The Brazilian scope routing model is a fine-tuned micro large language model based on Gemma 3 270M, specifically designed for the Brazilian food delivery service system. It can serve as an extremely fast and lightweight intent classifier (router). This model is trained using Unsloth and converted to the GGUF format.

Natural Language ProcessingSafetensorsOther

Hunyuan OCR is an end-to-end OCR expert VLM model driven by the Hunyuan native multimodal architecture. With a lightweight design of only 1 billion parameters, it has achieved state-of-the-art results in multiple industry benchmark tests. This model excels in handling complex multilingual document parsing and performs well in practical application scenarios such as text localization, open-domain information extraction, video subtitle extraction, and image translation.

MultimodalTransformersMultiple Languages

ZygAI is a fast local large language model designed specifically for Lithuanian and English tasks and is in the testing phase. As a lightweight bilingual assistant, it can run in real-time on local hardware, providing users with efficient and accurate bilingual services.

Natural Language ProcessingGgufMultiple Languages

This is a lightweight sentence embedding model based on the MiniLM architecture, specifically designed to generate high-quality sentence vector representations. The model is trained using MultipleNegativesRankingLoss and performs excellently in sentence similarity calculation and feature extraction tasks.

Natural Language ProcessingSentence-transformers

Granite-4.0-H-350M-Base is a lightweight decoder-only language model developed by IBM, designed for resource-constrained devices, supporting multilingual text generation and code completion functions.

Natural Language ProcessingTransformers

Granite-4.0-H-350M is a lightweight instruction model developed by IBM with 350M parameters. It performs excellently in multilingual processing and instruction following, and is designed for device-side deployment and research scenarios.

Natural Language ProcessingTransformers

This is a sequence-to-sequence model specifically designed to compress short user prompts (≤64 tokens) and can achieve low-latency operation of less than 100 milliseconds on modern GPUs. It serves as a lightweight preprocessing stage to support high-capacity large language models.

Natural Language ProcessingTransformers

brats_medgemma_light is a fusion model based on unsloth/medgemma-4b-it, fine-tuned on the BraTS and TextBraTS datasets. It is a lightweight vision-language model specifically designed for brain MRI interpretation and radiology text generation.

MultimodalTransformersEnglish

This is a lightweight text summary generation model based on the Google T5 Efficient Tiny architecture. It is trained using the shorecode/summary-collection-200k-rows dataset and is specifically designed for automatic text summarization tasks. It features efficient inference and low resource consumption.

Natural Language ProcessingTransformersEnglish

Granite-4.0-350M-Base is a lightweight decoder-only language model developed by IBM, designed specifically for resource-constrained devices, supporting multilingual text generation and code completion functions.

Natural Language ProcessingTransformers

Granite-4.0-1B-Base is a lightweight decoder-only language model developed by IBM, designed specifically for resource-constrained devices, supporting multilingual text generation and code completion functions.

Natural Language ProcessingTransformers

Granite - 4.0 - H - 350M - Base is a lightweight decoder - only language model developed by IBM, specifically designed for resource - constrained devices. It supports multi - language text generation and code completion functions and can run efficiently on smartphones or IoT devices.

Natural Language ProcessingTransformers

Granite-4.0-H-1B-Base is a lightweight decoder-only language model developed by IBM, designed specifically for resource-constrained devices, supporting multi-language text generation and code completion functions.

Natural Language ProcessingTransformers

A lightweight medical fine-tuned model based on Arcee's AFM-4.5B, optimized using a three-stage training method (SFT→DPO→GRPO-CoT), specifically designed for medical education and research scenarios.

Natural Language ProcessingTransformersMultiple Languages

EdgeTAM is a lightweight variant of SAM 2, designed for on-device video segmentation and tracking. It is 22 times faster than SAM 2, reaching 16 FPS on the iPhone 15 Pro Max, and supports real-time video object segmentation and cross-frame tracking.

Computer VisionTransformers

Qwen2.5-14B-Instruct is a large language model with 14 billion parameters, designed specifically for chat and text generation scenarios. This model is built based on the transformers library and is suitable for internal testing and lightweight application deployment.

Natural Language ProcessingTransformersEnglish

GA Guard Lite is a lightweight open-source moderation model developed by General Analysis, specifically designed to detect prohibited content in the outputs of language models. This model can identify seven categories of security risks, including illegal activities, hate speech, and personal information leakage, providing efficient content security protection for AI applications.

Natural Language ProcessingTransformersEnglish

A lightweight AI-generated text detection model based on the BERT-tiny architecture, specifically designed to identify AI-generated text content from the RAID dataset. The model's output score represents the probability that the text is human-generated.

Natural Language ProcessingTransformers

Jan-v1-AIO-GGUF contains two powerful language models: Jan-v1-4B and Jan-v1-edge. Jan-v1-4B is based on the Qwen3-4B-thinking architecture, with 4 billion parameters. It has strong intelligent reasoning, problem-solving, and tool-using capabilities and supports web search tasks. Jan-v1-edge is a lightweight intelligent model designed for fast execution on devices and is suitable for resource-constrained environments.

Natural Language ProcessingTransformersEnglish

embeddinggemma-300M is a lightweight text embedding model optimized through quantization. Based on Google's embeddinggemma architecture, it uses QAT (Quantization-Aware Training) and Q4_0 quantization techniques, with 300M parameters. This model is specifically designed to generate high-quality text embedding vectors and supports tasks such as sentence similarity calculation and feature extraction.

Natural Language ProcessingSentence-transformers