Instructions to use axiomofmind/Qwen3.8-Flash-Next-W4A16-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 axiomofmind/Qwen3.8-Flash-Next-W4A16-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 axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4-GGUF:BF16

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 axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4-GGUF:BF16

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 axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4-GGUF:BF16

Use Docker

docker model run hf.co/axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4-GGUF:BF16

LM Studio
Jan

vLLM

How to use axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "axiomofmind/Qwen3.8-Flash-Next-W4A16-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": "axiomofmind/Qwen3.8-Flash-Next-W4A16-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/axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4-GGUF:BF16

Ollama
How to use axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4-GGUF with Ollama:
```
ollama run hf.co/axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4-GGUF:BF16
```
Unsloth Desktop

How to use axiomofmind/Qwen3.8-Flash-Next-W4A16-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 axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4-GGUF:BF16

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": "axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4-GGUF:BF16
```

Lemonade

How to use axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4-GGUF:BF16

Run and chat with the model

lemonade run user.Qwen3.8-Flash-Next-W4A16-NVFP4-GGUF-BF16

List all available models

lemonade list

Hermes Agent

How to use axiomofmind/Qwen3.8-Flash-Next-W4A16-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 axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4-GGUF:BF16

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 axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4-GGUF:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use axiomofmind/Qwen3.8-Flash-Next-W4A16-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 axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4-GGUF:BF16

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 "axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4-GGUF:BF16" \
  --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"

axiomofmind commited on 29 days ago

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+---
+base_model: axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4
+pipeline_tag: image-text-to-text
+license: other
+license_name: qwen-community-1.0
+license_link: https://huggingface.co/Qwen/Qwen3.8-Flash-Next/blob/main/LICENSE
+tags:
+  - gguf
+  - qwen
+  - qwen4exp
+  - nvfp4
+  - modelopt
+  - moe
+  - vision-language
+---
+# Qwen3.8-Flash-Next W4A16 NVFP4 GGUF
+GGUF conversion of
+[`axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4`](https://huggingface.co/axiomofmind/Qwen3.8-Flash-Next-W4A16-NVFP4),
+based on [`Qwen/Qwen3.8-Flash-Next`](https://huggingface.co/Qwen/Qwen3.8-Flash-Next).
+The routed-expert weights use W4A16 NVFP4. Attention, shared experts,
+routers, embeddings, PLE, and other retained tensors remain in BF16 or F32.
+## Files
+| File | Description | Size |
+| --- | --- | ---: |
+| `Qwen3.8-Flash-Next-W4A16-NVFP4-BF16attn-PLE-noMTP.gguf` | Main text model | 180.4 GB |
+| `mmproj-Qwen3.8-Flash-Next-BF16.gguf` | BF16 vision projector | 907.5 MB |
+The vision projector is required for image inputs. It is not required for
+text-only use.
+This GGUF does not include MTP weights.
+## Requirements
+A llama.cpp build with Qwen4Exp and NVFP4 GGUF support is required.
+For multimodal use, load the main model together with the included `mmproj`
+file.
+## License
+This model is distributed under the
+[Qwen Community License 1.0](https://huggingface.co/Qwen/Qwen3.8-Flash-Next/blob/main/LICENSE).
+Refer to the [official model card](https://huggingface.co/Qwen/Qwen3.8-Flash-Next)
+for architecture details, usage guidance, and limitations.