Instructions to use Lucebox/Qwen3.8-Flash-Next-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 Lucebox/Qwen3.8-Flash-Next-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 Lucebox/Qwen3.8-Flash-Next-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf Lucebox/Qwen3.8-Flash-Next-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Lucebox/Qwen3.8-Flash-Next-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf Lucebox/Qwen3.8-Flash-Next-GGUF:Q8_0

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 Lucebox/Qwen3.8-Flash-Next-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Lucebox/Qwen3.8-Flash-Next-GGUF:Q8_0

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 Lucebox/Qwen3.8-Flash-Next-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Lucebox/Qwen3.8-Flash-Next-GGUF:Q8_0

Use Docker

docker model run hf.co/Lucebox/Qwen3.8-Flash-Next-GGUF:Q8_0

Llama new
LM Studio
Jan
Ollama
How to use Lucebox/Qwen3.8-Flash-Next-GGUF with Ollama:
```
ollama run hf.co/Lucebox/Qwen3.8-Flash-Next-GGUF:Q8_0
```
Unsloth Desktop

How to use Lucebox/Qwen3.8-Flash-Next-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Lucebox/Qwen3.8-Flash-Next-GGUF:Q8_0

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": "Lucebox/Qwen3.8-Flash-Next-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Lucebox/Qwen3.8-Flash-Next-GGUF with Docker Model Runner:
```
docker model run hf.co/Lucebox/Qwen3.8-Flash-Next-GGUF:Q8_0
```

Lemonade

How to use Lucebox/Qwen3.8-Flash-Next-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Lucebox/Qwen3.8-Flash-Next-GGUF:Q8_0

Run and chat with the model

lemonade run user.Qwen3.8-Flash-Next-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use Lucebox/Qwen3.8-Flash-Next-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 Lucebox/Qwen3.8-Flash-Next-GGUF:Q8_0

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 Lucebox/Qwen3.8-Flash-Next-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Lucebox/Qwen3.8-Flash-Next-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Lucebox/Qwen3.8-Flash-Next-GGUF:Q8_0

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 "Lucebox/Qwen3.8-Flash-Next-GGUF:Q8_0" \
  --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"

Qwen3.8-Flash-Next GGUF for Lucebox

The Qwen3.8-Flash-Next files Lucebox serves: the UD-Q4_K_XL quantization and the multi-token-prediction (MTP) drafter used for speculative decoding. They are byte-identical copies of unsloth/Qwen3.8-Flash-Next-GGUF at revision 766911a6b7369840a91dbcd95f9f997acaab6cd6, kept here so Lucebox installs download from one place.

Base model: Qwen/Qwen3.8-Flash-Next by Qwen.
Quantization and MTP export: Unsloth.

Files

File	Size (bytes)	sha256
`UD-Q4_K_XL/Qwen3.8-Flash-Next-UD-Q4_K_XL-00001-of-00004.gguf`	10,946,624	`4448186216b3af4cc558bbce2c3213f01608f8f8b2e5267a9767971dd3ec8082`
`UD-Q4_K_XL/Qwen3.8-Flash-Next-UD-Q4_K_XL-00002-of-00004.gguf`	49,859,583,136	`3f342f1c1580473f1ee94ddd5b28206e8c07a70fa1a366f59d1d6c922919a6c9`
`UD-Q4_K_XL/Qwen3.8-Flash-Next-UD-Q4_K_XL-00003-of-00004.gguf`	49,376,141,504	`56758f40269cad5cd9b0d3d6fbae0f40f6d5be6de49e4ab392dbe83157d9cbd3`
`UD-Q4_K_XL/Qwen3.8-Flash-Next-UD-Q4_K_XL-00004-of-00004.gguf`	12,087,983,520	`753bda48b98ba4f1636134a90a967de1b2d3908a236c026e464777342e53510a`
`MTP/mtp-Qwen3.8-Flash-Next-shared-Q8_0.gguf`	2,786,568,256	`5ff54097406a905cf3a724c709124ceb0e3e10235ee862298969e91c96fa96e6`

Performance on Lucebox

Measured with the Lucebox engine: UD-Q4_K_XL, greedy decoding with the MTP drafter, 131K context.

	Strix Halo iGPU	Strix Halo + Radeon AI PRO R9700
Decode, short reply	40 tok/s	75 tok/s
Decode after a 16K prompt	24-28 tok/s	44-45 tok/s
Prefill, 16K prompt	1,300 tok/s	1,820 tok/s
Same 16K prompt sent again		2.7 s instead of 10.6 s (prefix cache)

On the two-GPU box the R9700 keeps the dense weights, the KV cache, the drafter and the most-used experts, and the Strix Halo runs the other experts. Speculative decoding does not change the output: greedy text is identical with the drafter on or off.

Use with the Lucebox engine

Point luce_server at the first shard; the other shards load from the same folder, and the engine finds the drafter in MTP/ on its own. --draft PATH picks a drafter explicitly and --verify-width 1 turns speculative decoding off.

luce_server UD-Q4_K_XL/Qwen3.8-Flash-Next-UD-Q4_K_XL-00001-of-00004.gguf

License

Qwen community license 1.0; the full text is in LICENSE and applies to every file here.

Downloads last month: 193

GGUF

Model size

177B params

Architecture

qwen4exp

Hardware compatibility

4-bit

8-bit

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