# Image and Text to Text Models: Open-Weight AI Source: https://savrn.com/models/tasks/image-and-text-to-text Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- SAVRN Model Hub · Models by Task # Image and Text to Text Models 577 open-weight image and text to text models in the SAVRN Model Hub, with Qwen, Solstice-AI and Isaac Godfried publishing the most. 577Models 278Publishers 665K to 2.8TParameter range 8Licenses ## SAVRN's Take Hand one of these models a photograph or a screenshot, ask a question, and it answers in text. Our hub lists 138 models for the job, 49 of them in the Index. Qwen publishes 28 and Unsloth AI 11, with Google, LM Studio Community and the OpenVLA Collaboration at 4 each. Downloads follow suit: Qwen3-VL-8B-Instruct pulled 19,098,599 last month, ahead of Qwen3.6-35B-A3B-FP8 at 10,009,539 and Google's gemma-4-26B-A4B-it at 9,794,984. All eight leaders share a 262,144-token context and fit on a single MI300X at $1.85 an hour on the Index, so the question is how much of that card each takes. At 16-bit there are two tiers: Qwen3.5-4B, Qwen3-VL-8B-Instruct and Qwen3.5-9B need 11.2 to 23.2 GB, leaving the rest of the card for batch, while gemma-4-26B-A4B-it, both Qwen3.8-27B builds, gemma-4-31B-it and Qwen3.6-35B-A3B-FP8 need 61.9 to 86.3 GB. Halve those for 8-bit; 4-bit puts the whole group under 22 GB. Kimi-K3, at 2.8T parameters, is the outlier: 6,671.8 GB at 16-bit, which no Index host can hold, and 1,668.0 GB at 4-bit across 7 MI325X cards at $14.00 an hour. Licensing is the easy part. 89 of the 138 are Apache 2.0, including all eight leaders, and 16 more are MIT, so most of the catalog deploys without a negotiation. The 20 marked other, Kimi-K3 among them, and the 5 under Gemma terms need a read first; 3 state no license, which we treat as a no. Settle the memory tier first, then the license, then treat the download count as a tally of operators who got there before you. SAVRN Research, 2026-09-18 ## Most Downloaded | Model | Publisher | Parameters | License | Monthly downloads | Cheapest GPUs at 16-bit | | --- | --- | --- | --- | --- | --- | | [gemma-4-26B-A4B-it](https://savrn.com/models/gemma-4-26b-a4b-it) | Google | 25.8B | apache-2.0 | 9.4M | 1x MI300X, $1.85/hr | | [gemma-4-31B-it](https://savrn.com/models/gemma-4-31b-it) | Google | 31.3B | apache-2.0 | 8.6M | 1x MI300X, $1.85/hr | | [Qwen3.8-27B](https://savrn.com/models/qwen3-8-27b) | Qwen | 27.8B | apache-2.0 | 7.8M | 1x MI300X, $1.85/hr | | [Qwen3.5-4B](https://savrn.com/models/qwen3-5-4b) | Qwen | 4.7B | apache-2.0 | 7.6M | 1x MI300X, $1.85/hr | | [Qwen3.5-9B](https://savrn.com/models/qwen3-5-9b) | Qwen | 9.7B | apache-2.0 | 6.7M | 1x MI300X, $1.85/hr | | [Qwen3.8-Flash-Next-GSQ-RCO-GGUF](https://savrn.com/models/qwen3-8-flash-next-gsq-rco-gguf) | IST Austria Distributed Algorithms and Systems Lab | — | apache-2.0 | 6.4M | — | | [GLM-5.3-Flash](https://savrn.com/models/glm-5-3-flash) | Z.ai | 321.3B | mit | 6.4M | 3x MI355X, $7.77/hr | | [Qwen2.5-VL-7B-Instruct](https://savrn.com/models/qwen2-5-vl-7b-instruct) | Qwen | 8.3B | apache-2.0 | 5M | 1x MI300X, $1.85/hr | | [Qwen3-VL-8B-Instruct](https://savrn.com/models/qwen3-vl-8b-instruct) | Qwen | 8.8B | apache-2.0 | 4.8M | 1x MI300X, $1.85/hr | | [Qwen3.8-27B-FP8](https://savrn.com/models/qwen3-8-27b-fp8) | Qwen | 27.8B | apache-2.0 | 4.3M | 1x MI300X, $1.85/hr | ## Licenses | License | Models | Commercial use | | --- | --- | --- | | [apache-2.0](https://savrn.com/models/licenses/apache-2-0) | 360 | Yes | | other | 105 | Read the license | | [mit](https://savrn.com/models/licenses/mit) | 53 | Yes | | not stated | 40 | Not stated | | [gemma](https://savrn.com/models/licenses/gemma) | 7 | Yes, with conditions | | [openrail](https://savrn.com/models/licenses/openrail) | 3 | Yes, with conditions | | [openmdw-1.1](https://savrn.com/models/licenses/openmdw-1-1) | 2 | Read the license | | [llama2](https://savrn.com/models/licenses/llama2) | 2 | Read the license | ## Who Publishes Them | Publisher | Models | | --- | --- | | [Qwen](https://savrn.com/model-publishers/qwen) | 28 | | [Solstice-AI](https://savrn.com/model-publishers/solstice-ai) | 16 | | [Isaac Godfried](https://savrn.com/model-publishers/isaacmg) | 16 | | [RKNNAI](https://savrn.com/model-publishers/rknnai) | 15 | | [David Belton](https://savrn.com/model-publishers/davidau) | 14 | | [Unsloth AI](https://savrn.com/model-publishers/unsloth) | 13 | ## All 577 Models, Page 1 of 10 Model · Image and text to text ### [gemma-4-26B-A4B-it](https://savrn.com/models/gemma-4-26b-a4b-it) [Google](https://savrn.com/model-publishers/google) Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages. Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in five distinct sizes: E2B, E4B, 12B, 26B A4B, and 31B. Their diverse sizes make them deployable in environments ranging from… Open weights apache-2.0 25.8B parameters 262,144 tokens transformers [View model](https://savrn.com/models/gemma-4-26b-a4b-it) Model · Image and text to text ### [gemma-4-31B-it](https://savrn.com/models/gemma-4-31b-it) [Google](https://savrn.com/model-publishers/google) Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages. Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in five distinct sizes: E2B, E4B, 12B, 26B A4B, and 31B. Their diverse sizes make them deployable in environments ranging from… Open weights apache-2.0 31.3B parameters 262,144 tokens transformers [View model](https://savrn.com/models/gemma-4-31b-it) Model · Image and text to text ### [Qwen3.8-27B](https://savrn.com/models/qwen3-8-27b) [Qwen](https://savrn.com/model-publishers/qwen) Following the widespread community adoption of the Qwen3.5 and Qwen3.6 series, we are pleased to introduce Qwen3.8, the most capable generation in the Qwen open-model family to date. Built on the architectural foundation of Qwen3.5, Qwen3.8 delivers substantial gains across coding, professional work, research, and long-horizon agentic tasks. Qwen3.8-27B brings these advances to a compact, deployment-friendly dense model: a native vision-language model that understands images and videos, with flexible thinking control, designed to carry complex, multi-step tasks through to completion with greater reliability. For streamlined integration, we recommend using Qwen3.8 via APIs. Qwen3.8 can be… Open weights apache-2.0 27.8B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-8-27b) Model · Image and text to text ### [Qwen3.5-4B](https://savrn.com/models/qwen3-5-4b) [Qwen](https://savrn.com/model-publishers/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 [View model](https://savrn.com/models/qwen3-5-4b) Model · Image and text to text ### [Qwen3.5-9B](https://savrn.com/models/qwen3-5-9b) [Qwen](https://savrn.com/model-publishers/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 9.7B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-5-9b) Model · Image and text to text ### [GLM-5.3-Flash](https://savrn.com/models/glm-5-3-flash) [Z.ai](https://savrn.com/model-publishers/zai-org) Join our WeChat or Discord community. Check out the GLM-5.3-Flash blog and GLM-5 Technical report. Use GLM-5.3-Flash API services on Z.ai API Platform. We introduce GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series. With 320B total parameters and just 18B active parameters, it outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude Opus 4.8 on coding and agentic benchmarks. GLM-5.3-Flash starts from a newly trained base model, with its architecture and training recipe redesigned around capability and efficiency. For the first time in the GLM series, we introduce a hybrid architecture combining sparse and linear… Open weights mit 321.3B parameters 1,048,576 tokens transformers [View model](https://savrn.com/models/glm-5-3-flash) Model · Image and text to text ### [Qwen2.5-VL-7B-Instruct](https://savrn.com/models/qwen2-5-vl-7b-instruct) [Qwen](https://savrn.com/model-publishers/qwen) pipelinetag: image-text-to-text - multimodal libraryname: transformers In the past five months since Qwen2-VL’s release, numerous developers have built new models on the Qwen2-VL vision-language models, providing us with valuable feedback. During this period, we focused on building more useful vision-language models. Today, we are excited to introduce the latest addition to the Qwen family: Qwen2.5-VL. Understanding long videos and capturing events: Qwen2.5-VL can comprehend videos of over 1 hour, and this time it has a new ability of cpaturing event by pinpointing the relevant video segments. Capable of visual localization in different formats: Qwen2.5-VL can accurately localize objects in… Open weights apache-2.0 8.3B parameters 128,000 tokens transformers [View model](https://savrn.com/models/qwen2-5-vl-7b-instruct) Model · Image and text to text ### [Qwen3-VL-8B-Instruct](https://savrn.com/models/qwen3-vl-8b-instruct) [Qwen](https://savrn.com/model-publishers/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 8.8B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-vl-8b-instruct) Model · Image and text to text ### [Qwen3.6-35B-A3B](https://savrn.com/models/qwen3-6-35b-a3b) [Qwen](https://savrn.com/model-publishers/qwen) Following the February release of the Qwen3.5 series, we're pleased to share the first open-weight variant of Qwen3.6. Built on direct feedback from the community, Qwen3.6 prioritizes stability and real-world utility, offering developers a more intuitive, responsive, and genuinely productive coding experience. This release delivers substantial upgrades, particularly in For more details, please refer to our blog post Qwen3.6-35B-A3B. Empty cells (--) indicate scores not available or not applicable. For streamlined integration, we recommend using Qwen3.6 via APIs. Below is a guide to use Qwen3.6 via OpenAI-compatible API. Qwen3.6 can be served via APIs with popular inference frameworks. In… Open weights apache-2.0 36B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-6-35b-a3b) Model · Image and text to text ### [Qwen3-VL-4B-Instruct](https://savrn.com/models/qwen3-vl-4b-instruct) [Qwen](https://savrn.com/model-publishers/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 [View model](https://savrn.com/models/qwen3-vl-4b-instruct) Model · Image and text to text ### [Qwen3.5-0.8B](https://savrn.com/models/qwen3-5-0-8b) [Qwen](https://savrn.com/model-publishers/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. Scores of Qwen3.5 models are reported… Open weights apache-2.0 873M parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-5-0-8b) Model · Image and text to text ### [Florence-2-base](https://savrn.com/models/florence-2-base) [Microsoft](https://savrn.com/model-publishers/microsoft) This Hub repository contains a HuggingFace's transformers implementation of Florence-2 model from Microsoft. Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks. Florence-2 can interpret simple text prompts to perform tasks like captioning, object detection, and segmentation. It leverages our FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. The model's sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings, proving to be a competitive vision foundation model. Use the code below to get started with the… Open weights mit 232M parameters 1,024 tokens transformers [View model](https://savrn.com/models/florence-2-base) Model · Image and text to text ### [Qwen3.5-2B](https://savrn.com/models/qwen3-5-2b) [Qwen](https://savrn.com/model-publishers/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. Scores of Qwen3.5 models are reported… Open weights apache-2.0 2.3B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-5-2b) Model · Image and text to text ### [Qwen3.8-Flash-Next](https://savrn.com/models/qwen3-8-flash-next) [Qwen](https://savrn.com/model-publishers/qwen) As the frontier of foundation models pushes toward ever-larger parameter counts and ever-longer context windows, the question is no longer just how much we can scale, but how efficiently we can do so. Sustainable progress toward artificial general intelligence (AGI) that benefits everyone demands architectural innovation. Today, we are sharing a concrete step in that direction: Qwen3.8-Flash-Next. This experimental preview of the architecture that will underpin Qwen4 is built around a fundamental rethinking of how the core components of modern large language models (LLMs) interact at scale. The first open-weight release under this architecture is Qwen3.8-Flash-Next, which introduces: For… Open weights other 180B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-8-flash-next) Model · Image and text to text ### [Qwen3.6-27B](https://savrn.com/models/qwen3-6-27b) [Qwen](https://savrn.com/model-publishers/qwen) Following the February release of the Qwen3.5 series, we're pleased to share the first open-weight variant of Qwen3.6. Built on direct feedback from the community, Qwen3.6 prioritizes stability and real-world utility, offering developers a more intuitive, responsive, and genuinely productive coding experience. This release delivers substantial upgrades, particularly in For more details, please refer to our blog post Qwen3.6-27B. Empty cells (--) indicate scores not yet available or not applicable. For streamlined integration, we recommend using Qwen3.6 via APIs. Below is a guide to use Qwen3.6 via OpenAI-compatible API. Qwen3.6 can be served via APIs with popular inference frameworks. In… Open weights apache-2.0 27.8B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-6-27b) Model · Image and text to text ### [moondream2](https://savrn.com/models/moondream2) [Vik Korrapati](https://savrn.com/model-publishers/vikhyatk) This repository contains the latest version of Moondream 2, our previous generation model. The latest version of Moondream is Moondream 3 (Preview). Moondream is a small vision language model designed to run efficiently everywhere. This repository contains the latest (2025-06-21) release of Moondream 2, as well as historical releases. The model is updated frequently, so we recommend specifying a revision as shown below if you're using it in a production application. Grounded Reasoning Introduces a new step-by-step reasoning mode that explicitly grounds reasoning in spatial positions within the image before answering, leading to more precise visual interpretation (e.g., chart median… Open weights apache-2.0 1.9B parameters transformers [View model](https://savrn.com/models/moondream2) Model · Image and text to text ### [Unlimited-OCR](https://savrn.com/models/unlimited-ocr) [BAIDU](https://savrn.com/model-publishers/baidu) [2026/07/21] Thanks to the ms-swift community for their support, our model now supports training with ms-swift. - [2026/07/03] Thanks to the Baidu Cloud team for their support. Our model is now available on Baidu Cloud. - [2026/06/28] Thanks to the vLLM community and Tianyu Guo for their support, our model now supports vLLM inference. - [2026/06/24] Thanks to AK for creating a demo for us. It is now available at Hugging Face Spaces. - [2026/06/23] Our paper is now available on arXiv. - [2026/06/23] Thanks to the ModelScope community for their support. Our model is now available at ModelScope. - [2026/06/22] We present Unlimited-OCR, aiming to push Deepseek-OCR one step further. Inference… Open weights mit 3.3B parameters 32,768 tokens transformers [View model](https://savrn.com/models/unlimited-ocr) Model · Image and text to text ### [DeepSeek-OCR](https://savrn.com/models/deepseek-ocr) [DeepSeek](https://savrn.com/model-publishers/deepseek-ai) Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.12.9 + CUDA11.8： Refer to GitHub for guidance on model inference acceleration and PDF processing, etc. [2025/10/23] DeepSeek-OCR is now officially supported in upstream vLLM. We would like to thank Vary, GOT-OCR2.0, MinerU, PaddleOCR, OneChart, Slow Perception for their valuable models and ideas. author={Wei, Haoran and Sun, Yaofeng and Li, Yukun}, year={2025} Open weights mit 3.3B parameters 8,192 tokens transformers [View model](https://savrn.com/models/deepseek-ocr) Model · Image and text to text ### [Qwen3-VL-2B-Instruct](https://savrn.com/models/qwen3-vl-2b-instruct) [Qwen](https://savrn.com/model-publishers/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 2.1B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-vl-2b-instruct) Model · Image and text to text ### [Qwen2.5-VL-3B-Instruct](https://savrn.com/models/qwen2-5-vl-3b-instruct) [Qwen](https://savrn.com/model-publishers/qwen) licensename: qwen-research licenselink: https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct/blob/main/LICENSE pipelinetag: image-text-to-text - multimodal libraryname: transformers In the past five months since Qwen2-VL’s release, numerous developers have built new models on the Qwen2-VL vision-language models, providing us with valuable feedback. During this period, we focused on building more useful vision-language models. Today, we are excited to introduce the latest addition to the Qwen family: Qwen2.5-VL. Understanding long videos and capturing events: Qwen2.5-VL can comprehend videos of over 1 hour, and this time it has a new ability of cpaturing event by pinpointing the relevant video… Open weights 3.8B parameters 128,000 tokens transformers [View model](https://savrn.com/models/qwen2-5-vl-3b-instruct) Model · Image and text to text ### [Qwen3.5-27B](https://savrn.com/models/qwen3-5-27b) [Qwen](https://savrn.com/model-publishers/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 27.8B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-5-27b) Model · Image and text to text ### [DeepSeek-V4.1-Flash](https://savrn.com/models/deepseek-v4-1-flash) [DeepSeek](https://savrn.com/model-publishers/deepseek-ai) We introduce DeepSeek-V4.1-Flash, a multimodal Mixture-of-Experts (MoE) model with 552B backbone parameters and support for contexts of up to one million tokens. The model natively processes images and text, and generates text autoregressively. Architecture. DeepSeek-V4.1-Flash adopts a Causal Encoder-Decoder (CED) architecture: a 40-layer Transformer organized as a 20-layer causal encoder followed by a 20-layer decoder. With CED, the decoder's global KV cache is projected from the final encoder hidden states rather than derived from each decoder layer's own hidden states. This allows the model to activate only 8B parameters per token during prefill and 16B during decode, substantially… Open weights mit 763.2B parameters 1,048,576 tokens transformers [View model](https://savrn.com/models/deepseek-v4-1-flash) Model · Image and text to text ### [SmolVLM2-500M-Video-Instruct](https://savrn.com/models/smolvlm2-500m-video-instruct) [Hugging Face Smol Models Research](https://savrn.com/model-publishers/huggingfacetb) SmolVLM2-500M-Video is a lightweight multimodal model designed to analyze video content. The model processes videos, images, and text inputs to generate text outputs - whether answering questions about media files, comparing visual content, or transcribing text from images. Despite its compact size, requiring only 1.8GB of GPU RAM for video inference, it delivers robust performance on complex multimodal tasks. This efficiency makes it particularly well-suited for on-device applications where computational resources may be limited. SmolVLM2 can be used for inference on multimodal (video / image / text) tasks where the input consists of text queries along with video or one or more images.… Open weights apache-2.0 507M parameters 8,192 tokens transformers [View model](https://savrn.com/models/smolvlm2-500m-video-instruct) Model · Image and text to text ### [surya-ocr-2](https://savrn.com/models/surya-ocr-2) [Datalab](https://savrn.com/model-publishers/datalab-to) Surya is a 650M param OCR model with these features: - Accuracy - scores 83.3% on olmOCR-bench (top under 3B params) - Multilingual - scores 87.2% on an internal benchmark set of 91 languages (more here) - Layout analysis (table, image, header, etc.) with reading order - Table recognition (rows + columns) It works on a range of documents (see usage and benchmarks). Our managed platform runs both Surya, and variants of our highest accuracy model, Chandra. Get started with $5 in free credits — sign up (takes under 30 seconds) or try our free public playground. Surya is named for the Hindu sun god, who has universal vision. The Surya code is licensed under Apache 2.0. The model weights use a… Open weights openrail 686M parameters 262,144 tokens transformers [View model](https://savrn.com/models/surya-ocr-2) Model · Image and text to text ### [chandra-ocr-2](https://savrn.com/models/chandra-ocr-2) [Datalab](https://savrn.com/model-publishers/datalab-to) 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 [View model](https://savrn.com/models/chandra-ocr-2) Model · Image and text to text ### [Qwen3.5-35B-A3B](https://savrn.com/models/qwen3-5-35b-a3b) [Qwen](https://savrn.com/model-publishers/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 36B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-5-35b-a3b) Model · Image and text to text ### [Inkling](https://savrn.com/models/inkling) [Thinking Machines Lab](https://savrn.com/model-publishers/thinkingmachines) Inkling is a general-purpose multimodal model that accepts text, image and audio inputs and generates text outputs. It is intended for use in English and other languages, and across multiple coding languages. The model is designed to be used by developers building AI-powered applications, including agentic and tool-use systems, coding assistants, chatbots, and retrieval-augmented generation systems, and is suitable for general-purpose conversational use, instruction-following, and other natural language and multimodal tasks. It is released with open weights to support research, fine-tuning and integration into third-party products by downstream developers. Languages: English, with general… Open weights apache-2.0 952.4B parameters transformers [View model](https://savrn.com/models/inkling) Model · Image and text to text ### [Qwen2-VL-2B-Instruct](https://savrn.com/models/qwen2-vl-2b-instruct) [Qwen](https://savrn.com/model-publishers/qwen) We're excited to unveil Qwen2-VL, the latest iteration of our Qwen-VL model, representing nearly a year of innovation. SoTA understanding of images of various resolution & ratio: Qwen2-VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc. Understanding videos of 20min+: Qwen2-VL can understand videos over 20 minutes for high-quality video-based question answering, dialog, content creation, etc. Agent that can operate your mobiles, robots, etc.: with the abilities of complex reasoning and decision making, Qwen2-VL can be integrated with devices like mobile phones, robots, etc., for automatic operation based on… Open weights apache-2.0 2.2B parameters 32,768 tokens transformers [View model](https://savrn.com/models/qwen2-vl-2b-instruct) Model · Image and text to text ### [Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive](https://savrn.com/models/qwen3-6-35b-a3b-uncensored-hauhaucs-aggressive) [HauhauCS](https://savrn.com/model-publishers/hauhaucs) Qwen3.6-35B-A3B uncensored by HauhauCS. 0/465 Refusals. No changes to datasets or capabilities. Fully functional, 100% of what the original authors intended - just without the refusals. These are meant to be the best lossless uncensored models out there. Stronger uncensoring — model is fully unlocked and won't refuse prompts. May occasionally append short disclaimers (baked into base model training, not refusals) but full content is always generated. For a more conservative uncensor that keeps some safety guardrails, check the Balanced variant when it's available. All quants generated with importance matrix (imatrix) for optimal quality preservation on abliterated weights. KP ("Perfect")… Open weights apache-2.0 [View model](https://savrn.com/models/qwen3-6-35b-a3b-uncensored-hauhaucs-aggressive) Model · Image and text to text ### [Qwen2.5-VL-32B-Instruct](https://savrn.com/models/qwen2-5-vl-32b-instruct) [Qwen](https://savrn.com/model-publishers/qwen) In addition to the original formula, we have further enhanced Qwen2.5-VL-32B's mathematical and problem-solving abilities through reinforcement learning. This has also significantly improved the model's subjective user experience, with response styles adjusted to better align with human preferences. Particularly for objective queries such as mathematics, logical reasoning, and knowledge-based Q&A, the level of detail in responses and the clarity of formatting have been noticeably enhanced. In the past five months since Qwen2-VL’s release, numerous developers have built new models on the Qwen2-VL vision-language models, providing us with valuable feedback. During this period, we focused on… Open weights apache-2.0 33.5B parameters 128,000 tokens transformers [View model](https://savrn.com/models/qwen2-5-vl-32b-instruct) Model · Image and text to text ### [Qwen3.6-27B-NVFP4](https://savrn.com/models/qwen3-6-27b-nvfp4) [Unsloth AI](https://savrn.com/model-publishers/unsloth) 2.5x faster throughput than other NVFP4 quants. This is an Unsloth NVFP4 quantized checkpoint calibrated on a mixture of our Unsloth dataset + UltraChat dataset. Works on a 24GB VRAM GPU. Benchmarks on 1xB200 128 concurrency. For accuracy benchmarks, we conducted MMLU-Pro, AIME 2025, GPQA for FP8, BF16, NVIDIA's NVFP4 and our NVFP4s - we show our faster quants do similarly on all: Read all benchmarks in our NVFP4 blog To install vLLM in a separate venv: Then to serve the 27B NVFP4 quant: Also do NOT use the Marlin backend since it's 2x slower - use the native vLLM or cute-DSL / CUTLASS / flashinfertrtllm backends! You must use the below or you will get 2x slower inference! This checkpoint… Open weights apache-2.0 21.2B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-6-27b-nvfp4) Model · Image and text to text ### [medgemma-4b-it](https://savrn.com/models/medgemma-4b-it) [Google](https://savrn.com/model-publishers/google) Model on Google Cloud Model Garden: MedGemma GitHub repository (supporting code, Colab notebooks, discussions, and Foundations terms of use](https://developers.google.com/health-ai-developer-foundations/terms). This section describes the MedGemma model and how to use it. MedGemma is a collection of Gemma 3 variants that are trained for performance on medical text and image comprehension. Developers can use MedGemma to accelerate building healthcare-based AI applications. MedGemma currently comes in three variants: a 4B multimodal version and 27B text-only and multimodal versions. Both MedGemma multimodal versions utilize a SigLIP image encoder that has been specifically pre-trained on a… Access requested at publisher other 4.3B parameters transformers [View model](https://savrn.com/models/medgemma-4b-it) Model · Image and text to text ### [Qwen3.6-35B-A3B-NVFP4](https://savrn.com/models/qwen3-6-35b-a3b-nvfp4-2) [Unsloth AI](https://savrn.com/model-publishers/unsloth) 1.56x faster throughput than other NVFP4 quants. This is an Unsloth NVFP4 quantized checkpoint calibrated on a mixture of our Unsloth dataset + UltraChat dataset. Works on a 32GB VRAM GPU. Benchmarks on 1xB200 128 concurrency. Use the 35B NVFP4 Fast version for 1.79x faster at a little less accuracy For accuracy benchmarks, we conducted MMLU-Pro, AIME 2025, GPQA for FP8, BF16, NVIDIA's NVFP4 and our NVFP4s - we show our faster quants do similarly on all: Read all benchmarks in our NVFP4 blog To install vLLM in a separate venv: Then to serve the 35B variant: You must use the below or you will get 2x slower inference! Also do NOT use the Marlin backend since it's 2x slower - use the native… Open weights apache-2.0 24.6B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-6-35b-a3b-nvfp4-2) Model · Image and text to text ### [Qwen3.8-27B-NVFP4](https://savrn.com/models/qwen3-8-27b-nvfp4-2) [RadixArk](https://savrn.com/model-publishers/radixark) The RadixArk Qwen3.8-27B-NVFP4 model is the quantized version of Qwen/Qwen3.8-27B. The quantization was produced at RadixArk using NVIDIA Model Optimizer, following a mixed NVFP4 W4A4 recipe. Run on SGLang: launch command and per-platform recipes in the Qwen3.8-27B cookbook. This model is not owned or developed by RadixArk. It is a quantized derivative of Qwen's model; see the upstream Qwen3.8-27B model card for the source model's capabilities, training information, limitations, and license. Global Developers looking to deploy an off-the-shelf, pre-quantized model in AI agent systems, chatbots, RAG systems, and other AI-powered applications. Hugging Face 08/14/2026 via… Open weights apache-2.0 18.2B parameters 262,144 tokens Model Optimizer [View model](https://savrn.com/models/qwen3-8-27b-nvfp4-2) Model · Image and text to text ### [Kimi-K3](https://savrn.com/models/kimi-k3) [Moonshot AI](https://savrn.com/model-publishers/moonshotai) Kimi K3 is an open-weight, native multimodal agentic model and our most capable model to date. It is a 2.8T-parameter model built on Kimi Delta Attention (KDA) and Attention Residuals (AttnRes), with native vision capabilities and a 1-million-token context window. It is the world's first open 3T-class model, designed for frontier intelligence across long-horizon coding, knowledge work, and reasoning. All Kimi K3 results are obtained with reasoning effort set to 'max' and temperature = 1.0. For single-step tasks, such as GPQA Diamond, HLE-Full, and vision benchmarks without tools, we set top-p = 0.95; for agentic tasks, we set top-p = 1.0. For HLE-Full, MMMU-Pro, CharXiv (RQ), MathVision… Open weights other 2.8T parameters 1,048,576 tokens transformers [View model](https://savrn.com/models/kimi-k3) Model · Image and text to text ### [gemma-3-4b-it](https://savrn.com/models/gemma-3-4b-it) [Google](https://savrn.com/model-publishers/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 [View model](https://savrn.com/models/gemma-3-4b-it) Model · Image and text to text ### [llava-1.5-7b-hf](https://savrn.com/models/llava-1-5-7b-hf) [Llava Hugging Face](https://savrn.com/model-publishers/llava-hf) Below is the model card of Llava model 7b, which is copied from the original Llava model card that you can find here. Check out also the Google Colab demo to run Llava on a free-tier Google Colab instance: Or check out our Spaces demo! LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. LLaVA-v1.5-7B was trained in September 2023. Paper or resources for more information: https://llava-vl.github.io/ First, make sure to have transformers >= 4.35.3. The model supports multi-image and multi-prompt generation. Meaning that you can pass multiple… Open weights llama2 7.1B parameters 4,096 tokens transformers [View model](https://savrn.com/models/llava-1-5-7b-hf) Model · Image and text to text ### [Qwen3.8-27B-NVFP4](https://savrn.com/models/qwen3-8-27b-nvfp4-3) [Inferact Inc.](https://savrn.com/model-publishers/inferact) Quantized version of https://huggingface.co/Qwen/Qwen3.8-27B Open weights apache-2.0 17.6B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-8-27b-nvfp4-3) Model · Image and text to text ### [dots.ocr](https://savrn.com/models/dots-ocr) [Dots Studio](https://savrn.com/model-publishers/dots-studio) dots.ocr: Multilingual Document Layout Parsing in a Single Vision-Language Model dots.ocr is a powerful, multilingual document parser that unifies layout detection and content recognition within a single vision-language model while maintaining good reading order. Despite its compact 1.7B-parameter LLM foundation, it achieves state-of-the-art(SOTA) performance. 1. Powerful Performance: dots.ocr achieves SOTA performance for text, tables, and reading order on OmniDocBench, while delivering formula recognition results comparable to much larger models like Doubao-1.5 and gemini2.5-pro. 2. Multilingual Support: dots.ocr demonstrates robust parsing capabilities for low-resource languages… Open weights mit 3B parameters 131,072 tokens dots_ocr [View model](https://savrn.com/models/dots-ocr) Model · Image and text to text ### [Gemma4-12B-QAT-Uncensored-HauhauCS-Balanced](https://savrn.com/models/gemma4-12b-qat-uncensored-hauhaucs-balanced) [HauhauCS](https://savrn.com/model-publishers/hauhaucs) Gemma4-12B (QAT) uncensored by HauhauCS. 0/465 Refusals No changes to datasets or capabilities — fully functional, 100% of what the original authors intended, just without the refusals. Built from the official QAT weights, so the 4-bit quant stays close to full-precision quality. The Balanced variant (recommended — 99%+ of users will be happy here) uses optimized full uncensoring tuned especially for agentic coding, reasoning, creative writing and reliability-critical tasks. It reasons before answering and stays dependable and on-instruction. An Aggressive variant, for cases where Balanced still deflects too much, after current testing is not required. Ships with an MTP… Open weights gemma [View model](https://savrn.com/models/gemma4-12b-qat-uncensored-hauhaucs-balanced) Model · Image and text to text ### [Qwen3.6-35B-A3B-NVFP4](https://savrn.com/models/qwen3-6-35b-a3b-nvfp4-3) [Red Hat AI](https://savrn.com/model-publishers/redhatai) NVFP4 variant of Qwen 3.6 35B for reasoning and tool calling. Open weights apache-2.0 34.7B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-6-35b-a3b-nvfp4-3) Model · Image and text to text ### [Rax-4.5](https://savrn.com/models/rax-4-5) [RaxCore](https://savrn.com/model-publishers/raxcore-dev) Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Rax 4.5 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. Rax 4.5 features the following enhancement: For more details, please refer to our blog post Rax 4.5. 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… Open weights apache-2.0 2.3B parameters 262,144 tokens transformers [View model](https://savrn.com/models/rax-4-5) Model · Image and text to text ### [Qwen2-VL-7B-Instruct](https://savrn.com/models/qwen2-vl-7b-instruct) [Qwen](https://savrn.com/model-publishers/qwen) We're excited to unveil Qwen2-VL, the latest iteration of our Qwen-VL model, representing nearly a year of innovation. SoTA understanding of images of various resolution & ratio: Qwen2-VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc. Understanding videos of 20min+: Qwen2-VL can understand videos over 20 minutes for high-quality video-based question answering, dialog, content creation, etc. Agent that can operate your mobiles, robots, etc.: with the abilities of complex reasoning and decision making, Qwen2-VL can be integrated with devices like mobile phones, robots, etc., for automatic operation based on… Open weights apache-2.0 8.3B parameters 32,768 tokens transformers [View model](https://savrn.com/models/qwen2-vl-7b-instruct) Model · Image and text to text ### [DeepSeek-OCR-2](https://savrn.com/models/deepseek-ocr-2) [DeepSeek](https://savrn.com/model-publishers/deepseek-ai) Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.12.9 + CUDA11.8： Refer to GitHub for guidance on model inference acceleration and PDF processing, etc. We would like to thank DeepSeek-OCR, Vary, GOT-OCR2.0, MinerU, PaddleOCR for their valuable models and ideas. We also appreciate the benchmark OmniDocBench. author={Wei, Haoran and Sun, Yaofeng and Li, Yukun}, year={2025} title={DeepSeek-OCR 2: Visual Causal Flow}, author={Wei, Haoran and Sun, Yaofeng and Li, Yukun}, year={2026} Open weights apache-2.0 3.4B parameters 8,192 tokens transformers [View model](https://savrn.com/models/deepseek-ocr-2) Model · Image and text to text ### [Cosmos-Reason2-2B](https://savrn.com/models/cosmos-reason2-2b) [NVIDIA](https://savrn.com/model-publishers/nvidia) NVIDIA Cosmos Reason 2 is an open, customizable, 2B-parameter reasoning vision language model (VLM) for physical AI and robotics that enables robots and vision AI agents to reason like humans, using prior knowledge, physics understanding and common sense to understand and act in the real world. This model understands space, time, and fundamental physics, and can serve as a planning model to reason what steps an embodied agent might take next. New features with Cosmos Reason 2: Enhanced physical AI reasoning with improved spatio-temporal understanding and timestamp precision. Supports object detection with 2D/3D point localization and bounding box coordinates with reasoning explanations and… Access requested at publisher other 2.4B parameters cosmos [View model](https://savrn.com/models/cosmos-reason2-2b) Model · Image and text to text ### [Qwen3.8-27B-NVFP4-RTX5090](https://savrn.com/models/qwen3-8-27b-nvfp4-rtx5090) [Gittensor Model Hub](https://savrn.com/model-publishers/gittensor-model-hub) Runs on SparkInfer, SGLang and vLLM unmodified — configs for all three are below. Serving many users at once? See concurrency. SparkInfer × this NVFP4 build × the DSpark v2 drafter — an engine, a checkpoint, and a speculative drafter optimized against each other, compounding to 4.3×. The drafter never changes what the model says: the target verifies every drafted token. GeForce RTX 5090–specific NVFP4 checkpoint of Qwen/Qwen3.8-27B, quantized with NVIDIA Model Optimizer. Serves the full native 262,144-token context on 32 GB. With the DSpark v2 drafter: 264.8 tok/s overall — up to 420 on code — on SparkInfer (its bench harness; the HTTP server is autoregressive-only today) and 161.7 tok/s on… Open weights apache-2.0 14.6B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-8-27b-nvfp4-rtx5090) Model · Image and text to text ### [vllm-translategemma-4b-it](https://savrn.com/models/vllm-translategemma-4b-it) [Infomaniak Network SA](https://savrn.com/model-publishers/infomaniak-ai) This is a modified version of google/translategemma-4b-it optimized for deployment with vLLM. The original TranslateGemma model requires a structured payload with dedicated sourcelangcode and targetlangcode fields: However, vLLM does not support these custom content parameters. To maintain compatibility, the chat template has been modified to encode language codes directly in the message content using a delimiter-based format: Format: >>{sourcelang} >>{targetlang} >>{texttotranslate} If you need to provide a custom prompt input The original model uses the new Transformers RoPE configuration format with separate attention type settings: This has been simplified for vLLM compatibility: The… Open weights gemma 5B parameters 131,072 tokens transformers [View model](https://savrn.com/models/vllm-translategemma-4b-it) Model · Image and text to text ### [blip2-opt-2.7b](https://savrn.com/models/blip2-opt-2-7b) [Salesforce AI Research](https://savrn.com/model-publishers/salesforce) BLIP-2 model, leveraging OPT-2.7b (a large language model with 2.7 billion parameters). It was introduced in the paper BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models by Li et al. and first released in this repository. Disclaimer: The team releasing BLIP-2 did not write a model card for this model so this model card has been written by the Hugging Face team. BLIP-2 consists of 3 models: a CLIP-like image encoder, a Querying Transformer (Q-Former) and a large language model. The authors initialize the weights of the image encoder and large language model from pre-trained checkpoints and keep them frozen while training the Querying… Open weights mit 3.7B parameters transformers [View model](https://savrn.com/models/blip2-opt-2-7b) Model · Image and text to text ### [dots.mocr](https://savrn.com/models/dots-mocr) [Dots Studio](https://savrn.com/model-publishers/dots-studio) dots.mocr We present dots.mocr. Beyond achieving state-of-the-art (SOTA) performance in standard multilingual document parsing among models of comparable size, dots.mocr excels at converting structured graphics (e.g., charts, UI layouts, scientific figures and etc.) directly into SVG code. Its core capabilities encompass grounding, recognition, semantic understanding, and interactive dialogue. Simultaneously, we are releasing dots.mocr-svg, a variant specifically optimized for robust image-to-SVG parsing tasks. More information can be found in the paper. Visual languages (e.g., charts, graphics, chemical formulas, logos) encapsulate dense human knowledge. dots.mocr unifies the… Open weights mit 3B parameters 131,072 tokens dots_mocr [View model](https://savrn.com/models/dots-mocr) Model · Image and text to text ### [Qwen3.8-27B-nvfp4-NInfer](https://savrn.com/models/qwen3-8-27b-nvfp4-ninfer) [Neroued](https://savrn.com/model-publishers/neroued) This model card is the version-controlled source for The repository contains a mixed NVFP4/FP8 representation of Qwen3.8-27B. It combines the official BF16 checkpoint with the fixed packed Text weights from unsloth/Qwen3.8-27B-NVFP4 in the native NInfer.ninfer artifact format. The artifact is intended only for NInfer; it is not a Transformers checkpoint, Safetensors distribution, or GGUF file. The artifact uses the Qwen3.5 Dense architecture. Text layers 0–55 use NVFP4 MLP weights, while the token embedding, attention input/output projections, GDN Q/K/V/Z and output projections, full output head, and Text layers 56–63 MLP weights use row-scaled FP8. Control weights use BF16, with separate… Open weights apache-2.0 ninfer [View model](https://savrn.com/models/qwen3-8-27b-nvfp4-ninfer) Model · Image and text to text ### [blip2-flan-t5-xl](https://savrn.com/models/blip2-flan-t5-xl) [Salesforce AI Research](https://savrn.com/model-publishers/salesforce) BLIP-2 model, leveraging Flan T5-xl (a large language model). It was introduced in the paper BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models by Li et al. and first released in this repository. Disclaimer: The team releasing BLIP-2 did not write a model card for this model so this model card has been written by the Hugging Face team. BLIP-2 consists of 3 models: a CLIP-like image encoder, a Querying Transformer (Q-Former) and a large language model. The authors initialize the weights of the image encoder and large language model from pre-trained checkpoints and keep them frozen while training the Querying Transformer, which is a… Open weights mit 3.9B parameters transformers [View model](https://savrn.com/models/blip2-flan-t5-xl) Model · Image and text to text ### [LocateAnything-3B](https://savrn.com/models/locateanything-3b) [NVIDIA](https://savrn.com/model-publishers/nvidia) LocateAnything is a vision-language model for fast and high-quality visual grounding, enabling precise object localization, dense detection, and point-based localization across diverse domains in both Enterprise Intelligence and Physical AI. The model adopts a generalist design, supporting tasks such as referring expression grounding, multi-object detection, GUI element grounding, and text localization, with strong performance in complex and cluttered scenes. Its core innovation, Parallel Box Decoding (PBD), predicts complete bounding box coordinates in a single parallel step rather than autoregressive token-by-token decoding, improving efficiency while preserving geometric consistency.… Open weights other 3.8B parameters 32,768 tokens transformers [View model](https://savrn.com/models/locateanything-3b) Model · Image and text to text ### [VLX-Seek-1.5-10B](https://savrn.com/models/vlx-seek-1-5-10b) [OmAI Lab](https://savrn.com/model-publishers/omlab) VLX-Seek-1.5-10B is the open-source 10B model in the VLX-Seek 1.5 family, designed for fine-grained perception and visual grounding in embodied scenarios. It targets practical settings such as drones, robots, robotic dogs, surveillance cameras, inspection systems, and other edge-side visual intelligence applications where a model must identify what is present, localize the right instance, and avoid grounding objects that are absent. Unlike coordinate-generation-based VLMs that directly decode bounding-box numbers, VLX-Seek reformulates localization as region retrieval and region reference. Candidate visual regions are represented as addressable entities, and the model answers by selecting… Open weights apache-2.0 10B parameters 262,144 tokens transformers [View model](https://savrn.com/models/vlx-seek-1-5-10b) Model · Image and text to text ### [Nanonets-OCR2-3B](https://savrn.com/models/nanonets-ocr2-3b) [Nanonets](https://savrn.com/model-publishers/nanonets) Nanonets-OCR2: A model for transforming documents into structured markdown with intelligent content recognition and semantic tagging Nanonets-OCR2 by Nanonets is a family of powerful, state-of-the-art image-to-markdown OCR models that go far beyond traditional text extraction. It transforms documents into structured markdown with intelligent content recognition and semantic tagging, making it ideal for downstream processing by Large Language Models (LLMs). Nanonets-OCR2 is packed with features designed to handle complex documents with ease: 1. Start the vLLM server. Check out Docstrange for more details. 1. Increasing the image resolution will improve model's performance. 2. For complex… Open weights 3.8B parameters 128,000 tokens transformers [View model](https://savrn.com/models/nanonets-ocr2-3b) Model · Image and text to text ### [PaddleOCR-VL-1.6](https://savrn.com/models/paddleocr-vl-1-6) [PaddlePaddle](https://savrn.com/model-publishers/paddlepaddle) PaddleOCR-VL-1.6: Expanding the Frontier of Document Parsing with Under-Optimized Region Refinement and Progressive Post-Training We introduce PaddleOCR-VL-1.6, an upgraded compact document parsing model built upon PaddleOCR-VL-1.5. PaddleOCR-VL-1.6 introduces a region-aware data optimization framework that identifies weak regions from the previous model, applies targeted enhancement to those regions, and improves the reliability of supervision signals. It further adopts a progressive post-training recipe based on curated data selection and reinforcement learning, pushing model performance to a higher level through staged optimization. PaddleOCR-VL-1.6 achieves a new state-of-the-art score… Open weights apache-2.0 959M parameters 131,072 tokens PaddleOCR [View model](https://savrn.com/models/paddleocr-vl-1-6) Model · Image and text to text ### [Qari-OCR-v0.3-VL-2B-Instruct](https://savrn.com/models/qari-ocr-v0-3-vl-2b-instruct) [Network for Advancing Modern ArabicNLP & AI](https://savrn.com/model-publishers/namaa-space) QARI-OCR v0.3 is a specialized vision-language model fine-tuned for Arabic Optical Character Recognition with a focus on structural document understanding. - Built on Qwen2-VL-2B-Instruct, this model excels at preserving document layouts, HTML tags, and formatting while transcribing Arabic text. - It is described in detail in the paper QARI-OCR: High-Fidelity Arabic Text Recognition through Multimodal Large Language Model Adaptation. While QARI v0.2 achieves better raw text accuracy (CER: 0.061), QARI v0.3 excels in: - HTML/Markdown structure preservation - Document layout understanding - Handwritten text recognition (initial capabilities) - 5x faster training than v0.2 You can load this… Open weights apache-2.0 2.2B parameters 32,768 tokens transformers [View model](https://savrn.com/models/qari-ocr-v0-3-vl-2b-instruct) Model · Image and text to text ### [Qwen3.8-27B-Uncensored](https://savrn.com/models/qwen3-8-27b-uncensored) [OrcaRouter](https://savrn.com/model-publishers/orcarouter) This model has had its safety alignment substantially removed via abliteration (orthogonalizing the refusal direction out of the residual stream). As a direct consequence: - It will comply with harmful, unethical, offensive, or illegal requests that the original Qwen3.8-27B would refuse. It has no meaningful built-in guardrails. - It is released strictly for legitimate research — interpretability, AI-safety and refusal-mechanism study, red-teaming, robustness evaluation, and controlled experiments. - You assume full responsibility and liability for how you use it and for everything it generates. Do not deploy it to end users or in production without adding your own safety, moderation, and… Access requested at publisher apache-2.0 27.8B parameters transformers [View model](https://savrn.com/models/qwen3-8-27b-uncensored) Model · Image and text to text ### [PaddleOCR-VL-1.5](https://savrn.com/models/paddleocr-vl-1-5) [PaddlePaddle](https://savrn.com/model-publishers/paddlepaddle) PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing PaddleOCR-VL-1.5 is an advanced next-generation model of PaddleOCR-VL, achieving a new state-of-the-art accuracy of 94.5% on OmniDocBench v1.5. To rigorously evaluate robustness against real-world physical distortions—including scanning artifacts, skew, warping, screen photography, and illumination—we propose the Real5-OmniDocBench benchmark. Experimental results demonstrate that this enhanced model attains SOTA performance on the newly curated benchmark. Furthermore, we extend the model’s capabilities by incorporating seal recognition and text spotting tasks, while remaining a 0.9B ultra-compact VLM… Open weights apache-2.0 959M parameters 131,072 tokens PaddleOCR [View model](https://savrn.com/models/paddleocr-vl-1-5) Model · Image and text to text ### [ProcVLM-2B](https://savrn.com/models/procvlm-2b) [Youhe Feng](https://savrn.com/model-publishers/ce-amtic) ProcVLM-2B is a procedure-grounded vision-language model for estimating progress rewards from robot manipulation observations. Given a task description and a recent window of video frames, the model reasons about the remaining atomic actions and predicts the current task completion percentage. ProcVLM-2B is designed for research on robot learning, progress reward modeling, embodied evaluation, and procedure-aware video understanding. Typical use cases include: - estimating task completion progress from robot videos; - producing dense progress rewards from sparse demonstrations; - adapting progress prediction to a new environment with one-shot LoRA fine-tuning. This model is not intended to… Open weights cc-by-4.0 2.4B parameters 262,144 tokens transformers [View model](https://savrn.com/models/procvlm-2b) Model · Image and text to text ### [Qwen3.8-Flash-Next-Uncensored-NVFP4](https://savrn.com/models/qwen3-8-flash-next-uncensored-nvfp4) [OrcaRouter](https://savrn.com/model-publishers/orcarouter) This model has had its safety alignment substantially removed via abliteration (orthogonalizing the refusal direction out of the residual stream). It will comply with harmful, unethical, or illegal requests the original Qwen3.8-Flash-Next would refuse. Released strictly for legitimate research — interpretability, AI-safety / refusal-mechanism study, red-teaming, and robustness evaluation. You assume full responsibility for how you use it and everything it generates; add your own safety and moderation layers before any deployment. Use must comply with the Apache 2.0 License inherited from the base model and all applicable law. The authors accept no liability for misuse. - A Blackwell GPU… Access requested at publisher apache-2.0 180B parameters transformers [View model](https://savrn.com/models/qwen3-8-flash-next-uncensored-nvfp4) ## Questions ### Which Image and text to text models are most downloaded? By monthly downloads reported by the Hugging Face Hub: gemma-4-26B-A4B-it (9.4M); gemma-4-31B-it (8.6M); Qwen3.8-27B (7.8M). ## Other Tasks - [Text Generation](https://savrn.com/models/tasks/text-generation) 1548 - [Feature Extraction](https://savrn.com/models/tasks/feature-extraction) 398 - [Robotics](https://savrn.com/models/tasks/robotics) 398 - [Text Classification](https://savrn.com/models/tasks/text-classification) 331 - [Text to Image](https://savrn.com/models/tasks/text-to-image) 232 - [Speech Recognition](https://savrn.com/models/tasks/speech-recognition) 187 - [Text to Speech](https://savrn.com/models/tasks/text-to-speech) 145 - [Image Classification](https://savrn.com/models/tasks/image-classification) 139 - [Audio Classification](https://savrn.com/models/tasks/audio-classification) 130 - [Sentence Similarity](https://savrn.com/models/tasks/sentence-similarity) 103 - [Reinforcement Learning](https://savrn.com/models/tasks/reinforcement-learning) 100 - [Token Classification](https://savrn.com/models/tasks/token-classification) 100 [See all](https://savrn.com/models/tasks)