In internal tests of the Qwen3.5 0.8B model, the company increased aggregate accuracy from 20.7% to 74.3% across three decision benchmarks. Downstream accuracy also rose from a range of 30% to 37% up to 78% using the available open-source repository on GitHub.
Wednesday, October 21, 2026
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Unsloth Enables Local Decision Model Training on 3GB VRAM
AI14 days ago
The open-source project Unsloth has released tools that allow developers to fine-tune models such as Llama, Gemma, and Qwen into specialized decision-making agents on consumer hardware. The system utilizes a Clef head and LoRA for one epoch of training, requiring as little as 3GB to 4GB of VRAM to operate on a local machine via the Unsloth Desktop application.
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