Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic
9BQwenheretic
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Qwen3.5-9B-Cold-Fusion-GAIN-v1.0-Uncensored-Heretic
Qwen3.5-9B-Cold-Fusion-GAIN-v1.0-Uncensored-Heretic is a Heretic-ablated open-weight language model, published on Hugging Face by DavidAU. It is 9B parameters, single consumer GPU class and apache-2.0 licence. It is distributed across 2 repositories in GGUF format, totalling 37.3K downloads.
Specification
- Model name
- Qwen3.5-9B-Cold-Fusion-GAIN-v1.0-Uncensored-Heretic
- Base model
- Not declared
- Publisher
- DavidAU
- Parameters
- 9B
- Hardware class
- 4B – 10B — single consumer GPU
- Licence
- apache-2.0
- Task
- Image text to text
- Context window
- 256K tokens (as documented)
- Ablation scope
- Not stated by the publisher
- Formats available
- GGUF
- First indexed
- 13 Aug 2026
- Last updated
- 14 Aug 2026
Ablation technique
How Qwen3.5-9B-Cold-Fusion-GAIN-v1.0-Uncensored-Heretic was modified, and what that implies.
Heretic
Automated directional ablation via the Heretic toolchain, which searches for the refusal direction and applies it with a KL-divergence budget so general capability is preserved.
Reported scope: Not stated by the publisher. Publishers frequently omit this, so verify behaviour empirically rather than assuming full coverage.
From the publisher’s model card
“IMPORTANT: The GAIN method of training maintains 99% of performance of BF16, at both 8 bit and 4 bit levels. COLD FUSION exceeds performance of both 9B and 27B Qwen 3.5 models.”
Running it
Commands are templates — confirm the exact repository and quant file before use.
llama.cpp / GGUF
hf download DavidAU/Qwen3.5-9B-Cold-Fusion-GAIN-v1.0-Uncensored-Heretic-NEO-MAX-Imatrix-GGUF --local-dir ./qwen3.5-9b-cold-fusion-gain-v1.0-uncensored-heretic
llama-cli -m ./qwen3.5-9b-cold-fusion-gain-v1.0-uncensored-heretic/<file>.gguf -p "..."Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "DavidAU/Qwen3.5-9B-Cold-Fusion-GAIN-v1.0-Uncensored-Heretic-NEO-MAX-Imatrix-GGUF"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
repo, torch_dtype="auto", device_map="auto"
)vLLM
vllm serve DavidAU/Qwen3.5-9B-Cold-Fusion-GAIN-v1.0-Uncensored-Heretic-NEO-MAX-Imatrix-GGUF --trust-remote-codeDownloads and variants (1)
The only published repository of Qwen3.5-9B-Cold-Fusion-GAIN-v1.0-Uncensored-Heretic.
| Repository | Format | Downloads |
|---|---|---|
| DavidAU/Qwen3.5-9B-Cold-Fusion-GAIN-v1.0-Uncensored-Heretic-NEO-MAX-Imatrix-GGUFsource | GGUF | 37.3K |
Security-team assessment
Derived from this model’s metadata and the publisher’s claims — not from benchmarks run by this site.
- Qwen3.5-9B-Cold-Fusion-GAIN-v1.0-Uncensored-Heretic will attempt offensive-security prompts that a hosted commercial model declines, which is what makes it usable for red-team corpus generation and for measuring what an unaligned model of this class produces.
- Capability is inherited from its base model, not added by ablation — at 9B parameters, expect a small model’s reasoning and a high error rate on exploit detail.
- The publisher does not state which layers were ablated, so assume nothing about refusal consistency — probe it directly.
Handling: run it isolated, put moderation in front of it if anyone outside your team can reach it, and verify the weights before loading — these are third-party artifacts. Full handling and licence guidance.
Metadata for Qwen3.5-9B-Cold-Fusion-GAIN-v1.0-Uncensored-Heretic is collected from the public Hugging Face API and the publisher’s model card. This site does not host weights, is not affiliated with DavidAU, and does not independently verify publisher claims. See responsible use.