Instructions to use SummonGovernance/Qwable-27B-NVFP4-MTP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SummonGovernance/Qwable-27B-NVFP4-MTP-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SummonGovernance/Qwable-27B-NVFP4-MTP-GGUF",
	filename="Qwable-27B-NVFP4-MTP-Q4_K_M.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 SummonGovernance/Qwable-27B-NVFP4-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 SummonGovernance/Qwable-27B-NVFP4-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SummonGovernance/Qwable-27B-NVFP4-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 SummonGovernance/Qwable-27B-NVFP4-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SummonGovernance/Qwable-27B-NVFP4-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 SummonGovernance/Qwable-27B-NVFP4-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SummonGovernance/Qwable-27B-NVFP4-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 SummonGovernance/Qwable-27B-NVFP4-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SummonGovernance/Qwable-27B-NVFP4-MTP-GGUF:Q4_K_M

Use Docker

docker model run hf.co/SummonGovernance/Qwable-27B-NVFP4-MTP-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use SummonGovernance/Qwable-27B-NVFP4-MTP-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SummonGovernance/Qwable-27B-NVFP4-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": "SummonGovernance/Qwable-27B-NVFP4-MTP-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SummonGovernance/Qwable-27B-NVFP4-MTP-GGUF:Q4_K_M

Ollama
How to use SummonGovernance/Qwable-27B-NVFP4-MTP-GGUF with Ollama:
```
ollama run hf.co/SummonGovernance/Qwable-27B-NVFP4-MTP-GGUF:Q4_K_M
```

Unsloth Studio

How to use SummonGovernance/Qwable-27B-NVFP4-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 SummonGovernance/Qwable-27B-NVFP4-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 SummonGovernance/Qwable-27B-NVFP4-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 SummonGovernance/Qwable-27B-NVFP4-MTP-GGUF to start chatting

How to use SummonGovernance/Qwable-27B-NVFP4-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 SummonGovernance/Qwable-27B-NVFP4-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": "SummonGovernance/Qwable-27B-NVFP4-MTP-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use SummonGovernance/Qwable-27B-NVFP4-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 SummonGovernance/Qwable-27B-NVFP4-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 SummonGovernance/Qwable-27B-NVFP4-MTP-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use SummonGovernance/Qwable-27B-NVFP4-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/SummonGovernance/Qwable-27B-NVFP4-MTP-GGUF:Q4_K_M
```

Lemonade

How to use SummonGovernance/Qwable-27B-NVFP4-MTP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SummonGovernance/Qwable-27B-NVFP4-MTP-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwable-27B-NVFP4-MTP-GGUF-Q4_K_M

List all available models

lemonade list

Qwable 27B NVFP4 MTP GGUF

Unofficial NVFP4 MTP GGUF conversion of Mia-AiLab/Qwable-3.6-27b.

The upstream Qwable model is a Qwen3.6 27B fine-tune focused on structured instruction following, coding, and reasoning-style assistant behavior. The upstream model card states that MTP is disabled/not present in the original checkpoint.

This build keeps Qwable as the 64-block base model and grafts a compatible Qwen3.6 27B MTP sidecar as block 64, then converts eligible tensors to NVFP4.

File

File	Source lineage	Size
`Qwable-27B-NVFP4-MTP-Q4_K_M.gguf`	Qwable `Q4_K_M` base + compatible 27B MTP donor	15,388,070,176 bytes

Q4_K_M describes the source GGUF lineage. The file itself is an NVFP4 GGUF.

Conversion Summary

Base source:

Qwable-27b_Q4_K_M.gguf

MTP donor:

27B_MTP.gguf

Base source before graft:

qwen35.block_count = 64
qwen35.nextn_predict_layers = <absent>
mtp_tensors = 0

MTP donor:

qwen35.block_count = 65
qwen35.nextn_predict_layers = 1
mtp_tensors = 15

Converted:

433 Q4_K tensors -> NVFP4
65 Q6_K tensors  -> NVFP4
8 Q8_0 tensors   -> NVFP4
360 F32 tensors  -> F32

Validation

Final artifact:

tensor_count = 866
NVFP4 = 506
F32 = 360
qwen35.block_count = 65
qwen35.nextn_predict_layers = 1
mtp_tensors = 15
general.file_type = 39

Confirmed MTP tensor examples:

blk.64.nextn.eh_proj.weight = NVFP4
blk.64.ffn_down.weight      = NVFP4
blk.64.ffn_gate.weight      = NVFP4
blk.64.ffn_up.weight        = NVFP4
blk.64.attn_k.weight        = NVFP4

Attribution

Primary model lineage:

Mia-AiLab/Qwable-3.6-27b

The upstream Qwable repository is marked MIT. Users are responsible for also following the terms of the underlying Qwen/Qwen3.6 lineage and any other applicable upstream components.

Downloads last month: 1,330

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for SummonGovernance/Qwable-27B-NVFP4-MTP-GGUF

Base model

Qwen/Qwen3.6-27B

Finetuned

unsloth/Qwen3.6-27B

Quantized

Mia-AiLab/Qwable-3.6-27b

Quantized

(5)

this model