Instructions to use hotdogs/qwen27b-abliterated-Fable-MTP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hotdogs/qwen27b-abliterated-Fable-MTP with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="hotdogs/qwen27b-abliterated-Fable-MTP")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("hotdogs/qwen27b-abliterated-Fable-MTP", device_map="auto")

HERMES

How to use hotdogs/qwen27b-abliterated-Fable-MTP with HERMES:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

PEFT
How to use hotdogs/qwen27b-abliterated-Fable-MTP with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use hotdogs/qwen27b-abliterated-Fable-MTP 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 hotdogs/qwen27b-abliterated-Fable-MTP:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf hotdogs/qwen27b-abliterated-Fable-MTP:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf hotdogs/qwen27b-abliterated-Fable-MTP:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf hotdogs/qwen27b-abliterated-Fable-MTP: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 hotdogs/qwen27b-abliterated-Fable-MTP:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf hotdogs/qwen27b-abliterated-Fable-MTP: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 hotdogs/qwen27b-abliterated-Fable-MTP:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf hotdogs/qwen27b-abliterated-Fable-MTP:Q4_K_M

Use Docker

docker model run hf.co/hotdogs/qwen27b-abliterated-Fable-MTP:Q4_K_M

LM Studio
Jan

vLLM

How to use hotdogs/qwen27b-abliterated-Fable-MTP with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hotdogs/qwen27b-abliterated-Fable-MTP"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hotdogs/qwen27b-abliterated-Fable-MTP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hotdogs/qwen27b-abliterated-Fable-MTP:Q4_K_M

SGLang

How to use hotdogs/qwen27b-abliterated-Fable-MTP with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "hotdogs/qwen27b-abliterated-Fable-MTP" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hotdogs/qwen27b-abliterated-Fable-MTP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "hotdogs/qwen27b-abliterated-Fable-MTP" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hotdogs/qwen27b-abliterated-Fable-MTP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use hotdogs/qwen27b-abliterated-Fable-MTP with Ollama:
```
ollama run hf.co/hotdogs/qwen27b-abliterated-Fable-MTP:Q4_K_M
```
Unsloth Desktop

How to use hotdogs/qwen27b-abliterated-Fable-MTP with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf hotdogs/qwen27b-abliterated-Fable-MTP:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "hotdogs/qwen27b-abliterated-Fable-MTP:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use hotdogs/qwen27b-abliterated-Fable-MTP with Docker Model Runner:
```
docker model run hf.co/hotdogs/qwen27b-abliterated-Fable-MTP:Q4_K_M
```

Lemonade

How to use hotdogs/qwen27b-abliterated-Fable-MTP with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull hotdogs/qwen27b-abliterated-Fable-MTP:Q4_K_M

Run and chat with the model

lemonade run user.qwen27b-abliterated-Fable-MTP-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use hotdogs/qwen27b-abliterated-Fable-MTP with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf hotdogs/qwen27b-abliterated-Fable-MTP: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 hotdogs/qwen27b-abliterated-Fable-MTP:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use hotdogs/qwen27b-abliterated-Fable-MTP with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf hotdogs/qwen27b-abliterated-Fable-MTP: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 "hotdogs/qwen27b-abliterated-Fable-MTP: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"

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

base_model:

🔗 Base Model

This model is built on huihui-ai/Huihui-Qwen3.6-27B-abliterated — the abliterated (uncensored) version of Qwen3.6-27B with all refusal mechanisms removed.

🐉 Qwen3.6-27B-Fable-Abliterated

Abliterated Qwen3.6-27B with Fable-5 reasoning, Claude Opus 4.8 style reasoning, function calling, and cybersecurity knowledge

Last updated: 2026-07-08

📦 Available Models

Folder	Description	Download
`qwen27b-abliterated-Fable-opus4.8/`	Opus 4.8 reasoning SFT	✅
`Qwen3.6-27B-abliterated-Fable-opus4.8-cyber/`	Opus 4.8 + Cyber knowledge	✅

File	Format	Size	Description
`GGUF/...opus4.8-cyber.Q4_K_M.MTP.gguf`	Q4_K_M	16 GB	Recommended
`GGUF/...opus4.8-cyber.F16.MTP.gguf`	F16	54 GB	Full precision
`GGUF/...opus4.8_F16_MTP.gguf`	F16	54 GB	Base (no cyber)
`GGUF/...opus4.8_Q4_K_M_MTP.gguf`	Q4_K_M	16 GB	Base (no cyber)

Qwen3.6-27B (base)
  │
  ├── Huihui-Qwen3.6-27B-abliterated (diff-in-means)
  │
  ├── ORPO v1.2 → v1.10 (10 iterations)
  │   └── Best: v1.9 (margin 0.35)
  │
  └── VER4 (Clean Slate)
      ├── SFT perfect-v1 (3,376 rows, Hermes format)
      ├── ORPO v4.1 (150 pairs)
      ├── Cyber SFT v2 (70K filtered)
      └── 🏆 Opus 4.8 SFT (6,956 rows, Claude-style reasoning)

📚 Dataset

Dataset	Rows	Source	Description
perfect-v1	3,376	Fable-5	Reasoning + Hermes format conversion
perfect-v2	5,376	Fable-5 + hermes-fc	Tool calling + reasoning
fable-opus-reasoning 🔥	6,956	Fable-5 + Opus 4.8	Pure reasoning, no tools

🚀 Usage

llama.cpp (Recommended)

./llama-cli -m GGUF/Qwen3.6-27B-abliterated-Fable-opus4.8-cyber.Q4_K_M.MTP.gguf \
  --temp 0.6 --top-k 25 --top-p 0.9 --min-p 0.1 \
  --repeat-penalty 1.15 --dry-multiplier 0 --dry-sequence-breaker none \
  --ctx-size 65536

Python

from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
    "hotdogs/qwen27b-abliterated-Fable-MTP",
    subfolder="Qwen3.6-27B-abliterated-Fable-opus4.8-cyber"
)

📖 References

Paper / Project	Citation
LoRA	Hu et al. "LoRA: Low-Rank Adaptation of Large Language Models" (ICLR 2022)
ORPO	Hong et al. "ORPO: Monolithic Preference Optimization without Reference Model" (2024)
Hermes FC	NousResearch. "Hermes Function Calling"
Abliteration	Ardila et al. "Refusal in LLMs is mediated by a single direction" (2024)
Fable-5	Glint Research. Multi-step reasoning agent traces
Qwen3.6	Qwen Team. "Qwen3.6: Scaling Open Language Models"

🐛 Known Fixes

Issue	Fix
Model stops at colon (`:`)	`--dry-multiplier 0 --dry-sequence-breaker none`
Infinite correction loops	Fixed in opus4.8-cyber release
MTP tensor missing	All GGUFs now verified (866 tensors)

"From 20 bugs to one clean model. Never give up."

💖 Support / โปรดสนับสนุน

If you find this model useful, please consider supporting my work!
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₿ Bitcoin — BTC:

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Thank you for your support! 🙏✨
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Downloads last month: 283

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

16-bit

Model tree for hotdogs/qwen27b-abliterated-Fable-MTP

Base model

Qwen/Qwen3.6-27B

Finetuned

huihui-ai/Huihui-Qwen3.6-27B-abliterated

Adapter

(6)

this model

Quantizations

1 model

hotdogs
/

qwen27b-abliterated-Fable-MTP

🔗 Base Model

🐉 Qwen3.6-27B-Fable-Abliterated

📦 Available Models

📚 Dataset

🚀 Usage

llama.cpp (Recommended)

Python

📖 References

🐛 Known Fixes

💖 Support / โปรดสนับสนุน

₿ Bitcoin — BTC:

Model tree for hotdogs/qwen27b-abliterated-Fable-MTP

Datasets used to train hotdogs/qwen27b-abliterated-Fable-MTP