mannix / qwen2-57b

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

model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2-57B-A14B-Instruct",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-57B-A14B-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token的长输入，我们使用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化。

对于部署，我们推荐使用vLLM。您可以通过以下步骤启用长对话功能

安装vLLM：请确保您从vLLM的主分支上安装了最新版本。

配置模型设置：下载模型权重后，通过包含以下片段修改config.json文件

    {
        "architectures": [
            "Qwen2MoeForCausalLM"
        ],
        // ...
        "vocab_size": 152064,


        // adding the following snippets
        "rope_scaling": {
            "factor": 2.0,
            "original_max_position_embeddings": 32768,
            "type": "yarn"
        }
    }

此片段使YARN支持更长的上下文。

模型部署：利用vLLM部署您的模型。例如，您可以使用以下命令设置像openAI一样的服务器

python -m vllm.entrypoints.openai.api_server --served-model-name Qwen2-57B-A14B-Instruct --model path/to/weights

然后您可以按照以下方式访问Chat API

curl https://127.0.0.1:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
    "model": "Qwen2-57B-A14B-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，这意味着缩放因子不受输入长度的影响，可能影响短文本的性能。只有在需要处理长上下文时才建议添加rope_scaling配置。

评估

我们简要比较了Qwen2-57B-A14B-Instruct与类似大小的指令微调LLM，包括Qwen1.5-32B-Chat。结果如下

数据集	Mixtral-8x7B-Instruct-v0.1	Yi-1.5-34B-Chat	Qwen1.5-32B-Chat	Qwen2-57B-A14B-Instruct
架构	MoE	密集型	密集型	MoE
激活的参数数	12B	34B	32B	14B
总参数数	47B	34B	32B	57B
英语
MMLU	71.4	76.8	74.8	75.4
MMLU-Pro	43.3	52.3	46.4	52.8
GPQA	-	-	30.8	34.3
TheroemQA	-	-	30.9	33.1
MT-Bench	8.30	8.50	8.30	8.55
编码
HumanEval	45.1	75.2	68.3	79.9
MBPP	59.5	74.6	67.9	70.9
MultiPL-E	-	-	50.7	66.4
EvalPlus	48.5	-	63.6	71.6
LiveCodeBench	12.3	-	15.2	25.5
数学
GSM8K	65.7	90.2	83.6	79.6
MATH	30.7	50.1	42.4	49.1
中文
C-Eval	-	-	76.7	80.5
AlignBench	5.70	7.20	7.19	7.36

引用

如果您发现我们的工作有帮助，请随时引用我们。

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

# Qwen2-57B-A14B-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 57B-A14B Mixture-of-Experts 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.

**Qwen2-57B-A14B-Instruct supports a context length of up to 65,536 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/) and [GitHub](https://github.com/QwenLM/Qwen2).
<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 Qwen2MoE has been in the latest Hugging face transformers and we advise you to install `transformers>=4.40.0`, or you might encounter the following error:
```
KeyError: 'qwen2_moe'
```

## 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/Qwen2-57B-A14B-Instruct",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-57B-A14B-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**: Ensure you have the latest version from the main branch of [vLLM](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": [
                "Qwen2MoeForCausalLM"
            ],
            // ...
            "vocab_size": 152064,

// adding the following snippets
            "rope_scaling": {
                "factor": 2.0,
                "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 Qwen2-57B-A14B-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": "Qwen2-57B-A14B-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 Qwen2-57B-A14B-Instruct with similar-sized instruction-tuned LLMs, including Qwen1.5-32B-Chat. The results are shown as follows:

| Datasets | Mixtral-8x7B-Instruct-v0.1 | Yi-1.5-34B-Chat | Qwen1.5-32B-Chat | **Qwen2-57B-A14B-Instruct** |
| :--- | :---: | :---: | :---: | :---: |
|Architecture | MoE | Dense | Dense | MoE |
|#Activated Params | 12B | 34B | 32B | 14B |
|#Params | 47B | 34B | 32B | 57B   |
| _**English**_ |  |  |  |  |
| MMLU | 71.4 | **76.8** | 74.8 | 75.4 |
| MMLU-Pro | 43.3 | 52.3 | 46.4 | **52.8** |
| GPQA | - | - | 30.8 | **34.3** |
| TheroemQA | - | - | 30.9 | **33.1** |
| MT-Bench | 8.30 | 8.50 | 8.30 | **8.55** |
| _**Coding**_ |  |  |  |  |
| HumanEval | 45.1 | 75.2 | 68.3 | **79.9** |
| MBPP | 59.5 | **74.6** | 67.9 | 70.9 |
| MultiPL-E | - | - | 50.7 | **66.4** |
| EvalPlus | 48.5 | - | 63.6 | **71.6** |
| LiveCodeBench | 12.3 | - | 15.2 | **25.5** |
| _**Mathematics**_ |  |  |  |  |
| GSM8K | 65.7 | **90.2** | 83.6 | 79.6 |
| MATH | 30.7 | **50.1** | 42.4 | 49.1 |
| _**Chinese**_ |  |  |  |  |
| C-Eval | - | - | 76.7 | 80.5 |
| AlignBench | 5.70 | 7.20 | 7.19 | **7.36** |

## 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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