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Person: "tomaarsen"
not much happened today
embeddinggemma qwen-2.5-coder minicpm-v-4.5 gpt-4o gemini-2.0-pro google-deepmind hugging-face jina-ai lighton microsoft stanford openai ollama weaviate langchain llamaindex embeddings retrieval-augmented-generation quantization multilingual-models on-device-ai semantic-search contrastive-learning dataset-release vision multimodality video-generation text-to-speech optimizer-benchmarking training-recipes model-compression video-token-compression fine-tuning osanseviero _philschmid tomaarsen ollama weaviate_io lusxvr andimarafioti thibaudfrere _akhaliq clementdelangue gordonwetzstein konstmish wen_kaiyue percyliang
Google DeepMind released EmbeddingGemma (308M), a small multilingual embedding model optimized for on-device retrieval-augmented generation and semantic search, supporting over 100 languages and running efficiently with quantization and EdgeTPU latency under 15ms. Jina AI introduced new code-focused embedding models (0.5B/1.5B) with GGUF quantization, achieving state-of-the-art retrieval across multiple languages and tasks. LightOn demonstrated large-scale retrieval training without distillation using contrastive training on billions of passages. Hugging Face released the FineVision dataset with 17.3M images and 9.5B answer tokens for vision-language model training, showing significant benchmark improvements. The MiniCPM-V 4.5 (8B) multimodal model reported surpassing GPT-4o and Gemini-2.0 Pro on OpenCompass benchmarks with innovative video token compression. Microsoft’s VibeVoice TTS and Stanford’s Mixture-of-Contexts video generation also featured. Additionally, a Stanford study benchmarked optimizers like Muon, Soap, Mars, and Sophia, finding diminishing speedups over AdamW at larger scales but advantages at smaller scales. The new ChatGPT branching feature was noted for its simplicity and popularity. "Everyone's a decacorn now."
not much happened today
gemma-3-270m canary-1b parakeet-tdt-0.6b nemotron-nano-v2 qwen-image-edit dino-v3 nvidia alibaba tencent meta-ai-fair ibm datology synthetic-data multilingual-asr self-supervised-learning vision model-efficiency training-data data-augmentation model-speedup domain-transfer demishassabis adrgrondin rasbt reach_vb ctnzr clementdelangue natolambert _akhaliq itspaulai mervenoyann xenovacom tomaarsen pratyushmaini code_star leavittron k_schuerholt giffmana
Gemma 3 270M, an ultra-small model optimized for edge and mobile use, was released and is gaining adoption. NVIDIA launched two open multilingual ASR models, Canary 1B and Parakeet-TDT 0.6B, trained on 1 million hours of data with CC-BY licensing, plus the efficient Nemotron-Nano v2 9B model with significant speedups. Alibaba's Qwen-Image-Edit offers bilingual text editing and semantic image transformations. Tencent Hunyuan introduced a controllable game-world video generator trained on over 1 million gameplay recordings. Meta's DINOv3 presents a scalable self-supervised vision backbone with strong domain transfer capabilities. IBM quietly released efficient English embedding models under a commercial-friendly license. The BeyondWeb synthetic data paper shows significant training speed and performance gains over prior datasets. Analysis of HRM architecture suggests performance improvements largely stem from data augmentation and scaffolding rather than novel architecture. "Models and datasets are openly licensed and available on Hugging Face."