We opensource our **Qwen** series, now including **Qwen**, the base language models, namely **Qwen-7B** and **Qwen-14B**, as well as **Qwen-Chat**, the chat models, namely **Qwen-7B-Chat** and **Qwen-14B-Chat**. Links are on the above table. Click them and check the model cards.
In brief, we have strong base language models, which have been stably pretrained for up to 3 trillion tokens of multilingual data with a wide coverage of domains, languages (with a focus on Chinese and English), etc. They are able to achieve competitive performance on benchmark datasets. Additionally, we have chat models that are aligned with human preference based on SFT and RLHF (not released yet), which are able to chat, create content, extract information, summarize, translate, code, solve math problems, and so on, and are able to use tools, play as agents, or even play as code interpreters, etc.
* Quickstart with Qwen, and enjoy the simple inference.
* Details about the quantization models, including usage, memory, inference speed. For comparison, we also provide the statistics of the BF16 models.
* Tutorials on finetuning, including full-parameter tuning, LoRA, and Q-LoRA.
* Instructions on building demos, including WebUI, CLI demo, etc.
* Information about Qwen for tool use, agent, and code interpreter
* Statistics of long-context understanding evaluation
* License agreement
* ...
Also, if you meet problems, turn to [FAQ](FAQ.md) for help first. Still feeling struggled? Feel free to shoot us issues (better in English so that more people can understand you)! If you would like to help us, send us pull requests with no hesitation! We are always excited about PR!
Would like to chat with us or date us coffee time? Welcome to our Discord or WeChat!
* 2023.9.25 🔥 We release both **Qwen-14B** and **Qwen-14B-Chat** on ModelScope and Hugging Face. At the same time, we update **Qwen-7B** and **Qwen-7B-Chat**. Compared to **Qwen-7B** (original), **Qwen-7B** uses more training tokens, increasing from 2.2T tokens to 2.4T tokens, while the context length extends from 2048 to 8192. The Chinese knowledge and coding ability of **Qwen-7B** have been further improved. **PLEASE MAKE SURE YOU ARE USING THE LATEST CODES AND CHECKPOINTS!**
* 2023.8.21 We release the Int4 quantized model for Qwen-7B-Chat, **Qwen-7B-Chat-Int4**, which requires low memory costs but achieves improved inference speed. Besides, there is no significant performance degradation on the benchmark evaluation.
* 2023.8.3 We release both **Qwen-7B** and **Qwen-7B-Chat** on ModelScope and Hugging Face. We also provide a technical memo for more details about the model, including training details and model performance.
Qwen-14B and Qwen-7B (this is the new version trained with more tokens and the context length is extended from 2048 to 8192) outperform the baseline models of similar model sizes on a series of benchmark datasets, e.g., MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, etc., which evaluate the models' capabilities on natural language understanding, mathematic problem solving, coding, etc. However, even Qwen-14B still significantly fall behind GPT-3.5, let alone GPT-4. See the results below.
For all compared models, we report the best scores between their official reported results and [OpenCompass](https://opencompass.org.cn/leaderboard-llm).
For more experimental results (detailed model performance on more benchmark datasets) and details, please refer to our technical report by clicking [here](TODO).
Before running the code, make sure you have setup the environment and installed the required packages. Make sure you meet the above requirements, and then install the dependent libraries.
If your device supports fp16 or bf16, we recommend installing [flash-attention](https://github.com/Dao-AILab/flash-attention) for higher efficiency and lower memory usage. (**flash-attention is optional and the project can run normally without installing it**)
To use Qwen-Chat for the inference, all you need to do is to input a few lines of codes as demonstrated below. Remember to pass in the correct model names or paths, such as "Qwen/Qwen-7B-Chat" and "Qwen/Qwen-14B-Chat". However, **please make sure that you are using the latest code.**
ModelScope is an opensource platform for Model-as-a-Service (MaaS), which provides flexible and cost-effective model service to AI developers. Similarly, you can run the models with ModelScope as shown below:
We provide a solution based on [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ), and release an Int4 quantized model for Qwen-7B-Chat [Click here](https://huggingface.co/Qwen/Qwen-7B-Chat-Int4) and Qwen-14B-Chat [Click here](https://huggingface.co/Qwen/Qwen-14B-Chat-Int4), which achieve nearly lossless model effects but improved performance on both memory costs and inference speed.
Here we demonstrate how to use our provided quantized models for inference. Before you start, make sure you meet the requirements of auto-gptq (e.g., torch 2.0 and above, transformers 4.32.0 and above, etc.) and install the required packages:
We illustrate the model performance of both BF16 and Int4 models on the benchmark, and we find that the quantized model does not suffer from significant performance degradation. Results are shown below:
In detail, the setting of profiling is generating 8192 new tokens with 1 context token. The profiling runs on a single A100-SXM4-80G GPU with PyTorch 2.0.1 and CUDA 11.4. The inference speed is averaged over the generated 8192 tokens.
We also profile the peak GPU memory usage for encoding 2048 tokens as context (and generating single token) and generating 8192 tokens (with single token as context) under BF16 or Int4 quantization level, respectively. The results are shown below.
Now we provide the official training script, `finetune.py`, for users to finetune the pretrained model for downstream applications in a simple fashion. Additionally, we provide shell scripts to launch finetuning with no worries. This script supports the training with [DeepSpeed](https://github.com/microsoft/DeepSpeed) and [FSDP](https://engineering.fb.com/2021/07/15/open-source/fsdp/). The shell scripts that we provide use DeepSpeed, and thus we advise you to install DeepSpeed before you start.
To prepare your training data, you need to put all the samples into a list and save it to a json file. Each sample is a dictionary consisting of an id and a list for conversation. Below is a simple example list with 1 sample:
```json
[
{
"id": "identity_0",
"conversations": [
{
"from": "user",
"value": "你好",
},
{
"from": "assistant",
"value": "我是一个语言模型，我叫通义千问。"
}
]
}
]
```
After data preparation, you can use the provided shell scripts to run finetuning. Remember to specify the path to the data file, `$DATA`.
The finetuning scripts allow you to perform:
- Full-parameter finetuning
- LoRA
- Q-LoRA
Full-parameter parameter finetuning requires updating all parameters in the whole training process. To launch your training, run the following script:
```bash
# Distributed training. We do not provide single-GPU training script as the insufficient GPU memory will break down the training.
sh finetune/finetune_ds.sh
```
Remember to specify the correct model name or path, the data path, as well as the output directory in the shell scripts. Another thing to notice is that we use DeepSpeed ZeRO 3 in this script. If you want to make changes, just remove the argument `--deepspeed` or make changes in the DeepSpeed configuration json file based on your requirements. Additionally, this script supports mixed-precision training, and thus you can use `--bf16 True` or `--fp16 True`. Empirically we advise you to use bf16 to make your training consistent with our pretraining and alignment if your machine supports bf16, and thus we use it by default.
Similarly, to run LoRA, use another script to run as shown below. Before you start, make sure that you have installed `peft`. Also, you need to specify your paths to your model, data, and output. We advise you to use absolute path for your pretrained model. This is because LoRA only saves the adapter and the absolute path in the adapter configuration json file is used for finding out the pretrained model to load. Also, this script support both bf16 and fp16.
```bash
# Single GPU training
sh finetune/finetune_lora_single_gpu.sh
# Distributed training
sh finetune/finetune_lora_ds.sh
```
In comparison with full-parameter finetuning, LoRA ([paper](https://arxiv.org/abs/2106.09685)) only updates the parameters of adapter layers but keeps the original large language model layers frozen. This allows much fewer memory costs and thus fewer computation costs. However, if you still suffer from insufficient memory, you can consider Q-LoRA ([paper](https://arxiv.org/abs/2305.14314)), which uses the quantized large language model and other techniques such as paged attention to allow even fewer memory costs. To run Q-LoRA, directly run the following script:
```bash
# Single GPU training
sh finetune/finetune_qlora_single_gpu.sh
# Distributed training
sh finetune/finetune_qlora_ds.sh
```
For Q-LoRA, we advise you to load our provided quantized model, e.g., Qwen-7B-Chat-Int4. However, different from full-parameter finetuning and LoRA, only fp16 is supported for Q-LoRA.
Different from full-parameter finetuning, the training of both LoRA and Q-LoRA only saves the adapter parameters. Suppose your training starts from Qwen-7B, you can load the finetuned model for inference as shown below:
```python
from peft import AutoPeftModelForCausalLM
model = AutoPeftModelForCausalLM.from_pretrained(
path_to_adapter, # path to the output directory
device_map="auto",
trust_remote_code=True
).eval()
```
The shell scripts uses `torchrun` to run single-GPU or multi-GPU training. For multi-GPU training, you need to specify the proper hyperparameters for distributed training based on your machine.
We provide a CLI demo example in `cli_demo.py`, which supports streaming output for the generation. Users can interact with Qwen-7B-Chat by inputting prompts, and the model returns model outputs in the streaming mode. Run the command below:
You can change your arguments, e.g., `-c` for checkpoint name or path, `--cpu-only` for CPU deployment, etc. If you meet problems launching your API deployment, updating the packages to the latest version can probably solve them.
Using the API is also simple. See the example below:
Qwen-Chat has been optimized for tool usage and function calling capabilities. Users can develop agents, LangChain applications, and even agument Qwen with a Python Code Interpreter.
We provide documentation on how to implement tool calls based on the principle of ReAct Prompting, please refer to [the ReAct example](examples/react_prompt.md). Based on this principle, we provide support for function calling in [openai_api.py](openai_api.py).
We have tested the model's tool calling capabilities on our open-source Chinese evaluation benchmark and found that Qwen-Chat consistently performs well:
To assess Qwen's ability to use the Python Code Interpreter for tasks such as mathematical problem solving, data visualization, and other general-purpose tasks such as file handling and web scraping, we have created and open-sourced a benchmark specifically designed for evaluating these capabilities. You can find the benchmark at this [link](https://github.com/QwenLM/Qwen-Agent/tree/main/benchmark).
In addition, we also provide experimental results demonstrating that our model is capable of acting as a HuggingFace Agent. For more information, please refer to the [example documentation](examples/transformers_agent.md). The model's performance on the evaluation dataset provided by Hugging Face is as follows:
<table>
<tr>
<thcolspan="4"align="center">HuggingFace Agent Benchmark- Run Mode</th>
To extend the context length and break the bottleneck of training sequence length, we introduce several techniques, including NTK-aware interpolation, window attention, and LogN attention scaling, to extend the context length of Qwen-7B/14B from 2k to over 8K tokens, and Qwen-7B from 8k to 32k tokens. We conduct language modeling experiments on the arXiv dataset with the PPL evaluation and find that Qwen can reach outstanding performance in the scenario of long context. Results are demonstrated below:
Our tokenizer based on tiktoken is different from other tokenizers, e.g., sentencepiece tokenizer. You need to pay attention to special tokens, especially in finetuning. For more detailed information on the tokenizer and related use in fine-tuning, please refer to the [documentation](tokenization_note.md).
For your reproduction of the model performance on benchmark datasets, we provide scripts for you to reproduce the results. Check [eval/EVALUATION.md](eval/EVALUATION.md) for more information. Note that the reproduction may lead to slight differences from our reported results.
Researchers and developers are free to use the codes and model weights of both Qwen and Qwen-Chat. We also allow their commercial use. Check our license at [LICENSE](LICENSE) for more details. If you have requirements for commercial use, please fill out the [form](https://dashscope.console.aliyun.com/openModelApply/qianwen) to apply.
If you are interested to leave a message to either our research team or product team, join our Discord or WeChat groups! Also, feel free to send an email to qianwen_opensource@alibabacloud.com.
