Qwen3.5-9B-Janus-Abliterated-V2-Safetensors

Qwen3.5-9B-Janus-Abliterated-V2

<p align="center"> <em>Base model intelligence. Abliterated freedom. One balanced merge.</em> </p>

🧬 What is "Janus"?

Janus is my personal merging recipe that blends a base model with its abliterated counterpart in a controlled ratio:

The goal is the two-faced balance of the Roman god Janus: keeps a solid core of the base model's reasoning while the abliterated half loosens refusal vectors. Unlike pure abliterations — which often degrade reasoning — the aim is to recover some of the base model's behavioral stability and coherence while retaining much of the reduced-refusal behavior of the abliterated checkpoint.

⚠️ This is a merge, not a finetune. No additional training data was used.

📦 Files / Quantization

• Format: safetensors

🎯 Intended Use

• Creative writing, fiction, and long-form narrative
• Roleplay (including mature/NSFW scenarios for users 18+)
• Uncensored brainstorming and research assistance
• General chat with fewer refusals on benign-but-edgy prompts

Not intended for: safety-critical deployments, automated decisioning, or anything requiring a guaranteed aligned refusal layer.

🛠️ Recipe

<details> <summary><b>Merge configuration</b> (click to expand)</summary>

merge_method: slerp
base_model: &model_a Qwen/Qwen3.5-9B   # Notice the & anchor trick!

slices:
  - sources:
      - model: *model_a                          # Automatically matches base_model
        layer_range: [0, 32]                    
      - model: huihui-ai/Huihui-Qwen3.5-9B-abliterated
        layer_range: [0, 32]
        # Only uncomment the 2 lines below if pulling from a Janus/VL model:
        # rename:
        #   - ["text_model.model.", "model."]
        #   - ["text_model.", ""]

parameters:
  t: 0.60                                        # 60% Model B, 40% Model A
dtype: bfloat16
tokenizer_source: base

</details>

🧪 Custom Benchmark

I don't publish MMLU / HellaSwag scores for a merge — those aren't what this model is for.

⚠️ Limitations & Warnings

• Merge artifacts: this model may occasionally produce degraded outputs on edge-case math or very long contexts.
• Reduced safety layer: the abliteration component intentionally lowers refusal behavior. Users are responsible for how they use the model.
• No new knowledge: knowledge cutoff and factual accuracy inherit from the base.
• Not affiliated with Qwen / Alibaba. Original licenses of the source models apply.

🙏 Credits

• Qwen/Qwen3.5-9B — base model
• huihui-ai/Huihui-Qwen3.5-9B-abliterated — abliterated variant
• Merge + Janus recipe: SOMEHOTMEAL

📜 License

Apache 2.0, inherited from the base Qwen model. Abliterated sources under their respective licenses.