---
license: other
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- transformers
- gguf
- imatrix
- Qwen2-7B-Instruct
---
Quantizations of https://huggingface.co/Qwen/Qwen2-7B-Instruct

**Note: you should use latest llama.cpp version with -fa switch to avoid garbage output.**

# From original readme

## 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 http://localhost: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.