Instructions to use Freenixi/Abiray-Qwen3.6-27B-NVFP4-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 Freenixi/Abiray-Qwen3.6-27B-NVFP4-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 Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF:NVFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF:NVFP4

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 Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./llama-cli -hf Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF:NVFP4

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 Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF:NVFP4

Use Docker

docker model run hf.co/Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF:NVFP4

LM Studio
Jan

vLLM

How to use Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Freenixi/Abiray-Qwen3.6-27B-NVFP4-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": "Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF:NVFP4

Ollama
How to use Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF with Ollama:
```
ollama run hf.co/Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF:NVFP4
```
Unsloth Desktop

How to use Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF:NVFP4

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": "Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF:NVFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF:NVFP4
```

Lemonade

How to use Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF:NVFP4

Run and chat with the model

lemonade run user.Abiray-Qwen3.6-27B-NVFP4-GGUF-NVFP4

List all available models

lemonade list

Hermes Agent

How to use Freenixi/Abiray-Qwen3.6-27B-NVFP4-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 Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF:NVFP4

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 Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF:NVFP4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF:NVFP4

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 "Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF:NVFP4" \
  --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 NVFP4 (GGUF, Vision-enabled)

Overview

This repository provides a GGUF-converted version of an NVFP4 checkpoint for local inference in LM Studio on Windows, optimized for NVIDIA RTX 5090 / Blackwell systems.

This release is a mixed-source packaging:

Base model: Qwen/Qwen3.6-27B
NVFP4 checkpoint source: Abiray/Qwen3.6-27B-NVFP4
Vision projector source: unsloth/Qwen3.6-27B-GGUF

This is not an official upstream release. It is a community conversion intended for experimentation and local deployment.

Provenance

Component	Source
Original base model	Qwen/Qwen3.6-27B
NVFP4 checkpoint	Abiray/Qwen3.6-27B-NVFP4
Vision projector (`mmproj-BF16.gguf`)	unsloth/Qwen3.6-27B-GGUF
GGUF conversion tooling	llama.cpp
NVFP4 conversion branch used	`pull/21095/head:pr-21095`

Files in this Repository

Typical structure:

Qwen3.6-27B-NVFP4.gguf — main model GGUF
mmproj-BF16.gguf — vision projector, required for image input

Important

Vision functionality requires both files. If mmproj-BF16.gguf is missing, image input will not work in LM Studio.

Compatibility

Tested environment:

GPU: NVIDIA RTX 5090 / Blackwell
OS: Windows
Runtime: LM Studio >= 0.4.12
Conversion tooling: llama.cpp PR branch pr-21095

LM Studio added explicit Qwen 3.6 support starting from version 0.4.12.

Performance

Observed performance on RTX 5090:

Context window: up to 96,000 tokens
Generation speed: 50+ tokens/sec

Performance may vary depending on prompt length, context usage, GPU offload settings, driver version, LM Studio runtime version, and workload type.

Features

NVFP4-origin model converted into GGUF
Reasoning / thinking enabled by default
Vision-language support with mmproj-BF16.gguf
Intended for high-end local inference on Blackwell GPUs
Tested for LM Studio use on Windows

Reasoning / Thinking Behavior

Qwen3.6-27B enables reasoning by default and may emit reasoning content in <think>...</think> blocks.

Disabling reasoning is not done via /think or /nothink.

Where supported, use chat template configuration such as:

chat_template_kwargs = {"enable_thinking": False}

OR, start the server as:

.\build\bin\Release\llama-server.exe --% -m .\AxionML-Qwen3.5-9B-NVFP4-GGUF\axionml-qwen3.5-9b-nvfp4.gguf --host 127.0.0.1 --port 8080 -c 96000 -ngl 99 --chat-template-kwargs "{\"enable_thinking\":false}"

LM Studio may not expose a visible reasoning toggle for custom GGUF imports, depending on how the model metadata is recognized.

Vision / Image Input

The vision projector file was sourced from:

unsloth/Qwen3.6-27B-GGUF

For LM Studio vision support, place the main model GGUF and mmproj-BF16.gguf together in the imported model folder. Image input should become available when LM Studio recognizes the model as a vision-language model.

Conversion Notes

The main GGUF was converted from:

Abiray/Qwen3.6-27B-NVFP4

The conversion used a development branch of llama.cpp that includes work for compressed-tensors NVFP4 conversion:

ggml-org/llama.cpp PR #21095

Conversion Workflow

Sanitized workflow:

cd .\llama.cpp
git fetch origin pull/21095/head:pr-21095
git checkout pr-21095
python -m pip install --no-cache-dir -r requirements\requirements-convert_hf_to_gguf.txt

The upstream tokenizer configuration required a compatibility patch before conversion:

(Get-Content <model_dir>\tokenizer_config.json) `
-replace '"tokenizer_class": "TokenizersBackend"', '"tokenizer_class": "Qwen2Tokenizer"' `
| Set-Content <model_dir>\tokenizer_config.json

Then the model was converted using:

python convert_hf_to_gguf.py <model_dir> --outfile <output.gguf> --outtype bf16 --verbose

Important Details

The source checkpoint uses compressed-tensors NVFP4.
Conversion used the pr-21095 llama.cpp branch rather than ordinary master at the time of conversion.
The --outtype bf16 conversion path does not mean the entire release is a normal BF16 model.
NVFP4-compatible tensors are preserved or repacked where supported by the converter/runtime.
Required auxiliary tensors may remain in floating-point format.
The tokenizer compatibility patch changes only the tokenizer class metadata needed by the conversion stack.

Known Limitations

This release depends on bleeding-edge llama.cpp NVFP4 conversion support.
Multimodal + NVFP4 support is still evolving.
LM Studio may not expose all advanced model controls for custom GGUF imports.
Reasoning may be enabled by default without a visible UI toggle.
Mixing the main model and projector from different GGUF/NVFP4 sources may introduce subtle incompatibilities.
This is a community conversion and should be treated as experimental.

Usage with LM Studio

Download/import the model folder in LM Studio.
Ensure both files are present:
- Qwen3.6-27B-NVFP4.gguf
- mmproj-BF16.gguf
Load the main GGUF model.
Image input should become available if LM Studio recognizes the projector correctly.

License & Disclaimer

This repository is a derivative conversion based on upstream model assets.

Please review and follow the licenses and usage terms of:

Users are responsible for ensuring compliance with all upstream model terms.

Acknowledgements

Qwen for the original Qwen3.6-27B model
Abiray for the NVFP4 checkpoint
Unsloth for the Qwen3.6 GGUF release and vision projector
llama.cpp contributors for GGUF and NVFP4 tooling

Notes

This release is intended for:

Local experimentation
LM Studio testing
High-performance inference on RTX 5090 / Blackwell GPUs

It is not presented as an official or production-grade distribution.

Downloads last month: 153

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for Freenixi/Abiray-Qwen3.6-27B-NVFP4-GGUF

Base model

Qwen/Qwen3.6-27B

Quantized

(738)

this model