Researchers introduced Transformer², a machine learning system that dynamically adjusts its weights for different tasks using Singular Value Decomposition and reinforcement learning. The method learns task-specific z-vectors that modulate weight matrix components, requiring far fewer parameters than LoRA while achieving comparable or better performance on math, coding, reasoning, and visual tasks. This approach enables LLMs to adapt to new tasks at inference time without retraining, and z-vectors learned on one model can partially transfer to another model.
Tinker is a managed training API that lets researchers fine-tune open-source AI models using LoRA without managing infrastructure. The platform abstracts away compute and infrastructure complexities, allowing researchers to focus on datasets and algorithms while Tinker handles distributed training on GPU clusters. Users gain full control over model training through four core functions and can download model weights, with pricing based on token usage at rates shown in their documentation.
A community developer released MiniCPM5-1B-Claude-Opus-Fable5-Thinking, a 1.08B-parameter open-source model fine-tuned on Claude outputs to run locally without API calls. The smallest GGUF quantization is 657MB and runs on standard hardware via llama.cpp, Ollama, and similar runtimes. The fine-tuning transferred response format and style from Claude but does not replicate frontier reasoning capabilities, and no benchmarks or training dataset have been published to verify its claims.
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