Model · Image and text to text
Qwen
Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency. For more details, please refer to our blog post Qwen3.5. WMT24++: a harder subset of WMT24 after difficulty labeling and rebalancing; we report the averaged scores on 55 languages using XCOMET-XXL. Empty cells (--) indicate scores not yet available or not applicable. Empty cells (--) indicate scores not…
Open weights
apache-2.0
4.7B parameters
262,144 tokens
transformers
Model · Image and text to text
Datalab
Chandra 2 is a state of the art OCR model from Datalab that outputs markdown, HTML, and JSON. It is highly accurate at extracting text from images and PDFs, while preserving layout information. Try Chandra in the free playground, or use the hosted API for higher accuracy and speed. - 85.8% olmocr bench score (sota), 77.8% multilingual bench score (12% improvement over Chandra 1) - Significant improvements to math, tables, complex layouts - 90+ language support with major accuracy gains - Convert documents to markdown, HTML, or JSON with detailed layout information - Reconstructs forms accurately, including checkboxes - Strong performance with tables, math, and complex layouts - Extracts…
Open weights
openrail
5.3B parameters
262,144 tokens
transformers
Expert-paged build of Vontra/Qwen3.8-Flash-Next-MLX-4bit. The weights that are read a fraction at a time live in their own containers, so a machine loads what it needs rather than all Total 105.46 GiB. Of that, 103.94 GiB is the source build, whose bytes moved into containers rather than being copied, and 1.52 GiB is the draft head, which no published build of this model carries. Where the weights fit they are filled from experts.bin and the model runs the stock path at stock speed; where they do not, they stream from disk. Reading the machine decides that, not a flag. To override that: GBXPAGING=off holds the experts resident, GBXPLE=off holds the n-gram table resident. Checked at build…
Open weights
other
5.4B parameters
262,144 tokens
mlx
This model is a fine-tuned version of Qwen/Qwen3.5-4B-Base on the Omni-Edu-70K dataset. The following hyperparameters were used during training: - learningrate: 5e-06 - trainbatchsize: 1 - evalbatchsize: 8 - distributedtype: multi-GPU - numdevices: 8 - gradientaccumulationsteps: 8 - totaltrainbatchsize: 64 - totalevalbatchsize: 64 - lrschedulertype: cosine - lrschedulerwarmupsteps: 0.1 - numepochs: 3.0 - Transformers 5.2.0 - Pytorch 2.10.0 - Datasets 4.0.0 - Tokenizers 0.22.2
Open weights
other
4.5B parameters
262,144 tokens
transformers
Model · Image and text to text
Qwen
Meet Qwen3-VL — the most powerful vision-language model in the Qwen series to date. This generation delivers comprehensive upgrades across the board: superior text understanding & generation, deeper visual perception & reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities. Available in Dense and MoE architectures that scale from edge to cloud, with Instruct and reasoning‑enhanced Thinking editions for flexible, on‑demand deployment. Text Understanding on par with pure LLMs: Seamless text–vision fusion for lossless, unified comprehension. 1. Interleaved-MRoPE: Full‑frequency allocation over time, width, and height…
Open weights
apache-2.0
4.4B parameters
262,144 tokens
transformers
Model · Image and text to text
Google
[Gemma 3 Technical Report][g3-tech-report] [Responsible Generative AI Toolkit][rai-toolkit] [Gemma on Kaggle][kaggle-gemma] [Gemma on Vertex Model Garden][vertex-mg-gemma3] Summary description and brief definition of inputs and outputs. Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous…
Access requested at publisher
gemma
4.3B parameters
transformers
