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Topic: "semantic-search"
Kimi K2‑0905 and Qwen3‑Max preview: two 1T open weights models launched
kimi-k2-0905 qwen-3-max qwen-3 moonshot-ai alibaba huggingface together-ai groq lmsys openrouter llamaindex long-context agents coding tool-use model-evaluation instruction-following context-windows semantic-search discriminator-models swyx karpathy willdepue levie bebischof andrew_n_carr bigeagle_xd
Moonshot AI updated their Kimi K2-0905 open model with doubled context length to 256k tokens, improved coding and tool-calling, and integration with agent scaffolds. Alibaba released Qwen 3 Max, a 1 trillion parameter model with agent-oriented behavior, available via Qwen Chat, Alibaba Cloud API, and OpenRouter. The community highlights China's dominance in open models and debates around meaningful evaluation methods for code agents, emphasizing long-horizon and domain-specific evals. Influential voices like @swyx and @karpathy discuss the importance of practical evals and discriminator models for ranking outputs.
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."
Moondream 2025.1.9: Structured Text, Enhanced OCR, Gaze Detection in a 2B Model
o1 vdr-2b-multi-v1 llava-mini openai llamaindex langchainai qdrant genmoai vision model-efficiency structured-output gaze-detection reasoning model-distillation multimodality embedding-models gan diffusion-models self-attention training-optimizations development-frameworks api cross-language-deployment semantic-search agentic-document-processing developer-experience philschmid saranormous jxmnop reach_vb iscienceluvr multimodalart arohan adcock_brett awnihannun russelljkaplan ajayj_
Moondream has released a new version that advances VRAM efficiency and adds structured output and gaze detection, marking a new frontier in vision model practicality. Discussions on Twitter highlighted advancements in reasoning models like OpenAI's o1, model distillation techniques, and new multimodal embedding models such as vdr-2b-multi-v1 and LLaVA-Mini, which significantly reduce computational costs. Research on GANs and decentralized diffusion models showed improved stability and performance. Development tools like MLX and vLLM received updates for better portability and developer experience, while frameworks like LangChain and Qdrant enable intelligent data workflows. Company updates include new roles and team expansions at GenmoAI. "Efficiency tricks are all you need."
Qdrant's BM42: "Please don't trust us"
claude-3.5-sonnet gemma-2 nano-llava-1.5 qdrant cohere stripe anthropic hugging-face stablequan_ai semantic-search benchmarking dataset-quality model-evaluation model-optimization vision fine-tuning context-windows nils-reimers jeremyphoward hamelhusain rohanpaul_ai
Qdrant attempted to replace BM25 and SPLADE with a new method called "BM42" combining transformer attention and collection-wide statistics for semantic and keyword search, but their evaluation using the Quora dataset was flawed. Nils Reimers from Cohere reran BM42 on better datasets and found it underperformed. Qdrant acknowledged the errors but still ran a suboptimal BM25 implementation. This highlights the importance of dataset choice and evaluation sanity checks in search model claims. Additionally, Stripe faced criticism for AI/ML model failures causing account and payment issues, prompting calls for alternatives. Anthropic revealed that Claude 3.5 Sonnet suppresses some answer parts with backend tags, sparking debate. Gemma 2 model optimizations allow 2x faster fine-tuning with 63% less memory and longer context windows, running up to 34B parameters on consumer GPUs. nanoLLaVA-1.5 was announced as a compact 1B parameter vision model with significant improvements.