This guide explains how to implement Multi-Token Prediction (MTP) models, such as Gemma 4 and Qwen3.6, to increase inference speeds on local hardware. By predicting multiple tokens at once rather than one per step, MTP can achieve speedups of approximately 1.4x to 2.2x when using GGUF files without losing accuracy. The guide covers requirements for VRAM headroom, specific implementations for Gemma 4 and Qwen models, and provides setup instructions for both Unsloth Studio and llama.cpp environments.
- Accelerates inference through multi-token prediction
- Compatible with Gemma 4 and Qwen3.6/3.5 models
- Supported in Unsloth Studio and llama.cpp
- Accelerates inference through multi-token prediction
- Compatible with Gemma 4 and Qwen3.6/3.5 models
- Supported in Unsloth Studio and llama.cpp
This guide provides instructions for running Alibaba's Qwen3.6 multimodal hybrid-thinking models locally using Unsloth tools. It covers the 27B and 35B-A3B variants, which support a 256K context window across 201 languages and excel in agentic coding, vision, and chat tasks. The article details hardware requirements for various quantization levels and explains how to leverage Multi Token Prediction (MTP) for significantly faster inference.
Key topics:
- Hardware memory requirements for quantized models
- Faster generation via Multi Token Prediction (MTP)
- Integration with Unsloth Studio, llama.cpp, and MLX
- Preserved thinking mode configurations
Key topics:
- Hardware memory requirements for quantized models
- Faster generation via Multi Token Prediction (MTP)
- Integration with Unsloth Studio, llama.cpp, and MLX
- Preserved thinking mode configurations
This repository provides optimized Jinja chat templates designed to fix critical rendering errors, KV cache invalidation, and agentic stalling issues found in official Qwen 3.5 and 3.6 templates. It is compatible with major inference engines including LM Studio, llama.cpp, vLLM, and MLX.
This repository provides the GGUF quantized weights for Qwen3.6-27B, a flagship-level coding model designed for stability and real-world utility. The model features significant upgrades in agentic coding capabilities, allowing it to handle frontend workflows and repository-level reasoning with high precision. It also introduces thinking preservation, which enables the model to retain reasoning context from historical messages to improve iterative development.
Key technical highlights:
* Native context length of 262,144 tokens, extensible up to 1,010,000 via RoPE scaling (YaRN).
* Enhanced tool-calling capabilities for complex agentic tasks.
* Support for multimodal inputs including images and video.
* Optimized for various inference frameworks like SGLang, vLLM, and KTransformers.
Key technical highlights:
* Native context length of 262,144 tokens, extensible up to 1,010,000 via RoPE scaling (YaRN).
* Enhanced tool-calling capabilities for complex agentic tasks.
* Support for multimodal inputs including images and video.
* Optimized for various inference frameworks like SGLang, vLLM, and KTransformers.
Unsloth AI presents performance benchmarks for Qwen3.6-35B-A3B GGUF quantizations, claiming state-of-the-art results in mean KL divergence across most model sizes. The discussion includes community analysis regarding SWE-bench Verified performance, where some users noted unexpected discrepancies between Qwen3.5 and Qwen3.6 quantization results during coding tasks.
Key points:
- Unsloth ranks first in 21 of 22 model sizes for mean KL divergence.
- Community debate over SWE-bench testing methodology and sample sizes.
- Reported performance variations between different quantization levels (Q4, Q5, Q6, Q8).
- Discussion on system prompt adherence and error rates in coding benchmarks.
Key points:
- Unsloth ranks first in 21 of 22 model sizes for mean KL divergence.
- Community debate over SWE-bench testing methodology and sample sizes.
- Reported performance variations between different quantization levels (Q4, Q5, Q6, Q8).
- Discussion on system prompt adherence and error rates in coding benchmarks.
