This is a lightweight text generation model based on the Meta LLaMA 3 8B model. It is efficiently fine-tuned using the Unsloth framework and converted to the GGUF format. The model is optimized for the Alpaca dataset, suitable for local deployment and inference, especially in resource-constrained environments.

Natural Language ProcessingGguf

Gemma 3n E4B IT is a lightweight multimodal open model launched by Google, built based on the same research as the Gemini model. This model supports text, audio, and visual inputs and is suitable for various tasks. It uses the MatFormer architecture to achieve efficient parameter utilization.

MultimodalTransformers

Gemma 3 is a lightweight open-source multimodal model launched by Google. This version is an instruction-tuned quantization-aware training model with 12B parameters, which has been converted to the MXFP4 format of the MLX framework. It supports text and image input and generates text output, with a 128K context window and support for over 140 languages.

MultimodalMlx

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 Video-1.5 is a lightweight and high-performance video generation model. With only 8.3 billion parameters, it can provide top-notch video quality, significantly lowering the usage threshold. It can run smoothly on consumer-grade GPUs, supporting text-to-video and image-to-video generation, enabling every developer and creator to use it easily.

MultimodalDiffusersMultiple Languages

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

A static quantized version of UME-R1-7B, supporting multiple tasks such as sentence similarity, embedding, zero-shot image classification, and video text-to-text. It offers various quantization types to meet different needs, from the lightweight Q2_K to the high-quality Q8_0 version.

MultimodalTransformersEnglish

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

This project performs 4-bit quantization on the MiniMax-M2 model using the DWQ (Dynamic Weight Quantization) method with the help of the mlx-lm library. This model is a lightweight version of MiniMax-M2, significantly reducing the model size while maintaining good performance.

Natural Language ProcessingMlx

SDXL-Deepfake-Detector is a tool for accurately detecting AI-generated faces. It focuses on maintaining the authenticity of the digital world and provides a privacy-protected and open-source solution to combat visual misinformation. This model achieves lightweight and highly accurate detection through fine-tuning a pre-trained model.

Computer VisionSafetensors

This project is the result of quantizing the MiniMax-M2 model to 3 bits using the mlx-lm library through Dynamic Weight Quantization (DWQ). It can efficiently perform text generation tasks under resource-constrained conditions, providing a more lightweight solution for related applications.

Natural Language ProcessingMlx

Lamapi/next-12b is a multilingual large language model with 12 billion parameters, offering multiple quantized versions and supporting various natural language processing tasks such as text generation, question answering, and chatting. This model is trained on multiple domain datasets and features high efficiency and lightweight characteristics.

Natural Language ProcessingTransformersMultiple Languages

The MLX MXFP4 quantized version of Gemma 3 27B IT QAT is a lightweight open - source multimodal model developed by Google. This model can simultaneously process text and image inputs and generate text outputs. It has a large context window of 128K and supports over 140 languages, making it suitable for various text generation and image understanding tasks.

MultimodalMlx

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

Granite-4.0-1B is a lightweight instruction model developed by IBM, fine-tuned based on Granite-4.0-1B-Base. This model combines open-source instruction datasets and internal synthetic datasets, and is developed using techniques such as supervised fine-tuning, reinforcement learning, and model merging. It is suitable for device-side deployment and research use cases.

Natural Language ProcessingTransformers

Granite-4.0-350M is a lightweight instruction model developed by IBM, fine-tuned based on Granite-4.0-350M-Base. This model combines open-source instruction datasets and internal synthetic datasets, and is developed using technologies such as supervised fine-tuning, reinforcement learning, and model fusion. It has powerful instruction-following capabilities and is particularly suitable for device-side deployment and research scenarios.

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