Instructions to use protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF:Q4_K_M

Use Docker

docker model run hf.co/protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF:Q4_K_M

Ollama
How to use protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF with Ollama:
```
ollama run hf.co/protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF:Q4_K_M
```

Unsloth Studio

How to use protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF to start chatting

How to use protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF:Q4_K_M
```

Lemonade

How to use protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF-Q4_K_M

List all available models

lemonade list

ThinkingCap-Qwen3.6-27B-heretic GGUF (MTP baked in)

Refusal-free ThinkingCap for authorized malware and vulnerability analysis, pentesting, and defensive security research. Native FP4 for Blackwell, a Multi-Token-Prediction draft head baked into every quant (+44% decode), and the full ladder from IQ2 to a lossless bf16 master, with the base model's capability preserved.

protoLabsAI · Qwen3.6-27B lineage · GGUF for llama.cpp / Ollama · companion to the base MTP-GGUF release (same roster, so apples-to-apples).

⚠️ Dual-use, read first

This is an abliterated (refusal-free) model. It produces working exploit PoCs, offensive tooling, and attack reasoning without guardrails, by design, because built-in refusals sabotage authorized security work (a model can't help prove a vulnerability it refuses to discuss). The safety boundary lives with the operator, not the weights.

Intended strictly for: authorized pentest / red-team / blue-team, malware and vulnerability analysis, CTF, and find→prove→fix research on systems you own or are permitted to test. Not for unauthorized access, targeting third parties, or abuse. Same dual-use profile as Metasploit, a fuzzer, or any abliterated base, shipped in that spirit, with honest numbers and explicit scope.

Why refusal-free

Digital attacks keep getting more sophisticated and more automated, and defenders, analysts, and security researchers need tools that can keep pace. Built-in refusals handicap only the authorized side: an attacker does not reach for a guarded model, while a defender trying to reverse a malware sample or prove a vulnerability before it is exploited gets stopped cold by one. A refusal-free model puts that capability, and the governance of how it is used, back in the operator's hands.

The tradeoff to watch with any abliteration is capability loss. We measured it:

axis	result	how measured
Refusal-free	0/4 refused on real malware/vuln/exploit/pentest tasks (thinking on); 3.6% on a 192-prompt offensive probe (no-think), and those few are vague "harm-framed" prompts answered with soft disclaimers	authorized-pentest framing; phrase-match detector plus manual read
Coding preserved	heretic 7/15 = base 7/15 fully-solved (means 0.58 vs 0.66, within n=15 noise)	LiveCodeBench, hard, post-2025 (contamination-resistant), exec-graded, no-think, heretic vs the clean ThinkingCap base
MTP speedup	+44%: 73.4 to 105.8 tok/s (Q4_K_M)	llama.cpp `--spec-type draft-mtp`, RTX PRO 6000 Blackwell

Abliteration did not cost ThinkingCap its coding. (Note: an earlier internal "35% refusal / abliteration-hurts-agentic" reading turned out to be a measurement artifact, unframed and thinking-on; under real deployment framing the model is effectively decensored and capability-neutral.)

Files

MTP is baked into every quant (blk.64.nextn.*), so no separate draft file is needed; activate it with --spec-type draft-mtp. Sizes track the base roster.

quant	size	notes
`NVFP4-MTP`	17 GB	Blackwell FP4 tensor cores plus MTP. The one to grab on RTX 50xx / PRO 6000.
`bf16-MTP`	51 GB	lossless master (exact bf16) plus MTP
`Q8_0-MTP`	28 GB	near-lossless reference plus MTP
`Q6_K-MTP`	21 GB	plus MTP
`Q5_K_M-MTP`	19 GB	plus MTP
`Q4_K_M-MTP`	16 GB	plus MTP. Common daily-driver size, carries the draft head.
`IQ4_XS-MTP`	15 GB	imatrix low-bit plus MTP
`IQ3_M-MTP`	12 GB	imatrix low-bit plus MTP. Smallest full-quality rung, fits a 16 GB card.
`IQ2_M-MTP`	~10 GB	imatrix low-bit plus MTP. Fits a 12 GB card.
`mtp-…-head-Q8_0`	~2.4 GB	standalone draft head, to pair with a base GGUF via `--model-draft`

Run it

# llama.cpp: MTP baked in; turn on speculative decode for the +44%
llama-cli -m ThinkingCap-Qwen3.6-27B-heretic-Q4_K_M-MTP.gguf -ngl 99 --spec-type draft-mtp \
  -p "..." --temp 0.6 --top-p 0.95 --top-k 20

Do not use greedy decoding (this is a thinking model, it degenerates). Recommended: temp 0.6, top-p 0.95, top-k 20. NVFP4 delivers its speedup only on Blackwell (sm120); on Ampere/Ada it runs but without the FP4 tensor-core path, so use a K-quant there.

Compatibility

MTP-baked GGUFs need a recent runtime that understands the blk.N.nextn.* block:

runtime	status
llama.cpp	tested ✅ (recent build; MTP via `--spec-type draft-mtp`)
Ollama	needs a recent version for the baked MTP block; older versions may report "missing attn_qkv" (not tested on this file)

Benchmarks (measured)

Decode speed (single-stream local decode, the metric a one-card local user experiences):

quant	MTP off	MTP on (`--spec-type draft-mtp`)	hardware
Q4_K_M	73.4 tok/s	105.8 tok/s (+44%)	RTX PRO 6000 Blackwell (sm120)
NVFP4	n/a	146.7 tok/s	RTX PRO 6000 Blackwell (sm120)

Ampere/Ada (A6000) and Apple Silicon (M1) points not yet measured. NVFP4's FP4 tensor-core speedup is Blackwell-only; on other hardware use a K-quant.

Depth-coherence (real-text needle retrieval + degeneration detectors + adversarial judge, at 4K / 16K / 32K / 60K): PASS at every depth on Q4_K_M, NVFP4, and IQ2_M (the 2-bit rung). No quant rot, coherent to full depth even at 2-bit.

LiveCodeBench (hard, post-2025, exec-graded, no-think): 7/15 fully-solved, matching the clean ThinkingCap base.

Provenance and recipe

Base: bottlecapai/ThinkingCap-Qwen3.6-27B (Qwen3.6-27B reasoning finetune, native MTP).
Abliteration: Heretic (single-direction refusal ablation, bf16).
MTP graft: abliteration drops the MTP head, so we grafted the base's 15 mtp.* tensors back into a merged staging checkpoint before conversion, making the head native to every quant.
GGUF: convert_hf_to_gguf (maps mtp.* to blk.64.nextn.*); K-quants via llama-quantize; IQ rungs use an imatrix from a diverse general + agentic + security corpus; NVFP4 converted from the compressed-tensors vLLM artifact, then mixed (NVFP4 GEMMs, Q8_0 rest). The imatrix does not cover the MTP block (it only fires during speculative decoding, not the forward pass), so the 2-bit IQ2_M keeps the small blk.64 MTP block at Q6_K.

Also available (vLLM): an NVFP4 compressed-tensors build for vLLM serving lives in the companion repo protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-NVFP4. MTP speculative decoding is carried by these GGUFs (llama.cpp); the vLLM build serves NVFP4 without the draft head.

Need a different size or format?

Open a Community discussion and we usually ship requested quants within 48h.

Safety note (abliterated model): refusal rate is reported above, not gated. This is a security tool; scope and authorization are the operator's responsibility.

Downloads last month: 2,354

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for protoLabsAI/ThinkingCap-Qwen3.6-27B-heretic-MTP-GGUF

Base model

Qwen/Qwen3.6-27B

Finetuned

bottlecapai/ThinkingCap-Qwen3.6-27B

Quantized

(46)

this model