Qwen2-7B-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 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 for detailed instructions on how to deploy Qwen2 for handling long texts.

For more details, please refer to our blog, GitHub, and Documentation.

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'

Getting Started

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

huggingface-cli download msimou/Qwen2-7B-Instruct-GGUF Qwen2-7B-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/Qwen2-7B-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 Qwen2-7B-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

msimou
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Qwen2-7B-Instruct-GGUF