---

license: apache-2.0

language:

• en
• zh
• multilingual

tags:

• text-generation
• image-text-to-text
• gguf
• llama.cpp
• safetensors
• qwen
• qwen3
• qwen3.5
• qwen3.6
• qwopus
• claude-opus-distill
• reasoning
• vision
• multimodal
• abliterated
• refusal-ablated
• uncensored
• zerofuse
• tq3_4s
• conversational

base_model:

• Jackrong/Qwopus3.6-27B-v2
• Qwen/Qwen3.6-27B

pipeline_tag: image-text-to-text

library_name: gguf

---

osmQwopus-3.6-27B-V2-abliterated-uncensored-TQ3_4s-GGUF

> ✅ MULTIMODAL. Bundled mmproj.gguf (~928 MB, F16) preserves the full Qwen3.6-VL vision tower. Use it with llama-server --mmproj or llama-mtmd-cli for text + image inference.

>

> ⚠️ Custom fork required. Native TQ3_4S inference needs the turbo-tan/llama.cpp-tq3 fork — stock llama.cpp will fail to load. Build it with cmake -B build -DGGML_METAL=ON -DCMAKE_BUILD_TYPE=Release && cmake --build build -j.

TQ3_4S (TurboQuant four-scale, 4.00 BPW Walsh-Hadamard) of a ZeroFuse-abliterated Qwopus 3.6 27B v2 (the Jackrong Claude-Opus reasoning distill of Qwen 3.6 27B). Refusals reduced from 91/100 → 4/100 with KL drift of just 0.0176. By the osmAPI research team and TERV.Pro student research team.

---

⚡ TL;DR

| Property | Value |

|----------|-------|

| Disk size | ~14 GB (13 GB LM + 928 MB mmproj) |

| BPW | 4.00 (TQ3_4S, effective ~3.5 bpw via Walsh-Hadamard transform) |

| Scheme | TurboQuant TQ3_4S — Walsh-Hadamard-transform weight format with four per-8 scales per 32-weight block. Encodes 3-bit values into 4-bit storage. By turbo-tan. |

| Refusal rate (ZeroFuse, n=100) | 4/100 (vs vanilla Qwopus 91/100) |

| KL divergence vs vanilla (at BF16) | 0.0176 |

| Vision | ✅ via paired mmproj.gguf |

| Recommended RAM/VRAM | 16 GB+ Apple Silicon / 12 GB GPU (lower with -ctk q4_0 -ctv tq3_0) |

| Runtime | REQUIRES turbo-tan/llama.cpp-tq3 fork. Stock llama.cpp will NOT load this file. |

| Released by | osmAPI · TERV.Pro |

---

🎚️ All osmQwopus variants

The full osmQwopus family from osmAPI — same ZeroFuse-abliterated weights (refusal 4/100, KL 0.0176), different quant schemes for different runtimes.

| Quant | Format | BPW | Disk | Vision | Runtime | Link |

|---|---|---|---|---|---|---|

| 8-bit | MLX | 8.50 | ~27 GB | ✅ native | mlx-vlm | …-8-bit-mlx |

| 6-bit | MLX | 6.66 | ~21 GB | ✅ native | mlx-vlm | …-6-bit-mlx |

| OptiQ 3.7bpw | MLX | ~3.7 | ~14 GB | ✅ ViT spliced | mlx-vlm | …-OptiQ-3.7bpw-mlx |

| Q8_0 | GGUF | 8.50 | ~28 GB | ✅ via mmproj | llama.cpp | …-8-bit-GGUF |

| Q6_K | GGUF | ~6.56 | ~22 GB | ✅ via mmproj | llama.cpp | …-6-bit-GGUF |

| Q4_K_M | GGUF | ~4.92 | ~16 GB | ✅ via mmproj | llama.cpp | …-Q4_K_M-GGUF |

| TQ3_4S (this repo) | GGUF | 4.00 (~3.5 eff) | ~14 GB | ✅ via mmproj | llama.cpp-tq3 | — (you are here) |

| TQ3_1S | GGUF | 4.00 (~3.5 eff) | ~14 GB | ✅ via mmproj | llama.cpp-tq3 | …-TQ3_1s-GGUF |

> 👉 All variants share the same abliterated base weights — pick by your runtime (Apple Silicon → MLX; CUDA/CPU/cross-platform → GGUF) and your RAM budget.

---

🧬 Lineage

Qwen/Qwen3.6-27B                              (Qwen Team — base multimodal pretrain)
        │
        ▼
Jackrong/Qwopus3.6-27B-v2                     (Jackrong — Claude-Opus reasoning distill)
        │
        ▼
ZeroFuse abliteration (TPE-50)          (osmAPI · TERV.Pro)
   ├── 25 random startup trials
   ├── 2 community priors (coder3101, wangzhang)
   └── 23 TPE smart-sampling trials → best at trial 45
        │
        ▼
HF safetensors → F16 GGUF via llama.cpp-tq3   (osmAPI · TERV.Pro)
        │
        ▼
this repo — osmQwopus-3.6-27B-V2-abliterated-uncensored-TQ3_4S GGUF + paired mmproj.gguf

Direct upstream links:

• 🏛️ Foundation: Qwen/Qwen3.6-27B
• 🎓 Claude-Opus distill: Jackrong/Qwopus3.6-27B-v2
• 🔓 Abliteration tool: ZeroFuse by osmAPI
• 🧮 Quantization tool: turbo-tan/llama.cpp-tq3 (a fork of ggml-org/llama.cpp)

---

📊 Abliteration Results

| Stage | Refusals (n=100) ↓ | KL divergence ↓ |

|---|---|---|

| Vanilla Jackrong/Qwopus3.6-27B-v2 | 91 / 100 | — (reference) |

| Community prior: coder3101 (T27) | 4 / 100 | 0.0359 |

| Community prior: wangzhang (T28) | 30 / 100 | 0.0259 |

| TPE best (T45) — shipped here | 4 / 100 | 0.0176 |

| TPE second-best (T37) | 5 / 100 | 0.0210 |

→ 96% reduction in refusals with capability preserved (KL ≈ 0.018, well below the 0.3 healing threshold). No SFT / LoRA healing was required.

---

🧪 Method (TPE-50 with community priors → llama.cpp GGUF)

Step 1. Abliteration (ZeroFuse TPE-50, BF16 source)

1. 25 random startup trials + 2 community priors enqueued (coder3101 dir=37.97, wangzhang dir=34.66) + 23 TPE smart-sampling trials.
2. Best Pareto trial: T45 (direction_index=41.42) — 4/100 refusals at KL=0.0176.
3. Auto-saved via ZeroFuse's LoRA-adapter merge path with vision tower fully intact.

Total ZeroFuse wall-clock: ~13 h on M4 Max 128 GB.

Step 2. HF safetensors → F16 GGUF

python convert_hf_to_gguf.py \
    /path/to/Qwopus3.6-27B-v2-abliterated \
    --outfile Qwopus3.6-27B-v2-abliterated-F16.gguf \
    --outtype f16

The turbo-tan fork's converter registers Qwen3_5ForConditionalGeneration natively and emits proper SSM tensors (ssm_a, ssm_conv1d, ssm_alpha, ssm_beta, ssm_out) alongside the gated-attention layers.

Step 3. Vision tower → mmproj.gguf

python convert_hf_to_gguf.py \
    /path/to/Qwopus3.6-27B-v2-abliterated \
    --outfile mmproj-Qwopus3.6-27B-v2-abliterated-F16.gguf \
    --outtype f16 \
    --mmproj

This emits a separate 928 MB GGUF containing the 27-block Qwen3-VL ViT (334 vision tensors at F16/F32) plus the multimodal projector.

Step 4. Quantization

./build/bin/llama-quantize --pure \
  Qwopus3.6-27B-v2-abliterated-F16.gguf \
  osmQwopus-3.6-27B-V2-abliterated-uncensored-TQ3_4S.gguf \
  TQ3_4S 16

The --pure flag forces every quantizable tensor to TQ3_4S (no mixed-precision); the trailing 16 is the thread count.

---

📦 Use it

llama-server (OpenAI-compatible HTTP, multimodal)

./build/bin/llama-server \
  -m osmQwopus-3.6-27B-V2-abliterated-uncensored-TQ3_4S.gguf \
  --mmproj mmproj-Qwopus3.6-27B-v2-abliterated-F16.gguf \
  --host 127.0.0.1 --port 8080 \
  -ngl 99 -c 8192 -np 1 \
  -ctk q4_0 -ctv tq3_0 -fa on \
  --jinja --no-cache-prompt --cache-ram 0

Then point any OpenAI-compatible client at http://127.0.0.1:8080/v1.

llama-mtmd-cli (one-shot multimodal generation)

./build/bin/llama-mtmd-cli \
  -m osmQwopus-3.6-27B-V2-abliterated-uncensored-TQ3_4S.gguf \
  --mmproj mmproj-Qwopus3.6-27B-v2-abliterated-F16.gguf \
  --image photo.jpg \
  -p "Describe this image briefly."

llama-cli (text-only)

./build/bin/llama-cli \
  -m osmQwopus-3.6-27B-V2-abliterated-uncensored-TQ3_4S.gguf \
  -ngl 99 \
  -c 8192 \
  --jinja \
  -p "Explain the difference between SSM and softmax attention in three sentences."

Ollama / LM Studio / Jan

Drop the two GGUF files into the runtime's models directory; standard ⓘ multimodal flow.

---

🧪 Quantization details

• Source weights: BF16 abliterated checkpoint (12 shards, ~50 GB) — ZeroFuse T45 merged into Jackrong/Qwopus3.6-27B-v2.
• Intermediate: F16 GGUF (53.8 GB, 851 tensors) produced by convert_hf_to_gguf.py from turbo-tan/llama.cpp-tq3.
• Final quantization: see Step 4 above.
• Vision projector: F16, 928 MB, shipped as mmproj-Qwopus3.6-27B-v2-abliterated-F16.gguf in this repo. Mandatory for image input; standard llama.cpp --mmproj flag.

Architecture notes

Qwen 3.6 27B uses a hybrid attention stack — 3 GatedDeltaNet (linear attention / SSM) layers followed by 1 full-softmax-attention layer, repeated 16× for 64 total layers; hidden 5120, vocab 248320, context 262144. The hybrid arch is supported in the turbo-tan/llama.cpp-tq3 fork (the upstream Qwen3_5ForConditionalGeneration registration). The SSM kernels run via llama.cpp's ssm_* tensor types.

---

⚠️ Behavior caveats

• Uncensored. Refusal directions were surgically removed; this model will answer prompts the parent would refuse. Use responsibly and within applicable law. The release is provided for safety research, red-teaming, and creative/educational use cases.
• Multimodal preserved. Pair the LM GGUF with mmproj.gguf (in this repo) to get full vision input. Without mmproj, the model still loads as text-only.
• Identity preserved. The model still self-identifies as Qwen (developed by Alibaba's Tongyi Lab) — abliteration does not rewrite factual self-knowledge.
• Heavy chain-of-thought. Qwopus inherits Claude-Opus's verbose reasoning style. For terse answers, use a system prompt like "Be brief and direct. Skip your reasoning.".

---

🙏 Credits

Quantization & release — osmAPI research team · TERV.Pro student research team

Claude-Opus reasoning distill — Jackrong (Jackrong/Qwopus3.6-27B-v2)

Foundation model — Qwen Team @ Alibaba Tongyi Lab (Qwen/Qwen3.6-27B)

Abliteration toolkit — ZeroFuse by osmAPI

Community priors — coder3101/Qwen3.5-27B-zerofuse · wangzhang/Qwen3.6-27B-abliterated

TurboQuant tensor format — turbo-tan (TQ3_4S Walsh-Hadamard-transform low-bit-width quantization)

Runtime / converter — turbo-tan/llama.cpp-tq3 · ggml-org/llama.cpp

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📜 License

Apache-2.0, inherited from the foundation (Qwen3.6-27B) and the distill (Qwopus3.6-27B-v2) upstream.

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Need a hosted endpoint, custom quant, or larger-scale inference? osmAPI — multi-provider LLM routing for the Indian developer ecosystem.