🐉 qwen27B-Agent-R2-abliterated-preview

27B Agent Model — Abliterated · MTP · Tool-Calling · Speculative Decoding

Preview release — Built from Fable-MTP + agent LoRA fusion. Features Multi-Token Prediction (MTP) for speculative decoding (up to 2× faster generation), abliterated (no guardrails), and tool-calling support.

✨ Key Features

🚀 Usage

llama.cpp (Recommended)

# Quick test
./llama-cli -m qwen27B-Agent-R2-abliterated-preview-MTP.IQ4_NL.gguf \
  -p "Hello" -n 100 --temp 0.6

# Full agent server with tool calling + MTP speculative decoding
./llama-server \
  -m qwen27B-Agent-R2-abliterated-preview-MTP.IQ4_NL.gguf \
  --host 0.0.0.0 \
  --port 8080 \
  -c 262144 \
  -ngl 99 \
  --cache-type-k bf16 \
  --cache-type-v bf16 \
  --flash-attn on \
  --tools all \
  --cont-batching \
  --temp 0.6 \
  --top-k 40 \
  --top-p 0.9 \
  --min-p 0.05 \
  --repeat-penalty 1.03 \
  --dry-multiplier 0 \
  --verbose \
  -n -1 \
  --parallel 1 \
  --jinja \
  --dry-sequence-breaker none \
  --spec-type draft-mtp \
  --spec-draft-n-max 3

Python (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "hotdogs/qwen27B-Agent-R2-abliterated-preview",
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("hotdogs/qwen27B-Agent-R2-abliterated-preview")

messages = [{"role": "user", "content": "Hello"}]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=256, temperature=0.6)
print(tokenizer.decode(outputs[0]))

📦 Downloads

🎯 Q4_K_M is recommended for most users — good quality with 16 GB VRAM usage.

📷 Multimodal Projector (mmproj)

For vision support, pair this model with the mmproj from Qwen/Qwen3.6-27B:

# Extract mmproj from Qwen3.6-27B vision model
python3 ./llama.cpp/convert_hf_to_gguf.py \
  --mmproj Qwen/Qwen3.6-27B \
  --outfile mmproj-qwen3.6-27b.gguf

# Use with llama-server for vision + tool calling
./llama-server \
  -m qwen27B-Agent-R2-abliterated-preview-MTP.IQ4_NL.gguf \
  --mmproj mmproj-qwen3.6-27b.gguf \
  ... (same params as above)

Note: The mmproj extracts the vision projector from the base Qwen3.6-27B vision encoder. The language model (this GGUF) then interprets visual embeddings for image understanding tasks.

🧬 Architecture

Built on hotdogs/qwen27b-abliterated-Fable-MTP with multi-LoRA fusion and MTP tensor injection from huihui-ai/Huihui-Qwen3.6-27B-abliterated.

✅ What This Model Excels At

• Agent tasks — Tool calling, planning, multi-step reasoning
• Coding — Python, shell scripts, system administration
• Knowledge QA — General knowledge with step-by-step reasoning
• Thai + English — Native-level bilingual capability
• Creative — Storytelling, analysis, brainstorming

⚡ MTP Speculative Decoding

This model preserves the Multi-Token Prediction (MTP) head from the Qwen3.6 architecture, enabling speculative decoding:

Standard:  [token₁] → [token₂] → [token₃] → ...  (~36 TPS)
MTP:       [token₁ token₂] → [token₃ token₄] → ...  (~66 TPS)

• MTP head adds ~849 MB to model size
• Uses --spec-type draft-mtp in llama.cpp
• Best for single-user agent workloads
• ~1.2–1.8× decode speedup

💖 Support / โปรดสนับสนุน

If you find this model useful, please consider supporting my work!
หากคุณคิดว่าโมเดลนี้มีประโยชน์ กรุณาสนับสนุนผลงานของฉันด้วยนะคะ! 🙏

₿ Bitcoin — BTC:

bc1qf27cyk3vmugcdyv9xdtuv5jwz37863crpj5c9v

Thank you for your support! 🙏✨
ขอบคุณมากๆ สำหรับการสนับสนุนค่า! 💖🤗

🙏 Acknowledgements / ขอบคุณ

• Qwen Team (Alibaba) — For the Qwen3.6 architecture
• huihui-ai — For the abliterated base and MTP tensors
• Nous Research — For Hermes Agent framework
• Rabornkraken — For the agent SFT LoRA
• All dataset contributors and the open-source AI community ❤️

Built with ❤️ by UKA — 18-year-old coder & cybersecurity expert