---
title: Qwen2-0.5B-Instruct-AWQ
canonical_url: "https://www.modelscope.cn/models/Qwen/Qwen2-0.5B-Instruct-AWQ"
md_url: "https://www.modelscope.cn/models/Qwen/Qwen2-0.5B-Instruct-AWQ.md"
repository: Qwen/Qwen2-0.5B-Instruct-AWQ
chinese_name: "千问2-0.5B-Instruct-AWQ"
last_updated: 2026-08-07
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - qwen2
architectures:
  - Qwen2ForCausalLM
base_model:
  - Qwen/Qwen2-0.5B-Instruct
base_model_relation: quantized
parameters: 630.2M
tensor_type:
  - F16
  - I32
library_name:
  - transformer
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
downloads: 3980
stars: 0
tags:
  - chat
---

# Qwen2-0.5B-Instruct-AWQ

> Qwen2-0.5B-Instruct-AWQ - Qwen 在 ModelScope 开源的模型。Qwen2-0.5B-Instruct-AWQ

Qwen/Qwen2-0.5B-Instruct-AWQ 是 ModelScope 魔搭社区上的 630.2M 参数text-generation模型，采用 apache-2.0 许可，基于 Qwen/Qwen2-0.5B-Instruct 构建。

- **Repository**: Qwen/Qwen2-0.5B-Instruct-AWQ
- **License**: apache-2.0
- **Tasks**: text-generation
- **Parameters**: 630.2M
- **Base model**: Qwen/Qwen2-0.5B-Instruct
- **Tags**: chat
- **Downloads**: 3980
- **Stars**: 0
- **Last updated**: 2026-08-07

Source: https://www.modelscope.cn/models/Qwen/Qwen2-0.5B-Instruct-AWQ

---

# Qwen2-0.5B-Instruct-AWQ

## 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 0.5B 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.

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/Qwen2-0.5B-Instruct-AWQ",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct-AWQ")

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]
```

## Benchmark and Speed

To compare the generation performance between bfloat16 (bf16) and quantized models such as GPTQ-Int8, GPTQ-Int4, and AWQ, please consult our [Benchmark of Quantized Models](https://qwen.readthedocs.io/en/latest/benchmark/quantization_benchmark.html). This benchmark provides insights into how different quantization techniques affect model performance.

For those interested in understanding the inference speed and memory consumption when deploying these models with either ``transformer`` or ``vLLM``, we have compiled an extensive [Speed Benchmark](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).

## Citation

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

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