license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- chat
Qwen2-72B-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 72B 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.
In this repo, we provide fp16 model and quantized models in the GGUF formats, including q5_0, q5_k_m, q6_k and q8_0.
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
We advise you to clone llama.cpp and install it following the official guide.
How to use
Cloning the repo may be inefficient, and thus you can manually download the GGUF file that you need or use huggingface-cli (pip install huggingface_hub) as shown below:
huggingface-cli download Qwen/Qwen2-72B-Instruct-GGUF qwen2-72b-instruct-q8_0.gguf --local-dir . --local-dir-use-symlinks False
We demonstrate how to use llama.cpp to run Qwen2:
./main -m qwen2-72b-instruct-q8_0.gguf -n 512 --color -i -cml -f prompts/chat-with-qwen.txt
Citation
If you find our work helpful, feel free to give us a cite.
@article{qwen2,
title={Qwen2 Technical Report},
year={2024}
}