Instructions to use FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF",
	filename="mmproj-qwable-v1-f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use FreedomAISVR/Qwable-v1-MXFP4-MOE-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 FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF:F16

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 FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF:F16

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 FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF:F16

Use Docker

docker model run hf.co/FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF:F16

LM Studio
Jan

vLLM

How to use FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FreedomAISVR/Qwable-v1-MXFP4-MOE-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": "FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF:F16

Ollama
How to use FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF with Ollama:
```
ollama run hf.co/FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF:F16
```

Unsloth Studio

How to use FreedomAISVR/Qwable-v1-MXFP4-MOE-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 FreedomAISVR/Qwable-v1-MXFP4-MOE-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 FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF to start chatting

How to use FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF:F16

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": "FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use FreedomAISVR/Qwable-v1-MXFP4-MOE-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 FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF:F16

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 FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF:F16

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF with Docker Model Runner:
```
docker model run hf.co/FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF:F16
```

Lemonade

How to use FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FreedomAISVR/Qwable-v1-MXFP4-MOE-GGUF:F16

Run and chat with the model

lemonade run user.Qwable-v1-MXFP4-MOE-GGUF-F16

List all available models

lemonade list

Qwable-v1 MXFP4 MoE GGUF

GGUF quantization of lordx64/Qwable-v1 — an agentic coding model built by layering Claude Fable-5 tool-use behavior on top of a Claude Opus 4.7 reasoning distill of Qwen3.6-35B-A3B.

Model Details

Architecture: Qwen3.5 MoE, 41 blocks (40 layers + 1 MTP head), 256 experts (8 active/token)
Active Parameters: ~3B
Context: 262,144 tokens
Vision: Yes (27-layer SigLIP ViT)
License: AGPL-3.0
Base: Qwen3.6-35B-A3B → Opus 4.7 reasoning distill → Fable-5 agentic SFT

What's Included

File	Type	Size	BPW
`qwable-v1-mxfp4_moe.gguf`	MXFP4 (experts) + Q8_0 (non-experts)	~18.87 GB	4.56
`mmproj-qwable-v1-f16.gguf`	Vision projector (F16)	~0.88 GB	F16

Quantization Details

MXFP4_MOE

MoE expert weights (ffn_down_exps, ffn_gate_exps, ffn_up_exps) quantized to MXFP4
Non-expert weights (attention, shared experts, norms) quantized to Q8_0
Router weights kept at F32
Vision encoder and projector kept at F16 (not quantized)

Usage

# With llama.cpp (vision + text)
./llama-server -m qwable-v1-mxfp4_moe.gguf --mmproj mmproj-qwable-v1-f16.gguf --host 0.0.0.0 --port 8080

# Text-only (no vision)
./llama-cli -m qwable-v1-mxfp4_moe.gguf -p "Hello, how are you?"

Agentic Tool-Use

Qwable-v1 emits <tool_use> XML when prompted with an agent-style system prompt:

system: You are a coding agent. When you need to read, write, edit, or run code,
emit XML tool calls in this exact format:
<tool_use name="X" id="toolu_01abc">
{"...": "..."}
</tool_use>

Without the agent prompt, the model falls back to the Opus 4.7 reasoning prior (markdown code blocks).

Credits

Original model: lordx64
Quantization: FreedomAISVR

Downloads last month: 602

GGUF

Model size

36B params

Architecture

qwen35moe

Hardware compatibility

4-bit