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
- llama.cpp
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:Q2_0 # Run inference directly in the terminal: llama cli -hf quimmedes/Qwen3.8-Flash-Next-MTP-GGUF:Q2_0
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:Q2_0 # Run inference directly in the terminal: llama cli -hf quimmedes/Qwen3.8-Flash-Next-MTP-GGUF:Q2_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 quimmedes/Qwen3.8-Flash-Next-MTP-GGUF:Q2_0 # Run inference directly in the terminal: ./llama-cli -hf quimmedes/Qwen3.8-Flash-Next-MTP-GGUF:Q2_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 quimmedes/Qwen3.8-Flash-Next-MTP-GGUF:Q2_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf quimmedes/Qwen3.8-Flash-Next-MTP-GGUF:Q2_0
Use Docker
docker model run hf.co/quimmedes/Qwen3.8-Flash-Next-MTP-GGUF:Q2_0
- 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:Q2_0
- Unsloth Desktop
- Pi
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:Q2_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": "quimmedes/Qwen3.8-Flash-Next-MTP-GGUF:Q2_0" } ] } } }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:Q2_0
- 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:Q2_0
Run and chat with the model
lemonade run user.Qwen3.8-Flash-Next-MTP-GGUF-Q2_0
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:Q2_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 quimmedes/Qwen3.8-Flash-Next-MTP-GGUF:Q2_0
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:Q2_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 "quimmedes/Qwen3.8-Flash-Next-MTP-GGUF:Q2_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"
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 https://github.com/quimmedes/cafe-llama.cpp.git
Building from Source
Pick the backend for your hardware, then build. -j N sets the number of compile threads โ a safe value is the number of physical cores.
1. NVIDIA CUDA (Windows / Linux)
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release -j 2
2. Vulkan (Cross-Platform AMD / Intel / NVIDIA) โ requires the Vulkan SDK installed
cmake -B build -DGGML_VULKAN=ON
cmake --build build --config Release -j 2
3. AMD ROCm / HIP (Linux / Windows)
cmake -B build -DGGML_HIP=ON -DAMDGPU_TARGETS="gfx1100;gfx1030"
cmake --build build --config Release -j 2
4. Apple Metal (macOS)
cmake -B build -DGGML_METAL=ON
cmake --build build --config Release -j 2
5. CPU Only (AVX2 / AVX-512)
cmake -B build -DGGML_CUDA=OFF -DGGML_VULKAN=OFF
cmake --build build --config Release -j 2
Available Files
mtp-Qwen3.8-Flash-Next-Q2_0.gguf: Quantized Q2_0 MTP draft model (~1.10 GB)mtp-Qwen3.8-Flash-Next-Q3_0.gguf: Quantized Q3_0 MTP draft model (~1.50 GB)mtp-Qwen3.8-Flash-Next-Q4_0.gguf: Quantized Q4_0 MTP draft model (~2.20 GB) - Recommended
Re-quantized from BF16 with llama-quantize (cafe-llama.cpp build c2f5366e3). The previous Q4_K_M/Q6_K/Q8_0/BF16 files were removed: they were produced from a broken conversion and did not work with speculative decoding.
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_0.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_0.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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Model tree for quimmedes/Qwen3.8-Flash-Next-MTP-GGUF
Base model
Qwen/Qwen3.8-Flash-Next