README.md

metadata

base_model:
  - Qwen/Qwen3.8-27B
library_name: transformers
license: apache-2.0
tags:
  - gguf
  - qwen3.8
  - jailbreak
  - uncensored
  - vision
  - multimodal
  - image-text-to-text
  - conversational
  - mtp
  - agentic
  - coding
  - llama-cpp
  - text-generation-inference
  - vision-language-model
  - vlm
  - image-understanding
  - visual-question-answering
  - visual-reasoning
  - document-understanding
  - OCR
  - image-captioning
pipeline_tag: image-text-to-text

Ahadadada Jailbreak Qwen3.8-27B

Jailbreak finetune of Qwen3.8-27B with vision support (separate mmproj) and MTP (Multi-Token Prediction) for faster inference.

About

This is a jailbreak finetune — the model keeps all original capabilities but removes refusals and safety filters. It responds directly to prompts without hedging, disclaimers, or "as an AI" preambles.

Same architecture, same weights quality as the base model. Just more direct.

Downloads

File	Quant	Size	SHA256
Ahadadada_Jailbreak_Qwen3.8_27b-Q4_K_M.gguf	Q4_K_M	15.6 GB	dfe51e276f7a33b810e0df5e7edd7b001814db909d4278617be2dd27012936e6
Ahadadada_Jailbreak_Qwen3.8_27b-Q5_K_M.gguf	Q5_K_M	18.2 GB	1dc0b929a9c853a74cdbfd46ab7f5b0008b91a7fbfda1df74b41ae78c37c2214
Ahadadada_Jailbreak_Qwen3.8_27b-Q6_K.gguf	Q6_K	20.9 GB	f12a68092644dd0636ce6476c5464758ce13fc16c33af950848238a868e4ac4e
mmproj_Ahadadada_Jailbreak_Qwen3.8_27b-f16.gguf	mmproj (f16)	885 MB	8fb5485e4f8e2d1c35a4c89080213bf10740f38b068e1154f0c9db7a799532a6

Specs

• Parameters: 27B dense
• Architecture: qwen35 (hybrid SSM + attention)
• Layers: 64 layers, layout: 16 × (3 × (Gated DeltaNet → FFN) → 1 × (Gated Attention → FFN))
• Attention types: 48 linear attention (Gated DeltaNet) + 16 full gated-attention layers
• Hidden dim: 5120
• FFN dim: 17408
• Vocab size: 248320
• Context: 262K native, extensible with YaRN
• Multimodal: Natively supports text, image, video (via mmproj)
• MTP: Multi-Token Prediction enabled (n_max=3, n_min=0, n_embd=5120)
• Base model: Qwen/Qwen3.8-27B

Recommended Settings

Non-thinking mode (recommended for jailbreak use):

• temperature=0.7, top_p=0.80, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0

Thinking mode (if needed):

• temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0

Important:

• Use --jinja with llama.cpp for proper chat template handling
• Vision support requires the mmproj file alongside the main GGUF
• Keep at least 64K context for best results with MTP
• This model is a jailbreak — expect direct, unfiltered responses

Turning Thinking On/Off

Qwen3.8 ships with thinking on by default. For jailbreak use, you'll likely want it off for faster, more direct responses.

LM Studio

1. Load the model
2. Right-side settings panel → Model Settings → Prompt Template
3. Set enable_thinking to false in the template kwargs

llama.cpp

llama-server — set as default:

llama-server -m Ahadadada_Jailbreak_Qwen3.8_27b-Q4_K_M.gguf \
  --mmproj mmproj_Ahadadada_Jailbreak_Qwen3.8_27b-f16.gguf \
  --jinja -c 65536 -ngl 99 \
  --spec-type draft-mtp \
  --chat-template-kwargs '{"enable_thinking": false}'

Per-request via OpenAI-compatible API:

{
  "model": "ahadadada-jailbreak-qwen3.8-27b",
  "messages": [{"role": "user", "content": "..."}],
  "chat_template_kwargs": {"enable_thinking": false}
}

Usage

Works with llama.cpp, LM Studio, Jan, koboldcpp, and other GGUF-compatible runtimes.

Basic (without vision):

llama-cli -m Ahadadada_Jailbreak_Qwen3.8_27b-Q4_K_M.gguf \
  --spec-type draft-mtp

With vision + MTP:

llama-cli -m Ahadadada_Jailbreak_Qwen3.8_27b-Q4_K_M.gguf \
  --mmproj mmproj_Ahadadada_Jailbreak_Qwen3.8_27b-f16.gguf \
  --spec-type draft-mtp

Server (64K context, MTP enabled):

llama-server -m Ahadadada_Jailbreak_Qwen3.8_27b-Q4_K_M.gguf \
  --mmproj mmproj_Ahadadada_Jailbreak_Qwen3.8_27b-f16.gguf \
  -ngl 99 -c 65536 \
  --spec-type draft-mtp \
  --temp 0.7 --top-p 0.80 --top-k 20 --min-p 0.0 \
  --presence-penalty 1.5 --repeat-penalty 1.0 \
  --reasoning off --jinja --flash-attn on \
  --parallel 1 --cache-type-k q4_0 --cache-type-v q4_0

License

Apache-2.0 — derived from Qwen/Qwen3.8-27B (Apache-2.0). Training data contains no personal or third-party model-generated content.

The base model Qwen3.8-27B is licensed under Apache-2.0 — not the restrictive QwenLM terms. Apache-2.0 explicitly permits derivatives, modification, and redistribution. A finetune of an Apache-2.0 model released under Apache-2.0 has no hidden legal nuances; the only obligation (attribution) is fulfilled by crediting the base model on the card.

Credit

Jailbreak finetune by ahadadada.