fine-tuning
Model merging: combining two fine-tuned models by averaging their weights Lesson
Model merging combines two or more models that share a common ancestor by arithmetic on their weights - averaging them, adding their differences, or interpolating between them - producing a single model with both sets of skills and no additional training.
Catastrophic forgetting: why teaching an AI something new can erase what it knew Lesson
Catastrophic forgetting is the tendency of a neural network to lose an old skill when trained on a new one, because both skills are stored in the same weights and nothing protects the old settings.
A $500 fine-tune of a 9B open model beat all five frontier models it was tested against News
A consultancy reinforcement-trained a 9-billion-parameter open model on a simulated product-catalog review workflow for about $500 of GPU time, and it outscored the best of five frontier configurations while costing $0.50 per thousand listings against $34.
Fine-tuning and LoRA: teaching an old model a new job without retraining it Lesson
You almost never train an AI from scratch. You take one that already knows language and nudge it toward your specific task - and a trick called LoRA lets you do that by adding a tiny sticky note instead of rewriting the whole brain.
veRL Tool
The open RL post-training framework used by most research labs training reasoning models today. Run GRPO, PPO, and related reward-training methods on your own models.
prime-rl Tool
Open-source RL post-training stack that splits rollout generation and gradient updates across GPUs, used in this week's widely discussed $500 fine-tune that beat five frontier configurations on a catalog-review workflow. Practical for teams that already have an automatically scored task and want to train a specialist rather than pay per call for a frontier model.
Unsloth Studio Tool
A no-code desktop and web interface for training and running language models on your own machine, with a one-line installer and desktop shortcuts. Training is NVIDIA-GPU centric; inference and export work across Mac, Windows and Linux.
Unsloth (AMD support) Tool
The fine-tuning and RL toolkit now documents AMD support across training, RL, chat, and deployment on Windows, WSL, and Linux, plus a cross-platform Studio beta.
Unsloth Tool
Toolkit and documentation for running and fine-tuning large open models faster and on smaller hardware, including aggressive dynamic quantization recipes that shrink models like GLM 5.2 by 80-plus percent while keeping most of their accuracy. The practical on-ramp to running near-frontier models privately.
Tinker Tool
Thinking Machines Lab's hosted fine-tuning service, now serving Inkling alongside its other models. It is the managed path to customizing Inkling if you do not want to provision the GPUs yourself -- with the caveat that the API caps context at 256K tokens, versus 1M for the open weights you run yourself.
SLAI T-Rex Tool
The public workflow behind a full-parameter Ascend post-training run on a DeepSeek-V4-family model: FP8-to-BF16-to-Megatron checkpoint conversion, launch templates, and inspectable data-construction pipelines for continued pre-training and supervised fine-tuning. The production engine and custom kernels are withheld.
Recursive-Task-Synthesis Tool
A public set of 37,484 verified long-horizon terminal-agent tasks, each a runnable bundle with instruction, environment, reference solution and hidden verifier, plus a companion set of 327,000 agent trajectories and three fine-tuned Qwen3.5 checkpoints.