Instructions to use agentionai/Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST-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-ROCmFP4-FAST-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-ROCmFP4-FAST-GGUF
# Run inference directly in the terminal:
llama cli -hf agentionai/Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST-GGUF

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-ROCmFP4-FAST-GGUF
# Run inference directly in the terminal:
llama cli -hf agentionai/Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST-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 agentionai/Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST-GGUF
# Run inference directly in the terminal:
./llama-cli -hf agentionai/Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST-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 agentionai/Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf agentionai/Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST-GGUF

Use Docker

docker model run hf.co/agentionai/Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST-GGUF

LM Studio
Jan

vLLM

How to use agentionai/Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST-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-ROCmFP4-FAST-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-ROCmFP4-FAST-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/agentionai/Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST-GGUF

Ollama
How to use agentionai/Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST-GGUF with Ollama:
```
ollama run hf.co/agentionai/Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST-GGUF
```
Unsloth Desktop

How to use agentionai/Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST-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-ROCmFP4-FAST-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": "agentionai/Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use agentionai/Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST-GGUF with Docker Model Runner:
```
docker model run hf.co/agentionai/Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST-GGUF
```

Lemonade

How to use agentionai/Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull agentionai/Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST-GGUF

Run and chat with the model

lemonade run user.Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use agentionai/Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST-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-ROCmFP4-FAST-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 agentionai/Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use agentionai/Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST-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-ROCmFP4-FAST-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 "agentionai/Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST-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"

Qwen3.8-Flash-Next MTP draft head (ROCmFP4-FAST)

The multi-token-prediction head from Qwen/Qwen3.8-Flash-Next, 2.28 GiB. Qwen trains it jointly with the target model, so it drafts better than a separate small model would.

This is a draft head, not a model. On its own it does nothing. It is used with agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-GGUF, which is an experimental build; see that card first.

Setup

qwen4exp and the ROCmFPx quant types are not in upstream llama.cpp yet, so build this branch:

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-ROCmFP4-FAST-00001-of-00005.gguf \
  -md Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST.gguf \
  -ngl 99 --n-gpu-layers-draft 99 \
  --spec-type draft-mtp --spec-draft-n-max 3 -c 32768

Adds ~2.3 GiB to the ~85 GiB the target uses.

Measurements

Radeon 8060S (Ryzen AI MAX+ 395), 250 tokens at temp 0, each config warmed up first.

draft	t/s	acceptance
none	28.1	--
n-max 2	31.8	0.695
n-max 3	32.4	0.612

Quantized to match the target, not above it. A Q8_0 draft measured worse on both throughput and acceptance and cost 1.5 GiB more (30.3 t/s, 0.587): acceptance is the draft agreeing with the target, and two models quantized the same way are wrong in the same places.

Adaptive drafting also measured worse here (28.4 t/s, 0.468). It drafts longer when acceptance looks good, and this head carries its own 512-expert MoE, so each extra drafted token is a real forward pass.

Credits

qwen4exp support is the work of Daniel Han (@danielhanchen), from ggml-org/llama.cpp#27742, and the MTP graph is from #27739 (JJJYmmm). Both are unmerged drafts; if they land upstream, prefer upstream.

Quant formats hand-ported from ciru-ai/ROCmFPX. Base model by the Qwen team.

License

Qwen Community License 1.0, included as LICENSE.

Downloads last month: -

GGUF

Model size

4B params

Architecture

qwen4exp

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for agentionai/Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST-GGUF

Base model

Qwen/Qwen3.8-Flash-Next

Quantized

(85)

this model