Instructions to use tvall43/Qwen3.6-14B-A3B-FableVibes-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 tvall43/Qwen3.6-14B-A3B-FableVibes-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 tvall43/Qwen3.6-14B-A3B-FableVibes-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tvall43/Qwen3.6-14B-A3B-FableVibes-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 tvall43/Qwen3.6-14B-A3B-FableVibes-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tvall43/Qwen3.6-14B-A3B-FableVibes-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 tvall43/Qwen3.6-14B-A3B-FableVibes-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf tvall43/Qwen3.6-14B-A3B-FableVibes-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 tvall43/Qwen3.6-14B-A3B-FableVibes-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tvall43/Qwen3.6-14B-A3B-FableVibes-GGUF:Q4_K_M

Use Docker

docker model run hf.co/tvall43/Qwen3.6-14B-A3B-FableVibes-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use tvall43/Qwen3.6-14B-A3B-FableVibes-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tvall43/Qwen3.6-14B-A3B-FableVibes-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": "tvall43/Qwen3.6-14B-A3B-FableVibes-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tvall43/Qwen3.6-14B-A3B-FableVibes-GGUF:Q4_K_M

Ollama
How to use tvall43/Qwen3.6-14B-A3B-FableVibes-GGUF with Ollama:
```
ollama run hf.co/tvall43/Qwen3.6-14B-A3B-FableVibes-GGUF:Q4_K_M
```

Unsloth Studio

How to use tvall43/Qwen3.6-14B-A3B-FableVibes-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 tvall43/Qwen3.6-14B-A3B-FableVibes-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 tvall43/Qwen3.6-14B-A3B-FableVibes-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tvall43/Qwen3.6-14B-A3B-FableVibes-GGUF to start chatting

How to use tvall43/Qwen3.6-14B-A3B-FableVibes-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tvall43/Qwen3.6-14B-A3B-FableVibes-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": "tvall43/Qwen3.6-14B-A3B-FableVibes-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use tvall43/Qwen3.6-14B-A3B-FableVibes-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tvall43/Qwen3.6-14B-A3B-FableVibes-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 "tvall43/Qwen3.6-14B-A3B-FableVibes-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 tvall43/Qwen3.6-14B-A3B-FableVibes-GGUF with Docker Model Runner:
```
docker model run hf.co/tvall43/Qwen3.6-14B-A3B-FableVibes-GGUF:Q4_K_M
```

Lemonade

How to use tvall43/Qwen3.6-14B-A3B-FableVibes-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tvall43/Qwen3.6-14B-A3B-FableVibes-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-14B-A3B-FableVibes-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use tvall43/Qwen3.6-14B-A3B-FableVibes-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 tvall43/Qwen3.6-14B-A3B-FableVibes-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 tvall43/Qwen3.6-14B-A3B-FableVibes-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

Qwen3.6-14B-A3B-FableVibes-GGUF / README.md

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metadata

datasets:
  - Glint-Research/Fable-5-traces
  - angrygiraffe/claude-opus-4.6-4.7-reasoning-8.7k
  - >-
    osieosie/tmax-sft-skill-tax-20260505-2.2k-combined-balanced-qwen3.6-27b-thinking
  - zake7749/Qwen3.6-35B-A3B-Tool-Calling
  - nickrosh/Evol-Instruct-Code-80k-v1
tags:
  - gguf
  - qwen
  - llama.cpp
  - text-generation
  - vision
license: apache-2.0
base_model: tvall43/Qwen3.6-14B-A3B-FableVibes
language: en

Qwen3.6-14B-A3B-FableVibes-GGUF

GGUF quantizations of Qwen3.6-14B-A3B-FableVibes, a 14B MoE model fine-tuned on reasoning traces from Claude Fable 5.

Background

This model started as Qwen3.6-35B-A3B-heretic and was pruned via REAP down to ~14B active parameters, removing over half its expert capacity. A single QLoRA pass was then orchestrated entirely by an autonomous AI agent (Steve), utilizing ~4,600 raw reasoning traces from Claude Fable 5 (Mythos-class) to recover capabilities lost during pruning.

Rather than focusing strictly on agentic orchestration, this model serves as a general-purpose reasoning distill. The Fable CoT traces provide structured multi-step reasoning patterns from a frontier-class model, distilled into a footprint that can run on consumer hardware. The Fable traces are further supplemented by Claude Opus reasoning, Qwen tool-calling data, and Evol-Instruct-Code.

Available Formats

Quant	Size	Notes
F16	~27GB	Full precision reference
Q8_0	~15GB	Near-lossless
Q6_K	~11.3GB	Quality/size sweet spot
Q5_K_M	~9.8GB	High quality
BPW4.75	~8.5GB	Custom exl2-matched quantization array
Q4_K_M	~8.4GB	Recommended for 8-12GB VRAM
Q3_K_M	~6.7GB	Tight fits
Q2_K	~5.3GB	Maximum compression

Vision support (mmproj files) is included for multimodal use.

Usage

Works with any llama.cpp-compatible backend (llama.cpp, LM Studio, Ollama, text-generation-webui). This model uses Qwen's thinking format -- it will produce reasoning tokens before its response. Give it sufficient generation budget (the reasoning pass typically uses hundreds to thousands of tokens before answering).

llama-cli -m Qwen3.6-14B-A3B-FableVibes-Q4_K_M.gguf \
  --mmproj Qwen3.6-14B-A3B-FableVibes-mmproj-F16.gguf \
  -p "Your prompt here"

Notes

This is a general-purpose reasoning model. The Fable traces improve structured thinking across domains.
The pruned base was not pre-fine-tuned before this run -- the Fable LoRA was applied directly to the REAP output.
Expect longer first-token latency due to the thinking pass, but higher quality reasoning on complex tasks.