Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains.
Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots.
Long-context Support up to 128K tokens and can generate up to 8K tokens.
Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.

This repo contains the instruction-tuned 32B Qwen2.5 model in the GGUF Format, which has the following features:

Type: Causal Language Models
Training Stage: Pretraining & Post-training
Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
Number of Parameters: 32.5B
Number of Paramaters (Non-Embedding): 31.0B
Number of Layers: 64
Number of Attention Heads (GQA): 40 for Q and 8 for KV
Context Length: Full 32,768 tokens and generation 8192 tokens
- Note: Currently, only vLLM supports YARN for length extrapolating. If you want to process sequences up to 131,072 tokens, please refer to non-GGUF models.
Quantization: q4_K_M, q5_K_M

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

Quickstart

Check out our llama.cpp documentation for more usage guide.

We advise you to clone llama.cpp and install it following the official guide. We follow the latest version of llama.cpp. In the following demonstration, we assume that you are running commands under the repository llama.cpp.

Since cloning the entire repo may be inefficient, you can manually download the GGUF file that you need or use huggingface-cli:

Install
pip install -U huggingface_hub
Download:
huggingface-cli download Qwen/Qwen2.5-32B-Instruct-GGUF --include "qwen2.5-32b-instruct-q5_k_m*.gguf" --local-dir . --local-dir-use-symlinks False
For large files, we split them into multiple segments due to the limitation of file upload. They share a prefix, with a suffix indicating its index. For examples, qwen2.5-32b-instruct-q5_k_m-00001-of-00006.gguf to qwen2.5-32b-instruct-q5_k_m-00006-of-00006.gguf. The above command will download all of them.
(Optional) Merge: For split files, you need to merge them first with the command llama-gguf-split as shown below:
bash
1# ./llama-gguf-split --merge <first-split-file-path> <merged-file-path> 2./llama-gguf-split --merge qwen2.5-32b-instruct-q5_k_m-00001-of-00006.gguf qwen2.5-32b-instruct-q5_k_m.gguf

For users, to achieve chatbot-like experience, it is recommended to commence in the conversation mode:

shell

./llama-cli -m <gguf-file-path> \
    -co -cnv -p "You are Qwen, created by Alibaba Cloud. You are a helpful assistant." \
    -fa -ngl 80 -n 512

dtometzki/Qwen2.5-Coder-32B-GGUF

Qwen2.5-32B-Instruct-GGUF

Introduction

Quickstart

dtometzki/
Qwen2.5-Coder-32B-GGUF