Instructions to use vumpt/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 vumpt/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 vumpt/Qwen3.8-Flash-Next-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vumpt/Qwen3.8-Flash-Next-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 vumpt/Qwen3.8-Flash-Next-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vumpt/Qwen3.8-Flash-Next-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 vumpt/Qwen3.8-Flash-Next-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf vumpt/Qwen3.8-Flash-Next-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 vumpt/Qwen3.8-Flash-Next-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vumpt/Qwen3.8-Flash-Next-GGUF:Q4_K_M

Use Docker

docker model run hf.co/vumpt/Qwen3.8-Flash-Next-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use vumpt/Qwen3.8-Flash-Next-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vumpt/Qwen3.8-Flash-Next-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": "vumpt/Qwen3.8-Flash-Next-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/vumpt/Qwen3.8-Flash-Next-GGUF:Q4_K_M

Ollama
How to use vumpt/Qwen3.8-Flash-Next-GGUF with Ollama:
```
ollama run hf.co/vumpt/Qwen3.8-Flash-Next-GGUF:Q4_K_M
```

Unsloth Studio

How to use vumpt/Qwen3.8-Flash-Next-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for vumpt/Qwen3.8-Flash-Next-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for vumpt/Qwen3.8-Flash-Next-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for vumpt/Qwen3.8-Flash-Next-GGUF to start chatting

How to use vumpt/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 vumpt/Qwen3.8-Flash-Next-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": "vumpt/Qwen3.8-Flash-Next-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use vumpt/Qwen3.8-Flash-Next-GGUF with Docker Model Runner:
```
docker model run hf.co/vumpt/Qwen3.8-Flash-Next-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vumpt/Qwen3.8-Flash-Next-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.8-Flash-Next-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use vumpt/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 vumpt/Qwen3.8-Flash-Next-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 vumpt/Qwen3.8-Flash-Next-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use vumpt/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 vumpt/Qwen3.8-Flash-Next-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 "vumpt/Qwen3.8-Flash-Next-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"

Qwen3.8-Flash-Next — GGUF (Q4_K_M)

GGUF conversion of Qwen/Qwen3.8-Flash-Next, the Qwen4-experimental hybrid model (Gated DeltaNet + Qwen Sparse Attention + 512-expert MoE + n-gram "PLE" embedding table).

125B total MoE params (6B active) + 51B n-gram table + 4B MTP = 180B params
Converted with the in-flight llama.cpp PR #27742 (qwen4exp architecture support)

Quantization layout

Q4_K_M recipe. Because several tensor shapes in this architecture aren't divisible by 256, llama.cpp's type-fallback applies per-tensor (this is a feature of PR #27742's quantizer fixes, not an error):

Component	Quant
MoE experts / attention / FFN	Q4_K_M (fallback to Q5_0/q8_0 where ncols % 256 ≠ 0)
Token embedding + output	Q6_K
N-gram (PLE) hash table (51B params)	Q5_0 (160-col layout → falls back from Q6_K)

Files

File	Quant	Size
`qwen3.8-flash-next-Q4_K_M.gguf`	Q4_K_M	~120 GB

Usage (llama.cpp)

git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
git fetch origin pull/27742/head:qwen4exp && git checkout qwen4exp
cmake -B build && cmake --build build -j --target llama-cli
./build/bin/llama-cli -m qwen3.8-flash-next-Q4_K_M.gguf -p "Hello" -ngl 99

Note: This architecture is only supported on the PR #27742 branch. Mainline llama.cpp (as of this writing) does not load qwen4_exp. Use the branch above.

Source

Weights: Qwen/Qwen3.8-Flash-Next (bf16, 360 GB)
License: qwen-community-1.0

Downloads last month: -

GGUF

Model size

177B params

Architecture

qwen4exp

Hardware compatibility

4-bit

Model tree for vumpt/Qwen3.8-Flash-Next-GGUF

Base model

Qwen/Qwen3.8-Flash-Next

Quantized

(35)

this model