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Thinking Machines Releases Tinker API for Flexible Model Fine-Tuning
Thinking Machines has released Tinker, an API for fine-tuning open-weight language models. The service is designed to reduce infrastructure overhead for developers, providing managed scheduling, GPU allocation, and checkpoint handling. By abstracting away cluster management, Tinker allows fine-tuning through simple Python calls.
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Unsloth Tutorials Aim to Make it Easier to Compare and Fine-tune LLMs
In a recent Reddit post, Unsloth published comprehensive tutorials of all of the open models they support. The tutorials can be used to compare the models’ strengths and weaknesses, as well as their performance benchmarks.
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Nvidia's GB200 NVL72 Supercomputer Achieves 2.7× Faster Inference on DeepSeek V3
In collaboration with NVIDIA, researchers from SGLang have published early benchmarks of the GB200 (Grace Blackwell) NVL72 system, showing up to a 2.7× increase in LLM inference throughput compared to the H100 on the DeepSeek-V3 671B model.
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instructlab.ai Uses Synthetic Data to Reduce Complexity of Fine-Tuning LLMs
InstructLab.ai implements the large-scale alignment for the chatbots concept(LAB), which intends to overcome the scalability challenges in the instruction-tuning phase of a large language model (LLM). Its approach leverages a synthetic data-based alignment tuning method for LLMs. Crafted taxonomies deliver the synthesization seeds for training data, reducing the need for human-annotated data.