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Company: "lmsysorg"
not much happened today
inkling thinking-machines-lab huggingface vllm_project lmsysorg modal baseten databricks mixture-of-experts multimodality foundation-models model-licensing context-window open-weights model-release miramurati soumithchintala johnschulman2 lilianweng natolambert artificialanlys scaling01
Thinking Machines Lab launched Inkling, its first fully released open-weights foundation model family, featuring 975B parameters with 41B active parameters in a Mixture-of-Experts architecture. Inkling supports multimodality with text, image, and audio inputs and text output, is Apache 2.0 licensed, and offers up to 1M context window. The model is available on platforms like Tinker, Hugging Face, and partners, with broad ecosystem support from vLLM, SGLang, Modal, Baseten, and Databricks. Key figures such as Mira Murati, Soumith Chintala, John Schulman, and Lilian Weng highlighted its open weights, customization, and practical use focus. Independent commentators noted it as the strongest U.S.-based open-weight release to date, though still behind top Chinese open-weight and best closed models on some benchmarks.
Gemini Nano: 50-90% of Gemini Pro, <100ms inference, on device, in Chrome Canary
gemini-nano gemini-pro claude-3.5-sonnet gpt-4o deepseek-coder-v2 glm-0520 nemotron-4-340b gpt-4-turbo-0409 google gemini huggingface anthropic deepseek zhipu-ai tsinghua nvidia model-quantization prompt-api optimization model-weights benchmarking code-generation math synthetic-data automatic-differentiation retrieval-augmented-generation mitigating-memorization tree-search inference-time-algorithms adcock_brett dair_ai lmsysorg
The latest Chrome Canary now includes a feature flag for Gemini Nano, offering a prompt API and on-device optimization guide, with models Nano 1 and 2 at 1.8B and 3.25B parameters respectively, showing decent performance relative to Gemini Pro. The base and instruct-tuned model weights have been extracted and posted to HuggingFace. In AI model releases, Anthropic launched Claude 3.5 Sonnet, which outperforms GPT-4o on some benchmarks, is twice as fast as Opus, and is free to try. DeepSeek-Coder-V2 achieves 90.2% on HumanEval and 75.7% on MATH, surpassing GPT-4-Turbo-0409, with models up to 236B parameters and 128K context length. GLM-0520 from Zhipu AI/Tsinghua ranks highly in coding and overall benchmarks. NVIDIA announced Nemotron-4 340B, an open model family for synthetic data generation. Research highlights include TextGrad, a framework for automatic differentiation on textual feedback; PlanRAG, an iterative plan-then-RAG decision-making technique; a paper on goldfish loss to mitigate memorization in LLMs; and a tree search algorithm for language model agents.