base_model: hotdogs/Qwen35B-Agent-R2-Abliterated
datasets:
- hotdogs/uka-fable-reasoning
- 11-47/claude_opus_4.8_max_thinking_5k_v2
- cx-cmu/agent_trajectories
language: - en
- th
library_name: transformers
license: agpl-3.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags: - qwen
- moe
- mixture-of-experts
- agent
- agent-world
- tool-use
- tool-calling
- reasoning
- sft
- abliterated
- uncensored
- opus
- fable
- conversational
- vision
- image-text-to-text
- transformers
- text-generation
- thai
- ykai
About
static quants of https://huggingface.co/hotdogs/Qwen35B-Agent-R2-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/Qwen35B-Agent-R2-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 | 13.0 | |
| GGUF | Q3_K_S | 15.3 | |
| GGUF | Q3_K_M | 16.9 | lower quality |
| GGUF | Q3_K_L | 18.2 | |
| GGUF | IQ4_XS | 19.0 | |
| GGUF | Q4_K_S | 20.0 | fast, recommended |
| GGUF | Q4_K_M | 21.3 | fast, recommended |
| GGUF | Q5_K_S | 24.1 | |
| GGUF | Q5_K_M | 24.8 | |
| GGUF | Q6_K | 28.6 | very good quality |
| GGUF | Q8_0 | 37.0 | 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.