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Topic: "post-training-methods"
Thinking Machines' Tinker: LoRA based LLM fine-tuning API
qwen-235b-a22b sora-2 thinking-machines openai fine-tuning lora model-training api model-optimization distributed-training post-training-methods research-productivity video-generation content-moderation engagement-patterns karpathy lilianweng sama
Thinking Machines recently raised $2 billion without shipping a product until now, launching their first product Tinker, a managed service API for fine-tuning large and mixture-of-experts models like Qwen-235B-A22B using LoRA for cost-efficient training. The Tinker API offers low-level primitives for post-training methods and is supported by an open-source Tinker Cookbook library. Influential AI figures like Andrej Karpathy and Lilian Weng praised its design for reducing complexity and boosting research productivity. Meanwhile, OpenAI launched Sora 2, a video+audio model integrated into their consumer social app, sparking viral engagement and concerns over misuse and content moderation. Sam Altman emphasized the product's dual focus on delight and revenue alongside AGI research.