base_model: GetSoloTech/GPT-OSS-Code-Reasoning-20B
datasets:
- nvidia/OpenCodeReasoning-2
- GetSoloTech/Code-Reasoning
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- code-reasoning
- coding
- reasoning
- problem-solving
- algorithms
- python
- c++
- competitive-programming
- vllm
About
static quants of https://huggingface.co/GetSoloTech/GPT-OSS-Code-Reasoning-20B
For a convenient overview and download list, visit our model page for this model.
weighted/imatrix quants are available at https://huggingface.co/mradermacher/GPT-OSS-Code-Reasoning-20B-i1-GGUF
Usage
If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.
Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|---|---|---|---|
| GGUF | Q3_K_S | 12.2 | |
| GGUF | Q2_K | 12.2 | |
| GGUF | IQ4_XS | 12.3 | |
| GGUF | Q3_K_M | 13.0 | lower quality |
| GGUF | Q3_K_L | 13.4 | |
| GGUF | Q4_K_S | 14.8 | fast, recommended |
| GGUF | Q4_K_M | 15.9 | fast, recommended |
| GGUF | Q5_K_S | 16.0 | |
| GGUF | Q5_K_M | 17.0 | |
| GGUF | Q6_K | 22.3 | very good quality |
| GGUF | Q8_0 | 22.4 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.
Thanks
I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.