base_model: huihui-ai/Qwen2.5-3B-Instruct-abliterated language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara library_name: transformers license: apache-2.0 license_link: https://huggingface.co/huihui-ai/Qwen2.5-3B-Instruct-abliterated/blob/main/LICENSE mradermacher: readme_rev: 1 quantized_by: mradermacher tags:
- chat
- abliterated
- uncensored
About
static quants of https://huggingface.co/huihui-ai/Qwen2.5-3B-Instruct-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/Qwen2.5-3B-Instruct-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 |
|---|---|---|---|
| PART 1 PART 2 | Q2_K | 2.8 | |
| PART 1 PART 2 | Q3_K_S | 3.1 | |
| PART 1 PART 2 | Q3_K_M | 3.4 | lower quality |
| PART 1 PART 2 | Q3_K_L | 3.6 | |
| PART 1 PART 2 | IQ4_XS | 3.8 | |
| PART 1 PART 2 | Q4_K_S | 3.9 | fast, recommended |
| PART 1 PART 2 | Q4_K_M | 4.1 | fast, recommended |
| PART 1 PART 2 | Q5_K_S | 4.7 | |
| PART 1 PART 2 | Q5_K_M | 4.8 | |
| PART 1 PART 2 | Q6_K | 5.4 | very good quality |
| GGUF | f16 | 6.9 | 16 bpw, overkill |
| PART 1 PART 2 | Q8_0 | 7.0 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
image.png
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. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.