Access the updated Gemma 4 12B file: https://t.co/N8QOgLNc5C Quickly try Gemma using LiteRT-LM CLI: https://t.co/E4e6Quyu9D Learn more about building with LiteRT-LM: https://t.co/dmBCWFdbEB
Google Gemma
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Read the blog for all the details, dive into the docs, and start building today: https://t.co/UVpgDrXvvV
- Google released EmbeddingGemma 2, a 740M-parameter multimodal embedding model built on Gemma 4 and licensed under Apache 2.0.
- The model has an 8K token context window with 768-dimensional outputs truncatable to 512, 256, or 128 via Matryoshka learning.
- Quantized on a Pixel 11 Pro, memory is roughly 191MB text-only and 567MB multimodal; MTEB Code reaches 78.68 versus 68.76 for v1.
Link to tech report: https://t.co/g710KPO6UR
- DiffusionGemma reportedly generates around 1,500 output tokens per second on a single NVIDIA H100 by refining blocks of 256 tokens in parallel.
- The model is a fine-tune of the mixture-of-experts Gemma 4 base, which has 3.8B activated and 25.2B total parameters.
- Its two-stage training pipeline of supervised denoising plus RL with sampler distillation used less than 10% of the original AR model's token budget.
See how AI can help scientists secure our global food supply. 🌾 https://t.co/eKVtjSr5OC Explore the research: 💻 Try the Gemma 4 + BOTANIC-1 demo: https://t.co/C7xtDf7PPX 🧬 Download BOTANIC-1: https://t.co/GKrCPoYWNi 📄 Read the paper: https://t.co/IEzsJaAVbw
See how AI can help scientists secure our global food supply. 🌾 https://t.co/eKVtjSr5OC Explore the research: 💻 Try the Gemma 4 + BOTANIC-1 demo: https://t.co/C7xtDf7PPX 🧬 Download BOTANIC-1: https://t.co/GKrCPoYWNi 📄 Read the paper: https://t.co/IEzsJaAVbw
Access the updated Gemma 4 12B file: https://t.co/N8QOgLNc5C Quickly try Gemma using LiteRT-LM CLI: https://t.co/E4e6Quyu9D Learn more about building with LiteRT-LM: https://t.co/dmBCWFdbEB
Read the full case study: https://t.co/63AwOuZEb1
Original post by @elliotarledge https://t.co/q9KnOlDAgO Resources: - GitHub Repo: https://t.co/UndyYJ2CqK - Gemma 4 (MLX): https://t.co/ALKU8ijMNm
- A 4-bit MLX conversion of Google's Gemma 4 E2B-it vision model fits into a 3.55 GB file targeting Apple silicon.
- The listing reports 38,478 downloads in the last month and ships under Google's Gemma license, not Apache 2.0.
- The card exposes the model as image-text-to-text, invoked through mlx_vlm.generate with a prompt and image path.
Ready to dive deeper into Per-Layer Embeddings? Check out these resources to get started: - Hugging Face: https://t.co/6Eml76vcXd - GitHub: https://t.co/C5XglGeamh - Visual Guide (via @MaartenGr): https://t.co/vf7ORXoraU
Hugging Face: https://t.co/97i2gZl8pZ Read the full breakdown to understand the goal of “smarter bits, not more bits”: https://t.co/Dd4spP8ITb
Read the full article from AlphaSense: https://t.co/ogYvtRAmzq
See how AI can help scientists secure our global food supply. 🌾 https://t.co/eKVtjSr5OC Explore the research: 💻 Try the Gemma 4 + BOTANIC-1 demo: https://t.co/C7xtDf7PPX 🧬 Download BOTANIC-1: https://t.co/GKrCPoYWNi 📄 Read the paper: https://t.co/IEzsJaAVbw
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