FineVine Qwen3.5-35B-A3B V18 is a fine-tuned version of Qwen/Qwen3.5-35B-A3B optimized for efficient local inference by reducing thinking-token usage, especially for local use on Apple silicon laptops. Significant effort was also made to reduce looping behavior.
This repository contains the 6-bit MLX variant of FineVine Qwen3.5-35B-A3B Vision V18.
significant reduction in looping during function calls and reasoning
significant reduction in the tokens needed to reach an answer
better prompt adherence on structured tasks
better handling of contradictions, underdetermined prompts, and no-solution cases
This repo is organized as a multi-quant MLX release so you can choose the quantization that fits your hardware. It is ideal for Macs with less than 50 GB of memory.
Base Model
This model is based on:
Qwen/Qwen3.5-35B-A3B
What Improved
Compared with the base model, this release was tuned to improve several behavior problems at once:
Reduced overthinking on easy and medium prompts
Much less looping and repeated branch restarts on quantized versions
Better prompt adherence on structured coding and reasoning tasks
Better balance between concise answers and still giving enough detail when helpful
Better formatting for practical answers, including selective use of lists, tables, and diagrams
Better handling of valid logic, contradictions, and insufficient-information cases
Better behavior on harder coding prompts with more branch coverage before writing code
Strong tool-calling behavior on structured tasks and tool-chains
More stable local assistant behavior overall; if you need a local model that answers quickly, this fine-tune is a strong fit.
In practice, the model tends to answer more directly, with less wasted reasoning, while still remaining capable on harder structured tasks.
Training Data
Training data sources included:
custom hand-written and hand-edited datasets (60%)
manually reviewed synthetic traces (20%)
high-quality examples and reasoning traces derived from models including Claude Opus and GLM-5 (20%)
Quantizations
This release includes three MLX-VLM quantization options:
