Instructions to use TeichAI/Qwen3.6-27B-Fable-5-Experimental-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 TeichAI/Qwen3.6-27B-Fable-5-Experimental-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 TeichAI/Qwen3.6-27B-Fable-5-Experimental-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TeichAI/Qwen3.6-27B-Fable-5-Experimental-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 TeichAI/Qwen3.6-27B-Fable-5-Experimental-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TeichAI/Qwen3.6-27B-Fable-5-Experimental-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 TeichAI/Qwen3.6-27B-Fable-5-Experimental-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf TeichAI/Qwen3.6-27B-Fable-5-Experimental-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 TeichAI/Qwen3.6-27B-Fable-5-Experimental-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TeichAI/Qwen3.6-27B-Fable-5-Experimental-GGUF:Q4_K_M

Use Docker

docker model run hf.co/TeichAI/Qwen3.6-27B-Fable-5-Experimental-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use TeichAI/Qwen3.6-27B-Fable-5-Experimental-GGUF with Ollama:
```
ollama run hf.co/TeichAI/Qwen3.6-27B-Fable-5-Experimental-GGUF:Q4_K_M
```

Unsloth Studio

How to use TeichAI/Qwen3.6-27B-Fable-5-Experimental-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 TeichAI/Qwen3.6-27B-Fable-5-Experimental-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 TeichAI/Qwen3.6-27B-Fable-5-Experimental-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for TeichAI/Qwen3.6-27B-Fable-5-Experimental-GGUF to start chatting

How to use TeichAI/Qwen3.6-27B-Fable-5-Experimental-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TeichAI/Qwen3.6-27B-Fable-5-Experimental-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": "TeichAI/Qwen3.6-27B-Fable-5-Experimental-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use TeichAI/Qwen3.6-27B-Fable-5-Experimental-GGUF with Docker Model Runner:
```
docker model run hf.co/TeichAI/Qwen3.6-27B-Fable-5-Experimental-GGUF:Q4_K_M
```

Lemonade

How to use TeichAI/Qwen3.6-27B-Fable-5-Experimental-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TeichAI/Qwen3.6-27B-Fable-5-Experimental-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-27B-Fable-5-Experimental-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use TeichAI/Qwen3.6-27B-Fable-5-Experimental-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 TeichAI/Qwen3.6-27B-Fable-5-Experimental-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 TeichAI/Qwen3.6-27B-Fable-5-Experimental-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use TeichAI/Qwen3.6-27B-Fable-5-Experimental-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TeichAI/Qwen3.6-27B-Fable-5-Experimental-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 "TeichAI/Qwen3.6-27B-Fable-5-Experimental-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"

Qwen3.6-27B-Fable-5-Experimental-GGUF / README.md

armand0e

Update README.md

7ff17c6 verified 2 months ago

preview code

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metadata

base_model: TeichAI/Qwen3.6-27B-Fable-5-Experimental
tags:
  - gguf
  - llama.cpp
  - unsloth
  - qwen3_5
license: apache-2.0
language:
  - en
datasets:
  - armand0e/claude-fable-5-claude-code

Qwen 3.6 27B - Claude Fable 5 (Experimental)

Update · 6/22/2026
GGUFs re-done with MTP built in.
llama.cpp MTP args: --spec-type draft-mtp --spec-draft-n-max 3

Heres Qwen3.6 slightly over-trained on a very small dataset of fable 5 traces.

Had to use aggressive settings for the style transfer here due to the data contraints. Overall the model seems to be a better planner now and better at 3D modeling in three.js, making small games, and ML engineering.

Either way give it a shot, it really does look, talk, and plan like fable... but like all fine-tunes it comes with it's limitations.

Hopefully we can get some more well rounded data from the rest of the community to a do a much less aggressive tune on a larger dataset.

Reasoning was left untouched

Benchmarks

As always big thanks to @nightmedia for the speedy benchmarks.

                                 arc     arc/e	boolq
Qwen3.6-27B-Fable-5-Experimental 0.650	0.813	0.909
                     Qwen3.6-27B	0.637	0.798	0.911

Not really a benchmark but here's a procedurally generated duck that it zero-shotted lol (quant: q3_k_s)

The data for this model was easily extracted, formatted, and masked for training with Teich

This model was trained 2x faster with Unsloth and Huggingface's TRL library.