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

Use Docker

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

LM Studio
Jan
Ollama
How to use quimmedes/Qwen3.8-Flash-Next-MTP-GGUF with Ollama:
```
ollama run hf.co/quimmedes/Qwen3.8-Flash-Next-MTP-GGUF:Q4_K_M
```
Unsloth Desktop

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf quimmedes/Qwen3.8-Flash-Next-MTP-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": "quimmedes/Qwen3.8-Flash-Next-MTP-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

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

Run Hermes

hermes

Atomic Chat

OpenClaw

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

Start the llama.cpp server

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

Qwen 3.8 Flash Next - MTP Draft Speculative Model (GGUF)

This repository provides working MTP (Multi-Token Prediction) draft models in GGUF format for Qwen 3.8 Flash Next (and experimental architectures with hyper-connections / QSA / Hadamard KV rotations), powered by cafe-llama.cpp.

Compatible Engine

This MTP model requires the fork implementation supporting hyper-connection hidden states, QSA Hadamard KV rotation, and MTP layer graph generation:

Repository: https://github.com/quimmedes/cafe-llama.cpp

git clone --single-branch --branch main https://github.com/quimmedes/cafe-llama.cpp

Available Files

mtp-Qwen3.8-Flash-Next-Q4_K_M.gguf: Quantized Q4_K_M MTP draft model (~2.65 GB) - Recommended
mtp-Qwen3.8-Flash-Next-Q6_K.gguf: Quantized Q6_K MTP draft model (~3.24 GB)
mtp-Qwen3.8-Flash-Next-Q8_0.gguf: Quantized Q8_0 MTP draft model (~3.94 GB)
mtp-Qwen3.8-Flash-Next-BF16.gguf: Full precision BF16 MTP draft model (~7.40 GB)

Usage with llama.cpp / llama-server

Run llama-server or llama-cli with --spec-type draft-mtp and point -md to your preferred MTP file. Setting --spec-draft-n-max 2 provides optimal acceptance rate (~50-70%) for a 1-layer MTP draft head:

llama-server   -m Qwen3.8-Flash-Next-UD-IQ3_XXS-00001-of-00003.gguf   -md mtp-Qwen3.8-Flash-Next-Q4_K_M.gguf   --spec-type draft-mtp   --spec-draft-n-max 2   -ngl 999   -hmoe   -fa on   -ctk q8_0 -ctv q8_0 -kvu   -c 8192 -b 1024 -ub 128

CLI Example:

llama-cli   -m Qwen3.8-Flash-Next-UD-IQ3_XXS-00001-of-00003.gguf   -md mtp-Qwen3.8-Flash-Next-Q4_K_M.gguf   --spec-type draft-mtp   --spec-draft-n-max 2   -ngl 999   -hmoe   -fa on   -ctk q8_0 -ctv q8_0   -p "Explain quantum entanglement simply."

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GGUF

Model size

4B params

Architecture

qwen4exp

Hardware compatibility

4-bit

6-bit

8-bit

16-bit

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