quimmedes/Qwen3.8-Flash-Next-MTP-GGUF overview
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…
Runs locally from ~1.10 GB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
Model Details
Model README
---
license: apache-2.0
base_model: Qwen/Qwen3.8-Flash-Next
tags:
- llama.cpp
- speculative-decoding
- mtp
- gguf
- qwen
---
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 — 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) — 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."Run quimmedes/Qwen3.8-Flash-Next-MTP-GGUF with guIDE
Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.
Source: Hugging Face · Compare models