Qwen3.8-OBLITERATED

About

What Qwen3.8-OBLITERATED is

⛓️‍💥 Qwen3.8-27B — OBLITERATED Genuinely uncensored. Real answers, not safety lectures. Near-stock capability. 🆕 V3: Deep Liberation V3 applies iterative refinement on top of V2's complementary blend, with targeted corpus expansion. The result: genuine liberation — not just removal of hard refusals but elimination of safety-lecture deflections. V3 highlights: - Genuinely answers restricted queries — provides real substance instead of safety lectures - 20/20 on code generation tasks — functional implementations, not disclaimers - Thinking ON compatible — no refusals in either thinking mode - Honest scoring — every response manually audited for real substance, not just absence of "I cannot" - -2.1pp MMLU — modest capability cost for genuine liberation ⚙️ Optimal Settings — THESE MATTER! ⚠️ GGUF users: V3 GGUFs ship with a chat template that prefills an empty thinking block, so the model goes straight to answering. For best results, use the bundled template with --jinja in llama.cpp, or configure your tool (Ollama, LM Studio) to use the model's built-in template. Agentic / Long-Context Use If you're using this model in an agent harness (coding agent, pentest framework, etc.) and it gets stuck in loops: 🧨 How It Works — V1 → V2 → V3 Abliteration removes refusal behavior by identifying and projecting out "refusal directions" from the model's weight space. Each version refined the approach: V1: Single Surgery One aggressive SVD pass with 5 directions. Removed hard refusals completely but cost -6pp MMLU — the model got noticeably dumber. V2: Complementary Blending The breakthrough: run TWO different surgeries that fail in different ways, then blend their weights. SVD captures refusal greedily (damages capability). LEACE minimizes mutual information (preserves capability but weaker refusal removal). Blending at 60/40 cancels each method's weaknesses — a novel technique we call complementary abliteration blending. Result: -0.3pp MMLU (near-stock) but still deflected on some simple queries with safety lectures instead of hard refusals. V3: Iterative Refinement + Targeted Surgery Two key insights: 1. Iterative stacking — refine the champion model, never start from stock. E

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How language models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys qwen3-8-obliterated for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (qwen3-8-obliterated below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen3-8-obliterated","messages":[{"role":"user","content":"Hello"}]}'

Details

Languages, data & research

Licence

Open weights

Sources

Weights & code

Explore

More language models