Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency. Summaries are generated from a stored copy of the article text; every sentence cites a span of that text, and nothing is shown that failed the citation check.
Official6
developers.googleblog.com2024-12-05officialauthorityHN front pageabout the paligemma familyHN 218 pts · 27 comments468 words
Google introduces PaliGemma 2, the next evolution in tunable vision-language models built upon the Gemma 2 family. This update adds vision capabilities to make fine-tuning easier and offers multiple model sizes ranging from 3B to 28B parameters alongside resolutions of 224px, 448px, and 896px. The models function as a drop-in replacement for existing users, enabling detailed captioning and specialized tasks like chemical formula recognition without major code modifications.written, reviewed, cited
ai.google.dev2024-05-14officialauthorityHN front pageabout the paligemma familyHN 145 pts · 15 comments264 words
Google released PaliGemma 2 and PaliGemma, lightweight open vision-language models inspired by PaLI-3 that take both images and text as inputs. PaliGemma 2 comes in 3B, 10B, and 28B parameter sizes based on Gemma 2 variants, while the original PaliGemma is only 3B. These models support resolutions of 224 x 224, 448 x 448, and 896 x 896 pixels.written, reviewed, cited
developers.googleblog.com2025-02-19officialauthorityabout the paligemma familyHN 2 pts · 0 comments537 words
Google launched PaliGemma 2 mix checkpoints in February 2025, offering a vision-language model tuned for multiple tasks like captioning and OCR. These models come in sizes of 3B, 10B, and 28B parameters and support resolutions of 224px and 448px. Users can explore capabilities on the Hugging Face demo, download weights from Kaggle and Hugging Face, or deploy directly in Vertex Model Garden.written, reviewed, cited
developers.googleblog.com2024-09-05officialauthorityabout the paligemma family1,071 words
PaliGemma is a lightweight open vision-language model combining the SigLIP vision encoder with the Gemma 2B language model. The model uses a Gemma tokenizer with 256000 tokens, extended by 1024 entries for image coordinates and 128 entries for segmentation, totaling 257216 tokens. Image patches are 14x14 pixel squares processed through an encoder supporting up to 256 positions.written, reviewed, cited
- The patch embedding convolution uses a kernel size of (14, 14) with a stride of (14, 14) to process RGB images.
- The model extends the Gemma tokenizer vocabulary with 1024 entries representing coordinates in normalized image-space.
- PaliGemma-3B-mix-224 was trained with a sequence length of 256 for both input text and output text tokens.
colab.research.google.com2024-05-22officialabout the paligemma familyHN 3 pts · 0 comments5 words
huggingface.co2024-12-05authorityabout the paligemma familyHN 1 pts · 0 comments1,778 words
Google releases PaliGemma 2, a new vision language model family combining SigLIP with Gemma 2. The models come in 3B, 10B, and 28B parameter sizes and support input resolutions of 224x224, 448x448, and 896x896. Fine-tuned variants on the DOCCI dataset achieve NES scores of 28.4 and 20.3 at 448x448 resolution.written, reviewed, cited
Analysis4
blog.roboflow.com2024-05-15HN front pageabout the paligemma familyHN 121 pts · 101 comments2,051 words
Google released PaliGemma, a 3 billion parameter open source multimodal model combining SigLIP and Gemma at the 2024 Google I/O event. The model features permissive commercial licensing and is designed explicitly to be fine-tuned on custom data rather than used out of the box. Engineers can self-host it on cloud infrastructure or larger edge devices like NVIDIA Jetsons.written, reviewed, cited
- PaliGemma is not designed to be used directly, but to be transferred by fine-tuning to specific tasks.
- Fine-tuning only the attention layers may result in limited performance improvements for the model.
- Object detection prompts must include specific keywords like 'detect' or 'segment' to yield correct results.
arxiv.org2024-07-11authorityabout the paligemma familyHN 5 pts · 0 comments294 words
PaliGemma is a 3B parameter open Vision-Language Model built on the SigLIP-So400m vision encoder and the Gemma-2B language model. The model is trained to be a versatile and broadly knowledgeable base model that is effective to transfer. It achieves strong performance on almost 40 diverse tasks including standard VLM benchmarks and specialized tasks such as remote-sensing and segmentation.written, reviewed, cited
blog.roboflow.com2024-05-17about the paligemma familyHN 2 pts · 0 comments1,374 words
datature.io2024-06-20about the paligemma familyHN 2 pts · 0 comments3,246 words
