base_model: lunahr/Qwen3-0.6B-Code-Expert-abliterated
datasets:
- nvidia/OpenCodeReasoning
language: - en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags: - unsloth
- trl
- sft
- code
- reasoning
- abliterated
- baukit-abliterated
About
static quants of https://huggingface.co/lunahr/Qwen3-0.6B-Code-Expert-abliterated
For a convenient overview and download list, visit our model page for this model.
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-0.6B-Code-Expert-abliterated-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 | Q2_K | 0.4 | |
| GGUF | Q3_K_S | 0.4 | |
| GGUF | Q3_K_M | 0.4 | lower quality |
| GGUF | Q3_K_L | 0.5 | |
| GGUF | IQ4_XS | 0.5 | |
| GGUF | Q4_K_S | 0.5 | fast, recommended |
| GGUF | Q4_K_M | 0.5 | fast, recommended |
| GGUF | Q5_K_S | 0.5 | |
| GGUF | Q5_K_M | 0.5 | |
| GGUF | Q6_K | 0.6 | very good quality |
| GGUF | Q8_0 | 0.7 | fast, best quality |
| GGUF | f16 | 1.3 | 16 bpw, overkill |
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.