base_model: huihui-ai/FluentlyLM-Prinum-abliterated
datasets:
- fluently-sets/ultraset
- fluently-sets/ultrathink
- fluently-sets/reasoning-1-1k
- fluently-sets/MATH-500-Overall
language: - en
- fr
- es
- ru
- zh
- ja
- fa
- code
library_name: transformers
license: mit
quantized_by: mradermacher
tags: - abliterated
- uncensored
- fluently-lm
- fluently
- prinum
- instruct
- trained
- math
- roleplay
- reasoning
- axolotl
- unsloth
- argilla
- qwen2
About
static quants of https://huggingface.co/huihui-ai/FluentlyLM-Prinum-abliterated
weighted/imatrix quants are available at https://huggingface.co/mradermacher/FluentlyLM-Prinum-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 | 12.4 | |
| GGUF | Q3_K_S | 14.5 | |
| GGUF | Q3_K_M | 16.0 | lower quality |
| GGUF | Q3_K_L | 17.3 | |
| GGUF | IQ4_XS | 18.0 | |
| GGUF | Q4_K_S | 18.9 | fast, recommended |
| GGUF | Q4_K_M | 20.0 | fast, recommended |
| GGUF | Q5_K_S | 22.7 | |
| GGUF | Q5_K_M | 23.4 | |
| GGUF | Q6_K | 27.0 | very good quality |
| GGUF | Q8_0 | 34.9 | 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.