;
  • All models support the context length of 32768 tokens;
  • System prompts enabled for all models, which means roleplay is possible.
  • No need of trust_remote_code anymore.

We have not integrated GQA and mixture of SWA and full attention in this version and we will add the features in the future version.

News

  • 2024.02.05: We released the Qwen1.5 series.

Performance

Detailed evaluation results are reported in this 📑 blog.

Requirements

  • transformers>=4.37.0.

Warning

🚨 This is a must because `transformers` integrated Qwen1.5 codes in `4.37.0`.

Quickstart

🤗 Hugging Face Transformers

For a quickstart, I advise you to use chat models. We often use base models only for post-training. Here is a simple code snippet:

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

model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-7B-Chat", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-7B-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, do_sample=True)

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-7B-Chat-GPTQ, Qwen-beta-7B-Chat-AWQ.

🤖 ModelScope

We strongly advise users especially those in mainland China to use ModelScope. snapshot_download can help you solve issues concerning downloading checkpoints.

💻 Run locally

llama.cpp

Download our provided GGUF files or create them by yourself, and you can directly use them with the latest llama.cpp with a one-line command:

./main -m <path-to-file> -n 512 --color -i -cml -f prompts/chat-with-qwen.txt

Ollama

We are now on Ollama, and you can use pull and run to make things work.

ollama pull qwen1.5
ollama run qwen1.5

You can also add things like ::14B to choose different models. Visit ollama.ai for more information.

LMStudio

Qwen1.5 has already been supported by lmstudio.ai. You can directly use LMStudio with our GGUF files.

Web UI

Text generation web UI

You can directly use text-generation-webui for creating a web UI demo. If you use GGUF, remember to install the latest wheel of llama.cpp with the support of Qwen1.5.

llamafile

Clone llamafile, run source install, and then create your own llamafile with the GGUF file following the guide here. You are able to run one line of command, say ./qwen.llamafile, to create a demo.

Deployment

Now, Qwen1.5 is supported by multiple inference frameworks. Here we demonstrate the usage of vLLM and SGLang.

vLLM

We advise you to use vLLM>=0.3.0 to build OpenAI-compatible API service. Start the server with a chat model, e.g. Qwen1.5-7B-Chat:

python -m vllm.entrypoints.openai.api_server --model Qwen/Qwen1.5-7B-Chat

Then use the chat API as demonstrated below:

curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
    "model": "Qwen/Qwen1.5-7B-Chat",
    "messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Tell me something about large language models."}
    ]
    }'
from openai import OpenAI
# Set OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

chat_response = client.chat.completions.create(
    model="Qwen/Qwen1.5-7B-Chat",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Tell me something about large language models."},
    ]
)
print("Chat response:", chat_response)

SGLang

Please install SGLang from source. Similar to vLLM, you need to launch a server and use OpenAI-compatible API service. Start the server first:

python -m sglang.launch_server --model-path Qwen/Qwen1.5-7B-Chat --port 30000

You can use it in Python as shown below:

from sglang import function, system, user, assistant, gen, set_default_backend, RuntimeEndpoint

@function
def multi_turn_question(s, question_1, question_2):
    s += system("You are a helpful assistant.")
    s += user(question_1)
    s += assistant(gen("answer_1", max_tokens=256))
    s += user(question_2)
    s += assistant(gen("answer_2", max_tokens=256))

set_default_backend(RuntimeEndpoint("http://localhost:30000"))

state = multi_turn_question.run(
    question_1="What is the capital of China?",
    question_2="List two local attractions.",
)

for m in state.messages():
    print(m["role"], ":", m["content"])

print(state["answer_1"])

Finetuning

We provide a colab notebook for you to experience the simplest way of finetuning. However, we advise you to turn to more advanced training frameworks, including Axolotl, Llama-Factory, Swift, etc.

License Agreement

Check the license of each model inside its HF repo. It is NOT necessary for you to submit a request for commercial usage.

Citation

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

@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}
}

Contact Us

If you are interested to leave a message to either our research team or product team, join our Discord or WeChat groups!