What are the hardware requirements for Huihui Qwen3.5 27B Claude 4.6 Opus Abliterated 6bit?

Huihui Qwen3.5 27B Claude 4.6 Opus Abliterated 6bit requires approximately 38.7 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Where can I download or evaluate Huihui Qwen3.5 27B Claude 4.6 Opus Abliterated 6bit?

Huihui Qwen3.5 27B Claude 4.6 Opus Abliterated 6bit is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Huihui Qwen3.5 27B Claude 4.6 Opus Abliterated 6bit by mlx-community — VRAM 38.7GB

Name: Huihui Qwen3.5 27B Claude 4.6 Opus Abliterated 6bit
Author: mlx-community

Huihui Qwen3.5 27B Claude 4.6 Opus Abliterated 6bit is an open-source language model by mlx-community. Features: 27b LLM, VRAM: 38.7GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.24.

6-bit 6bit Abliterated Base model:jackrong/qwen3.5-27... Base model:quantized:jackrong/... Chain-of-thought Claude Conversational Dense Endpoints compatible Image-text-to-text Mlx Quantized Qwen3 5 Reasoning Region:us Safetensors Sharded Tensorflow Uncensored

Model Card on HF 🤗: https://huggingface.co/mlx-community/Huihui-Qwen3.5-27B-Claude-4.6-Opus-abliterated-6bit

Huihui Qwen3.5 27B Claude 4.6 Opus Abliterated 6bit Parameters and Internals

LLM Name	Huihui Qwen3.5 27B Claude 4.6 Opus Abliterated 6bit
Repository 🤗	https://huggingface.co/mlx-community/Huihui-Qwen3.5-27B-Claude-4.6-Opus-abliterated-6bit
Model Name	qwen/Qwen3.5-27B
Base Model(s)	Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled
Model Size	27b
Required VRAM	38.7 GB
Updated	2026-08-08
Maintainer	mlx-community
Model Type	qwen3_5
Model Files	5.3 GB: 1-of-3 5.4 GB: 1-of-5 5.3 GB: 2-of-3 5.3 GB: 2-of-5 5.4 GB: 3-of-3 5.3 GB: 3-of-5 5.3 GB: 4-of-5 1.4 GB: 5-of-5
Quantization Type	6bit
Model Architecture	Qwen3_5ForConditionalGeneration
License	apache-2.0
Model Max Length	262144
Tokenizer Class	TokenizersBackend
Padding Token	<\|endoftext\|>
Errors	replace

Rank the Huihui Qwen3.5 27B Claude 4.6 Opus Abliterated 6bit capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to Huihui Qwen3.5 27B Claude 4.6 Opus Abliterated 6bit

Best Alternatives	Context / RAM	Downloads	Likes
Qwen3.6 27B OptiQ 4bit	256K / 18.7 GB	13065	59
Bonsai 27B Mlx 1bit	0K / 5.1 GB	809309	205
Ternary Bonsai 27B Mlx 2bit	0K / 8.5 GB	809024	168
Qwen3.8 27B 8bit	0K / 29.4 GB	13892	33
Qwen3.8 27B 4bit	0K / 16 GB	6055	27
Qwen3.8 27B MLX 4bit	0K / 16 GB	19712	10
Qwen3.8 27B MLX 8bit	0K / 29.4 GB	24811	7
Qwen3.6 27B MLX 8bit	0K / 29.4 GB	879145	14
Qwen3.8 27B OptiQ 4bit	0K / 19.4 GB	3189	9
Qwen3.6 27B MLX 6bit	0K / 22.7 GB	812977	2

Note: green Score (e.g. "73.2") means that the model is better than mlx-community/Huihui-Qwen3.5-27B-Claude-4.6-Opus-abliterated-6bit.