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#Model release

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Oct 6

Oct 6Tue
  1. meng shaoAI score62

    Google DeepMind releases EmbeddingGemma 2, an open multimodal embedding model for on-device use

    AIGoogle DeepMind released EmbeddingGemma 2, an open 740M-parameter embedding model that maps text, code, images, video, and audio into one 768-dimensional space. Text-only use needs a 270M-parameter footprint, about 191MB active RAM when quantized on a Pixel 11 Pro, while loading all modalities takes about 567MB. The reported MTEB Code NDCG@10 score is 78.68, about 14% above the first generation, and MTEB Multilingual v2 is 61.36, roughly flat.

    Image from @shao__meng's post
  2. Liquid AI BlogAI score62

    Liquid AI releases open d1-3B and d1-omni-600M decision models for edge devices

    AILiquid AI released two open-weight d1 decision models, d1-3B and d1-omni-600M, on Hugging Face. d1-3B scores 48.57 on the Decision Index v0.2.1 public split and answers a single question in 8 ms on an NVIDIA GeForce RTX 4090 and 50 ms on a Jetson Orin Nano. d1-omni-600M is an experimental checkpoint that handles text with images or audio and scores 15.95 on the same index.

    Why it matters: The release pairs open-weight decision models with measured latency across Apple, NVIDIA, and Jetson hardware, showing how edge deployment changes what is practical.

  3. Claude Apps Release NotesAI score60

    Claude Haiku 5.5 launches as a fast, low-cost small model, and Max and Team plans gain monthly API credits

    AIAnthropic launched Claude Haiku 5.5, which it describes as the cheapest, fastest, and most capable small model it has released, aimed at high-volume, cost-sensitive tasks. Max and Team plans now include monthly API credits for running their own apps and agents on the Claude Platform, rolling out over a few days. Users claim the credits by linking a Claude Console organization in Settings > Billing for Max or Organization settings > Billing for Team.

    Why it matters: The notes name a new small model and a credit change for Max and Team plans, with the claim path, which matters for teams budgeting API use.

  4. Comfy BlogAI score43

    Gemini Nano Banana 2.1 Is Now Available via ComfyUI Partner Nodes

    AIGoogle's Gemini Nano Banana 2.1 image generation and editing model is now available through ComfyUI Partner Nodes, succeeding Nano Banana 2 with a balance of price and performance. It accepts up to 14 reference images, outputs images up to 4K, and offers Minimal, Medium, and High thinking levels plus search grounding and 9:21 aspect ratio support.

  5. ollamaAI score55

    Google DeepMind's EmbeddingGemma 2 is now available on Ollama

    AIOllama announced that Google DeepMind's EmbeddingGemma 2 is now available on Ollama. The author describes it as made for consumer devices and multimodal, and gives the command ollama pull embeddinggemma-2 to download it. The quoted DeepMind post says the model is a natively multimodal open model for on-device embeddings that unifies code, images, audio, and video in a shared space.

  6. Google DeepMindAI score67

    Google DeepMind releases EmbeddingGemma 2, an open multimodal embedding model for on-device use

    AIGoogle DeepMind has released EmbeddingGemma 2, an open 740 million parameter model that maps text, images, audio, and video into one embedding space. It is built on the Gemma 4 architecture under an Apache 2.0 license and supports an 8K token context window. The company reports a code benchmark gain from 68.76 to 78.68 on MTEB Code and says the model can run on-device with about 567MB of active RAM for the full multimodal version on a Google Pixel 11 Pro.

    Why it matters: The release shows how a 740M-parameter embedding model can cover text, code, images, audio, and video on local hardware, with memory and storage figures to compare against other on-device options.

  7. Philipp SchmidAI score70

    EmbeddingGemma 2 releases native multimodal embeddings built on Gemma 4

    AIGoogle releases EmbeddingGemma 2, its first native multimodal embedding model, built on Gemma 4 under Apache 2.0. It embeds over 100 languages, code, images, audio, and video into one vector, with an 8,192-token context and four sizes from 270M to 740M parameters. Matryoshka output dimensions of 768, 512, 256, or 128 are supported, and the model is available in Sentence Transformers and LiteRT-LM, with a reported 14% gain on MTEB Code.

    Why it matters: The release extends an embedding model to text, code, images, audio, and video in one vector, a useful option for retrieval systems that mix media types.

  8. vLLMAI score60

    vLLM Adds Day-0 Support for Google's EmbeddingGemma 2 Multimodal Embeddings

    AIvLLM announced day-0 support for EmbeddingGemma 2 from Google DeepMind, a bidirectional omni-modal embedding model that maps text, image, audio, video, and interleaved inputs into one vector space. Users can try it with the latest vLLM nightly build using the command vllm serve google/embeddinggemma-2 --runner pooling. The quoted Google post says the model is built on the Gemma 4 architecture and released under Apache 2.0.

    Image from @vllm_project's post
  9. clem 🤗AI score62

    Mistral Large 4 announced with API access today and open weights due end of October

    AIMistral announced Mistral Large 4, a natively multimodal model with 1T parameters and 49B active parameters. It is available via API today, with open weights planned for the end of October. Clément Delangue, Hugging Face's CEO, reacted by noting that the model cannot be the best open-weight model until its weights are actually released.

  10. 👩‍💻 Paige BaileyAI score54

    EmbeddingGemma 2 launches as an Apache 2.0 multimodal embeddings model

    AIGoogle's EmbeddingGemma 2 is an open embeddings model for on-device use that covers code, image, video, audio, and text. It comes in modular sizes from 270M text/code to 740M full multimodal, supports Matryoshka truncation down to 128 dimensions, and reports a 14% gain on MTEB Code over v1 under an Apache 2.0 license. The author's post highlights the release and a Hugging Face demo, while the benchmark table compares it with several models.

    Video from @DynamicWebPaige's post
  11. Google GemmaAI score62

    Google Gemma introduces EmbeddingGemma 2, a multimodal on-device embedding model

    AIGoogle Gemma announces EmbeddingGemma 2, a lightweight embedding model that maps text, code, images, video, and audio into a single unified embedding space. The model has a 740M parameter form factor with modular encoders, Matryoshka Representation Learning dimensions from 768 down to 128, and an 8K context window that is 4x larger than the text-only EmbeddingGemma. It is released under the commercially permissive Apache 2.0 license.

    Video from @googlegemma's post