---
license: apache-2.0
language:
- en
license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct/blob/main/LICENSE
base_model:
- Qwen/Qwen2.5-Coder-32B-Instruct
pipeline_tag: text-generation
tags:
- code
- chat
- qwen
- qwen-coder
- exl3
---

These models are exl3 quantization models of [Qwen2.5-Coder-32B](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct) which is still SOTA no-reasoning coder model as of today. This model is still my go-to FIM(fill in the middle) autocompletion model after Qwen3, Gemma3 release.
I used [exllamav3 version 0.0.2](https://github.com/turboderp-org/exllamav3/releases/tag/v0.0.2).

## EXL3 Quantized Models

[4.0bpw](https://huggingface.co/LLMJapan/Qwen2.5-Coder-32B-Instruct_exl3/tree/4.0bpw)

[6.0bpw](https://huggingface.co/LLMJapan/Qwen2.5-Coder-32B-Instruct_exl3/tree/6.0bpw)

[8.0bpw](https://huggingface.co/LLMJapan/Qwen2.5-Coder-32B-Instruct_exl3/tree/8.0bpw)

For coding, I found >=6.0bpw or preferably 8.0bpw model with KV Cache Quantization (>=Q6) is much better than 4.0bpw.
If you are using these models only for short Auto Completion, 4.0bpw is usable.

## Credits

Thanks to excellent work of exllamav3 dev teams.