mannix / qwen2-7b

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

model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2-7B-Instruct",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-7B-Instruct")

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]

处理长文本

为了处理超过32,768个令牌的大量输入，我们利用了YARN技术，这是一种增强模型长度外推的技术，确保在处理长文本时性能最优。

对于部署，我们推荐使用vLLM。您可以通过以下步骤启用长上下文功能

安装vLLM：您可以通过运行以下命令来安装vLLM。

pip install "vllm>=0.4.3"

或者您也可以从源代码安装vLLM。

配置模型设置：下载模型权重后，通过包括以下片段来修改config.json文件

    {
        "architectures": [
            "Qwen2ForCausalLM"
        ],
        // ...
        "vocab_size": 152064,


        // adding the following snippets
        "rope_scaling": {
            "factor": 4.0,
            "original_max_position_embeddings": 32768,
            "type": "yarn"
        }
    }

此片段启用YARN来支持更长的上下文。

模型部署：使用vLLM来部署您的模型。例如，您可以通过以下命令设置类似openAI的服务器

python -m vllm.entrypoints.openai.api_server --served-model-name Qwen2-7B-Instruct --model path/to/weights

然后您可以通过以下方式访问Chat API

curl https://127.0.0.1:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
    "model": "Qwen2-7B-Instruct",
    "messages": [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Your Long Input Here."}
    ]
    }'

有关vLLM的进一步使用说明，请参阅我们的Github。

注意：目前，vLLM仅支持静态YARN，这意味着刻度因子不受输入长度的限制，可能会影响短文本的性能。我们建议仅在需要处理长上下文时添加rope_scaling配置。

评估

我们简要比较了Qwen2-7B-Instruct与相似规模的指令调优LLM，包括Qwen1.5-7B-Chat。结果如下所示

数据集	Llama-3-8B-Instruct	Yi-1.5-9B-Chat	GLM-4-9B-Chat	Qwen1.5-7B-Chat	Qwen2-7B-Instruct
英语
MMLU	68.4	69.5	72.4	59.5	70.5
MMLU-Pro	41.0	-	-	29.1	44.1
GPQA	34.2	-	-	27.8	25.3
TheroemQA	23.0	-	-	14.1	25.3
MT-Bench	8.05	8.20	8.35	7.60	8.41
编码
Humaneval	62.2	66.5	71.8	46.3	79.9
MBPP	67.9	-	-	48.9	67.2
MultiPL-E	48.5	-	-	27.2	59.1
Evalplus	60.9	-	-	44.8	70.3
LiveCodeBench	17.3	-	-	6.0	26.6
数学
GSM8K	79.6	84.8	79.6	60.3	82.3
MATH	30.0	47.7	50.6	23.2	49.6
中文
C-Eval	45.9	-	75.6	67.3	77.2
AlignBench	6.20	6.90	7.01	6.20	7.21

引用

如果您觉得我们的工作有所帮助，请随意引用我们。

@article{qwen2,
  title={Qwen2 Technical Report},
  year={2024}
}

# Qwen2-7B-Instruct

## Introduction

Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the instruction-tuned 7B Qwen2 model.

Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc.

Qwen2-7B-Instruct supports a context length of up to 131,072 tokens, enabling the processing of extensive inputs. Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2 for handling long texts.

For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2/), [GitHub](https://github.com/QwenLM/Qwen2), and [Documentation](https://qwen.readthedocs.io/en/latest/).
<br>

## Model Details
Qwen2 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, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes.

## Training details
We pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization.

## Requirements
The code of Qwen2 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:
```
KeyError: 'qwen2'
```

## Quickstart

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

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

model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2-7B-Instruct",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-7B-Instruct")

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]
```

### Processing Long Texts

To handle extensive inputs exceeding 32,768 tokens, we utilize [YARN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.

For deployment, we recommend using vLLM. You can enable the long-context capabilities by following these steps:

1. **Install vLLM**: You can install vLLM by running the following command.

```bash
pip install "vllm>=0.4.3"
```

Or you can install vLLM from [source](https://github.com/vllm-project/vllm/).

2. **Configure Model Settings**: After downloading the model weights, modify the `config.json` file by including the below snippet:
    ```json
        {
            "architectures": [
                "Qwen2ForCausalLM"
            ],
            // ...
            "vocab_size": 152064,

// adding the following snippets
            "rope_scaling": {
                "factor": 4.0,
                "original_max_position_embeddings": 32768,
                "type": "yarn"
            }
        }
    ```
    This snippet enable YARN to support longer contexts.

3. **Model Deployment**: Utilize vLLM to deploy your model. For instance, you can set up an openAI-like server using the command:

```bash
    python -m vllm.entrypoints.openai.api_server --served-model-name Qwen2-7B-Instruct --model path/to/weights
    ```

Then you can access the Chat API by:

```bash
    curl https://127.0.0.1:8000/v1/chat/completions \
        -H "Content-Type: application/json" \
        -d '{
        "model": "Qwen2-7B-Instruct",
        "messages": [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Your Long Input Here."}
        ]
        }'
    ```

For further usage instructions of vLLM, please refer to our [Github](https://github.com/QwenLM/Qwen2).

**Note**: Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts**. We advise adding the `rope_scaling` configuration only when processing long contexts is required.

## Evaluation

We briefly compare Qwen2-7B-Instruct with similar-sized instruction-tuned LLMs, including Qwen1.5-7B-Chat. The results are shown below:

| Datasets | Llama-3-8B-Instruct | Yi-1.5-9B-Chat | GLM-4-9B-Chat | Qwen1.5-7B-Chat | Qwen2-7B-Instruct |
| :--- | :---: | :---: | :---: | :---: | :---: |
| _**English**_ |  |  |  |  |  |
| MMLU | 68.4 | 69.5 | **72.4** | 59.5 | 70.5 |
| MMLU-Pro | 41.0 | - | - | 29.1 | **44.1** |
| GPQA | **34.2** | - | **-** | 27.8 | 25.3 |
| TheroemQA | 23.0 | - | - | 14.1 | **25.3** |
| MT-Bench | 8.05 | 8.20 | 8.35 | 7.60 | **8.41** |
| _**Coding**_ |  |  |  |  |  |
| Humaneval | 62.2 | 66.5 | 71.8 | 46.3 | **79.9** |
| MBPP | **67.9** | - | - | 48.9 | 67.2 |
| MultiPL-E | 48.5 | - | - | 27.2 | **59.1** |
| Evalplus | 60.9 | - | - | 44.8 | **70.3** |
| LiveCodeBench | 17.3 | - | - | 6.0 | **26.6** |
| _**Mathematics**_ |  |  |  |  |  |
| GSM8K | 79.6 | **84.8** | 79.6 | 60.3 | 82.3 |
| MATH | 30.0 | 47.7 | **50.6** | 23.2 | 49.6 |
| _**Chinese**_ |  |  |  |  |  |
| C-Eval | 45.9 | - | 75.6 | 67.3 | **77.2** |
| AlignBench | 6.20 | 6.90 | 7.01 | 6.20 | **7.21** |

## Citation

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

```
@article{qwen2,
  title={Qwen2 Technical Report},
  year={2024}
}
```

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