Tag
作者在本地用 Qwen3.5 0.8B 作为基座训练了自己的 Jev 风格 Decision 模型，仅用 4GB 显存、60 步（约 10 分钟）LoRA 训练就将准确率从 37% 提升到 65%，并通过视频分享了使用 Unsloth Studio 的完整流程。
The Jeff models are ultra-fast, open-weight decision models trained locally on hardware like RTX PRO 6000, achieving competitive benchmarks against larger models like Jev with sub-30ms inference speeds.
PC-ALM is a local training method that uses layer-local dynamical systems to propagate supervision credit, enabling the training of up to 1000-layer networks without backpropagation while nearly matching its performance.
A local web UI for finetuning AI models on personal text, featuring live gradient activity monitoring, A/B prompt comparison, and AMD ROCm compatibility.
A tweet claiming that Chinese open-source LLMs have completely replaced newly released models, allowing local training, though raising the bar for casual developers.
The author details attempts to locally train a Qwen 3.6 27B autoregressive-to-diffusion model on an Nvidia 5090 GPU using qlora and modifications from open-dllm and d3LLM, facing VRAM constraints and hardware issues while exploring one-shot diffusion techniques.