Instructions to use WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-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 WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-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 WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF
# Run inference directly in the terminal:
llama cli -hf WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF
# Run inference directly in the terminal:
llama cli -hf WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF

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 WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF
# Run inference directly in the terminal:
./llama-cli -hf WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF

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 WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF

Use Docker

docker model run hf.co/WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF

LM Studio
Jan

vLLM

How to use WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-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": "WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF

Ollama
How to use WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF with Ollama:
```
ollama run hf.co/WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF
```
Unsloth Desktop

How to use WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF

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": "WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF with Docker Model Runner:
```
docker model run hf.co/WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF
```

Lemonade

How to use WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF

Run and chat with the model

lemonade run user.Winery-Qwen3.8-Flash-Next-Cuvee-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-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 WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF

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 WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF

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 "WineryLabs/Winery-Qwen3.8-Flash-Next-Cuvee-GGUF" \
  --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"

🍷 Winery Qwen3.8-Flash-Next · Cuvée

One blend of Qwen3.8-Flash-Next (125B MoE, 512 experts, 10 active) in three bottles, sized so it runs on a gaming PC with Winery Strata. That's a fork of Strata with a wine-red glass UI, where Cuvée is the first model in the installer.

Short thinking from Swift 1.5 (UkisAI)
Refusals poured out by Huihui's abliteration
Base everywhere else: routed experts' gate/up, routers, hyper-connections, PLE n-gram table

🍷 Visit the Cuvée Space for the expert-cellar map and a bottle picker for your RAM.

The bottles

Folder	For	Experts	Expert types	Size
`POCKET/`	8 GB GPU · 24-32 GB RAM	256 per layer	IQ2_XS gate/up · Q2_0 down, lean dense (Q4_K attn)	~48 GB
`SLIM/`	32 GB RAM	256 per layer	IQ3_XXS gate/up · IQ4_NL down	~60 GB
`RESERVE/`	64 GB RAM	all 512	IQ3_XXS gate/up · IQ4_NL down	~86 GB
`GRAND/`	96-128 GB+ RAM	all 512	Q4_K gate/up · Q5_1 down	~113 GB

All three keep the dense weights in Q6_K/Q8_0 and the 28.8 GB PLE table in IQ4_NL (Strata reads it from the SSD). They were quantised with Unsloth's imatrix for Flash-Next.

Slim drops half the experts, the same move as ISTA-DASLab's Coder. The difference is that it keeps the 256 experts routed most often on general text (Unsloth's calibration routing counts, 72-91% of routed tokens per layer, 77% on average), not the ones code uses. Expect it to be a little weaker than Reserve, mostly on rare topics.

How it was made

Comparing the fine-tunes with the base tensor by tensor showed they touch different parts of the model:

Tensors	Swift 1.5	Huihui
attention q/k/v · DeltaNet qkv/z · shared-expert gate/up	✓
attention/DeltaNet output · shared-expert down	✓	✓
routed experts' down		✓
everything else

So Cuvée = base + (Swift − base) + (Huihui − base) (task arithmetic, FP32 per tensor). Every tensor was streamed straight from the three BF16 checkpoints with HTTP range requests, merged and written to a Q8_0 GGUF. The Q8_0 was then quantised once to each bottle. The vision tower and MTP head are the original model's (Strata fetches them itself).

Run it

With Winery Strata (NVIDIA RTX 20-50 or a recent AMD card; 8 GB cards such as the RTX 4060 laptop GPU use POCKET, which setup picks for them by itself):

git clone https://huggingface.co/WineryLabs/Winery-Strata && cd Winery-Strata
START-HERE.bat --setup --family winery --model POCKET      # Windows, 8 GB GPU; SLIM / RESERVE / GRAND for more
START-HERE.bat --setup --family winery --model SLIM        # Windows; RESERVE / GRAND for more RAM
./setup.sh --setup --family winery --model RESERVE          # Linux

With llama.cpp (a build with qwen4exp support), e.g. llama-server -m RESERVE/Winery-Cuvee-RESERVE-00001-of-00002.gguf -c 32768 -ngl 99 --n-cpu-moe 48.

Not measured yet: speed on real hardware, and benchmark scores against the parent models. Sanity chats on CPU are in TASTING.md.

Licence

A derivative of Qwen3.8-Flash-Next (Qwen Community License 1.0, LICENSE-QWEN) that contains UkisAI's Swift Contribution (Swift Open License v1.0, LICENSE: free, including commercial use below US$1M yearly revenue). Huihui's weights are under the Qwen licence. Attribution and our changes are in NOTICE.