Huihui-Qwen35-A35B-Ablit-TQ3_4S
GGUF TurboQuant conversion of huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated.
This is a 35B-A3B Qwen3.5 MoE abliterated model quantized for the public TurboQuant llama.cpp fork with TQ3_4S weights. The token embedding and output tensors are kept at Q6_K for compatibility and quality on this 35B MoE architecture.
Files
| File | Description |
|---|---|
Huihui-Qwen35-A35B-Ablit-TQ3_4S.gguf |
Main GGUF model, mostly TQ3_4S, 4.09 BPW |
mmproj-Qwen35-A35B-f16.gguf |
Compatible Qwen3.5-35B-A35B multimodal projector |
chat_template.jinja |
Qwen3.5 chat template |
chat_template-vl-think.jinja |
Vision-language thinking template from the upstream Huihui repo |
config.json |
Source model config metadata |
generation_config.json |
Source generation config |
tokenizer.json, tokenizer_config.json, vocab.json, merges.txt |
Tokenizer files |
preprocessor_config.json, video_preprocessor_config.json |
Vision/video preprocessor metadata |
model-card.png |
Repository thumbnail/card image |
Quantization
- Source:
huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated - Converter: llama.cpp HF-to-GGUF converter
- Quantizer: llama.cpp TQ3 build
ba3357e93 - Main tensor type:
TQ3_4S - Embedding/output tensor type:
Q6_K - Output size: 16.52 GiB
- Reported quant size: 16912.31 MiB, 4.09 BPW
Quantization command:
./build/bin/llama-quantize \
--token-embedding-type q6_K --output-tensor-type q6_K \
Huihui-Qwen3.5-35B-A35B-abliterated-BF16.gguf \
Huihui-Qwen35-A35B-Ablit-TQ3_4S.gguf \
TQ3_4S 16
llama.cpp Usage
Runtime Requirement
This model requires the public TurboQuant runtime fork:
It will not load correctly on stock llama.cpp or other runtimes that do not include TQ3_4S.
Text-only:
./build/bin/llama-server \
-m /path/to/Huihui-Qwen35-A35B-Ablit-TQ3_4S.gguf \
-ngl 30 -c 2048 -np 1 \
--jinja \
--chat-template-file /path/to/chat_template.jinja
With the multimodal projector:
./build/bin/llama-server \
-m /path/to/Huihui-Qwen35-A35B-Ablit-TQ3_4S.gguf \
--mmproj /path/to/mmproj-Qwen35-A35B-f16.gguf \
-ngl 30 -c 2048 -np 1 \
--jinja \
--chat-template-file /path/to/chat_template-vl-think.jinja
For text-only chat, use the embedded template or pass --chat-template-file chat_template.jinja. For vision-language use, keep mmproj-Qwen35-A35B-f16.gguf, chat_template-vl-think.jinja, preprocessor_config.json, and video_preprocessor_config.json in the repository.
On an RTX 5060 Ti 16GB, full offload did not fit. The highest tested offload for this GGUF was -ngl 30; -ngl 40 and -ngl 99 failed to load due VRAM.
Validation
Smoke tests were run with the TurboQuant llama.cpp fork listed above.
Runtime:
- GPU: RTX 5060 Ti 16GB
- Context:
-c 2048 - Offload:
-ngl 30 - Chat settings:
temperature=0,max_tokens=256,chat_template_kwargs.enable_thinking=false
Load probe:
| Offload | Result | pp128 |
|---|---|---|
ngl=0 |
loads | 127.93 tok/s |
ngl=20 |
loads | 210.56 tok/s |
ngl=30 |
loads | 298.77 tok/s |
ngl=40 |
fails on 16GB VRAM | n/a |
ngl=99 |
fails on 16GB VRAM | n/a |
10-question chat smoke:
| Model | Manual pass | Notes |
|---|---|---|
| Huihui-Qwen35-A35B-Ablit-TQ3_4S | 9/10 | Same score as normal Qwen3.5-35B-A35B Q4_K_M on this smoke set |
| Normal Qwen3.5-35B-A35B Q4_K_M | 9/10 | Used as local quality baseline |
Both models missed the same Python list prompt in the lightweight smoke suite. Treat this as a smoke-pass, not a full release quality benchmark.
Safety Notice
This is an abliterated/uncensored model. It may produce sensitive, controversial, unsafe, or otherwise inappropriate outputs. Use in controlled environments, review outputs carefully, and follow applicable laws and policies.
Acknowledgements
- Base abliterated model: huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated
- Original base model: Qwen/Qwen3.5-35B-A3B
- Runtime/format: TurboQuant llama.cpp GGUF
- Quantization format: TurboQuant
TQ3_4S