Qwen2-0.5B-Instruct-GGUF

Qwen2-0.5B-Instruct-GGUF

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, GitHub, and Documentation. <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.

Getting Started

You can manually download the model using the huggingface-cli (pip install huggingface_hub):

huggingface-cli download msimou/Qwen2-0.5B-Instruct-GGUF Qwen2-0.5B-Instruct-Q8_0.gguf --local-dir .

To run Qwen2, you can use the llama-cli (the previous main) or llama-server. We recommend using the llama-server as it is simple and compatible with OpenAI API. For example:

./llama-server -m ./models/Qwen2-0.5B-Instruct-Q8_0.gguf --port 8000 --host 0.0.0.0 -ngl 28 -fa

(Note: -ngl 28 refers to offloading 24 layers to GPUs, and -fa refers to the use of flash attention.)

Then it is easy to access the service with:

curl --location 'http://localhost:8000/v1/chat/completions' \
--header 'Content-Type: application/json' \
--data '{
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant"
    },
    {
      "role": "user",
      "content": "What is your name?"
    }
  ]
}'

The service is compatible with OpenAI API style.

Evaluation

We briefly compare Qwen2-0.5B-Instruct with Qwen1.5-0.5B-Chat. The results are as follows:

References

• llama.cpp