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

Install from WinGet (Windows)

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

Use Docker

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

LM Studio
Jan

vLLM

How to use agentionai/Qwen3.8-Flash-Next-MTP-Q8_0-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "agentionai/Qwen3.8-Flash-Next-MTP-Q8_0-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": "agentionai/Qwen3.8-Flash-Next-MTP-Q8_0-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

Ollama
How to use agentionai/Qwen3.8-Flash-Next-MTP-Q8_0-GGUF with Ollama:
```
ollama run hf.co/agentionai/Qwen3.8-Flash-Next-MTP-Q8_0-GGUF:Q8_0
```
Unsloth Desktop

How to use agentionai/Qwen3.8-Flash-Next-MTP-Q8_0-GGUF with Pi:

Start the llama.cpp server

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

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

How to use agentionai/Qwen3.8-Flash-Next-MTP-Q8_0-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

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

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use agentionai/Qwen3.8-Flash-Next-MTP-Q8_0-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf agentionai/Qwen3.8-Flash-Next-MTP-Q8_0-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 "agentionai/Qwen3.8-Flash-Next-MTP-Q8_0-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 MTP draft head (Q8_0)

The multi-token-prediction head from Qwen/Qwen3.8-Flash-Next, 3.85 GiB at Q8_0.

Qwen trains it jointly with the model, so it drafts better than a separate small model would. Use it as the draft for any Qwen3.8-Flash-Next GGUF.

Setup

Qwen3.8-Flash-Next itself has been in upstream llama.cpp since b10660, but MTP for it has not, so this head still needs the fork. On upstream you get:

error loading model: check_tensor_dims: tensor 'blk.0.hc_attn_norm.weight' not found

Upstream's qwen4exp loader has no MTP-only path, so it requires the 48 trunk layers this file does not carry. Build this branch instead:

git clone https://github.com/LaurentZuijdwijk/llama.cpp
cd llama.cpp && git checkout vulkan/qwen4exp-rocmfpx
cmake -B build -DGGML_VULKAN=ON -DCMAKE_BUILD_TYPE=Release
cmake --build build -j

Run

./build/bin/llama-server \
  -m Qwen3.8-Flash-Next.gguf \
  -md Qwen3.8-Flash-Next-MTP-Q8_0.gguf \
  -ngl 99 --n-gpu-layers-draft 99 \
  --spec-type draft-mtp --spec-draft-n-max 3 -c 32768

Adds ~3.9 GiB on top of the target.

Measurements

Radeon 8060S, 250 tokens at temp 0, warmed up first.

target	no draft	with this head	acceptance
UD-IQ4_XS	24.2 t/s	29.3 t/s	0.623
ROCmFP4-FAST	28.1 t/s	30.3 t/s	0.587

A 4-bit version of this head measured faster on both targets and is 1.6 GiB smaller.

Credits

Qwen3.8-Flash-Next support in llama.cpp is the work of Daniel Han (@danielhanchen), from ggml-org/llama.cpp#27742; the MTP graph is from #27739 (JJJYmmm). Base model by the Qwen team.

Quantized and published by Agention.

License

Qwen Community License 1.0, included as LICENSE.

Downloads last month: 5,648

GGUF

Model size

4B params

Architecture

qwen4exp

Hardware compatibility

8-bit

Model tree for agentionai/Qwen3.8-Flash-Next-MTP-Q8_0-GGUF

Base model

Qwen/Qwen3.8-Flash-Next

Quantized

(256)

this model