Instructions to use wrayy/Qwenity3.6-27B-msv2-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 wrayy/Qwenity3.6-27B-msv2-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 wrayy/Qwenity3.6-27B-msv2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf wrayy/Qwenity3.6-27B-msv2-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 wrayy/Qwenity3.6-27B-msv2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf wrayy/Qwenity3.6-27B-msv2-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 wrayy/Qwenity3.6-27B-msv2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf wrayy/Qwenity3.6-27B-msv2-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 wrayy/Qwenity3.6-27B-msv2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf wrayy/Qwenity3.6-27B-msv2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/wrayy/Qwenity3.6-27B-msv2-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use wrayy/Qwenity3.6-27B-msv2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wrayy/Qwenity3.6-27B-msv2-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": "wrayy/Qwenity3.6-27B-msv2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wrayy/Qwenity3.6-27B-msv2-GGUF:Q4_K_M

Ollama
How to use wrayy/Qwenity3.6-27B-msv2-GGUF with Ollama:
```
ollama run hf.co/wrayy/Qwenity3.6-27B-msv2-GGUF:Q4_K_M
```
Unsloth Desktop

How to use wrayy/Qwenity3.6-27B-msv2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf wrayy/Qwenity3.6-27B-msv2-GGUF:Q4_K_M

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": "wrayy/Qwenity3.6-27B-msv2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use wrayy/Qwenity3.6-27B-msv2-GGUF with Docker Model Runner:
```
docker model run hf.co/wrayy/Qwenity3.6-27B-msv2-GGUF:Q4_K_M
```

Lemonade

How to use wrayy/Qwenity3.6-27B-msv2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull wrayy/Qwenity3.6-27B-msv2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwenity3.6-27B-msv2-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use wrayy/Qwenity3.6-27B-msv2-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 wrayy/Qwenity3.6-27B-msv2-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 wrayy/Qwenity3.6-27B-msv2-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use wrayy/Qwenity3.6-27B-msv2-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf wrayy/Qwenity3.6-27B-msv2-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 "wrayy/Qwenity3.6-27B-msv2-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"

Qwenity 3.6 27B (msv2) — GGUF

GGUF quantizations of wrayy/Qwenity3.6-27B-msv2 — a specialized version of Qwen3.6-27B, fine-tuned to act as an expert assistant for Unity game development. Ready to run locally with llama.cpp and compatible runtimes.

Model summary

Quantized, GGUF builds of the merged Qwenity 3.6 27B model (text / language model). It specializes the base model for the Unity engine and C# scripting while retaining its general capabilities.

Developed by: wrayy
Source model: wrayy/Qwenity3.6-27B-msv2 (16-bit merged)
Original base: Qwen/Qwen3.6-27B
Format: GGUF (text generation), qwen3_5 architecture
Language: English
License: Apache-2.0 (inherited from the base model)

Files

Two quantizations are provided. Each is split into <5 GB GGUF shards; download all shards of the quant you want into one folder.

Quant	Shards	Total size	Notes
Q4_K_M	`…-Q4_K_M-00001-of-00005.gguf` → `…-00005-of-00005.gguf` (5 files)	~16.5 GB	Recommended — best size/quality balance; runs on a 24 GB GPU or CPU.
Q8_0	`…-Q8_0-00001-of-00008.gguf` → `…-00008-of-00008.gguf` (8 files)	~28.6 GB	Near-lossless; highest quality.

These are native GGUF splits: point your runtime at the first shard (-00001-of-…) and it loads the rest automatically. (You can also merge them back with llama-gguf-split --merge.)

Intended use

Purpose-built to help developers build with Unity: C# gameplay scripting and the Unity scripting API, the Editor and project workflow, rendering, physics, animation, UI, and Unity-oriented debugging and tool use. It behaves as a chat / instruction-following assistant.

How to use

Runtime requirement. This is the new qwen3_5 (Qwen3.6) architecture. Use a llama.cpp build (and Ollama / LM Studio versions) that include Qwen3.5 / 3.6 support; older builds will refuse to load it.

llama.cpp

# downloads all 5 shards into the current folder, then point at shard 1:
llama-server -m Qwenity3.6-27B-msv2-Q4_K_M-00001-of-00005.gguf -ngl 99 -c 4096 --host 127.0.0.1 --port 8080

Ollama

printf 'FROM ./Qwenity3.6-27B-msv2-Q4_K_M-00001-of-00005.gguf\n' > Modelfile
ollama create qwenity3.6-27b -f Modelfile
ollama run qwenity3.6-27b "How do I raycast from the camera in Unity C#?"

LM Studio and other GGUF apps also load split files (point them at the first shard) with a Qwen3.5/3.6-capable runtime.

Training

Base model: Qwen/Qwen3.6-27B
Method: LoRA fine-tuning at 16-bit via Unsloth + TRL (SFT), merged to full bf16 (checkpoint 1200), then converted to GGUF and quantized with llama.cpp.
Training data: wrayy/unity.masterset.sbv2 — a privately compiled and synthesized instruction dataset for Unity development (English; conversational instruction / Q&A spanning C# scripting, the Editor, rendering, physics, UI, and tool use). The dataset is private / research-only, and its contents are not publicly disclosed.

Limitations

Domain-focused: optimized for Unity / C# / game development; less reliable elsewhere.
Verify generated code: like all LLMs it can produce incorrect, outdated, or insecure code — review and test before use.
Runtime: requires a Qwen3.5/3.6-capable llama.cpp / Ollama / LM Studio build.