base_model: huihui-ai/Huihui-MoE-46B-A14B-abliterated
extra_gated_prompt: |-
Usage Warnings
“Risk of Sensitive or Controversial Outputs“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.
“Not Suitable for All Audiences:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security.
“Legal and Ethical Responsibilities“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences.
“Research and Experimental Use“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications.
“Monitoring and Review Recommendations“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content.
“No Default Safety Guarantees“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-14B/blob/main/LICENSE
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags: - moe
About
static quants of https://huggingface.co/huihui-ai/Huihui-MoE-46B-A14B-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/Huihui-MoE-46B-A14B-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 | 17.5 | |
| GGUF | Q3_K_S | 20.5 | |
| GGUF | Q3_K_M | 22.7 | lower quality |
| GGUF | Q3_K_L | 24.5 | |
| GGUF | IQ4_XS | 25.5 | |
| GGUF | Q4_K_S | 26.9 | fast, recommended |
| GGUF | Q4_K_M | 28.5 | fast, recommended |
| GGUF | Q5_K_S | 32.4 | |
| GGUF | Q5_K_M | 33.4 | |
| GGUF | Q6_K | 38.5 | very good quality |
| GGUF | Q8_0 | 49.9 | 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.