This is a quantized version of the Ling-mini-2.0 model from inclusionAI, quantized using the imatrix technology of llama.cpp. It offers various quantization options from 32.54GB BF16 to 4.66GB IQ2_XS and can run efficiently in multiple hardware environments.

Natural Language ProcessingGguf

BEVANet is a deep learning model designed for real-time semantic segmentation, which performs excellently on datasets such as Cityscapes. It achieves an outstanding performance of 81.0% mIoU and 32.8 FPS on the RTX3090, balancing the requirements of accuracy and speed.

Computer VisionSafetensors

A2Search-3B-Instruct is a static quantized version based on zfj1998/A2Search-3B-Instruct, offering multiple quantization type options. This is a large language model with 3 billion parameters, specifically optimized for search and instruction - following tasks, and supports English language processing.

Natural Language ProcessingTransformersEnglish

This is a compact Spanish-to-English translation model optimized for accuracy and efficiency. The model size is 386MB, supporting a context length of 4096 tokens, and can efficiently and accurately complete translation tasks with an average response time of approximately 245ms.

Natural Language ProcessingGgufMultiple Languages

This is the 4-bit quantized MLX format version of the Qwen3-VL-30B-A3B-Instruct model, converted and maintained by mlx-community. This model is a multimodal visual language model with 30 billion parameters, supporting image understanding and text generation tasks.

MultimodalMlx

This is a BiEncoder model fine-tuned based on the sentence-transformers library, specifically optimized for the LangCache semantic caching task. The model maps sentences and paragraphs to a 384-dimensional vector space, supporting tasks such as semantic similarity calculation and semantic search.

Natural Language ProcessingSentence-transformersEnglish

Looking_Glass-llama is an intermediate model for testing, based on the llama 3 architecture and specifically trained for thinking tasks. This model is planned for merging and continuous training. Currently, there are certain repetition issues. It is recommended to use a higher temperature value and repetition penalty coefficient.

Natural Language ProcessingTransformers

GLM-4.6-AWQ is a model that performs high-performance AWQ quantization on Zhipu AI's GLM-4.6 (357B MoE). It is optimized for vLLM inference and can effectively improve the throughput of production deployment. This model uses 4-bit quantization technology, which significantly reduces video memory usage while maintaining high accuracy.

Natural Language ProcessingTransformersMultiple Languages

RND1 is an experimental diffusion language model with 30 billion parameters, adopting a sparse mixture of experts architecture. This model is converted from a pre-trained autoregressive base model, supporting diffusion-based text generation. Only 3 billion parameters are activated for each token, achieving a balance between computational efficiency and model capacity.

Natural Language ProcessingTransformers

IBM Granite-4.0-H-Tiny is a hybrid Mamba-2/Transformer model optimized for Apple silicon chips, using 3-bit quantization technology, and is designed for long context, efficient inference, and enterprise use. This model combines the Mamba-2 architecture with the Mixture of Experts (MoE) technology, significantly reducing memory usage while maintaining expressiveness.

Natural Language ProcessingMlxMultiple Languages

Granite-4.0-H-Small is a long-context instruction model developed by IBM with 32 billion parameters, fine-tuned based on Granite-4.0-H-Small-Base. This model combines open-source instruction datasets and internal synthetic datasets, and uses techniques such as supervised fine-tuning, reinforcement learning alignment, and model merging. It has significantly improved instruction following and tool invocation capabilities, and is particularly suitable for enterprise-level applications.

Natural Language ProcessingTransformers

Granite-4.0-H-Micro is a 3-billion parameter long-context instruction model developed by IBM, fine-tuned from Granite-4.0-H-Micro-Base. This model combines open-source instruction datasets and internal synthetic datasets, and is developed using techniques such as supervised fine-tuning, reinforcement learning alignment, and model merging. It has a structured chat format and performs excellently in instruction following and tool invocation capabilities.

Natural Language ProcessingTransformers

animat3d_style_wan-lora is a LoRA model trained based on AI Toolkit by Ostris, specifically designed for text-to-video generation, which can bring unique 3D animation style effects to image generation. This model is fine-tuned based on the Wan2.2-T2V-A14B base model and supports use on multiple mainstream AI platforms.

Computer VisionDiffusers

Granite-4.0-Micro is a long-context instruction model with 3 billion parameters developed by IBM, fine-tuned based on Granite-4.0-Micro-Base. This model combines open-source instruction datasets and internal synthetic datasets, and is developed using technologies such as supervised fine-tuning, reinforcement learning alignment, and model merging. It has enhanced instruction-following and tool invocation capabilities, and is particularly suitable for enterprise-level applications.

Natural Language ProcessingTransformers

Granite-4.0-H-Micro is a 3-billion-parameter long-context instruction model developed by IBM, fine-tuned from Granite-4.0-H-Micro-Base. This model is trained by combining an open-source instruction dataset and an internal synthetic dataset, and it has enhanced instruction-following and tool call capabilities, making it particularly suitable for enterprise-level applications.

Natural Language ProcessingTransformers

Mira is a text generation model based on the fusion of multiple Gemma 3 27B base models. Through carefully selected training data and specific training methods, it has a unique ability to generate poetic texts. This model performs excellently in role-playing and creative writing, and can generate texts with literary charm according to different system prompts.

Natural Language ProcessingTransformers

OpenLLaMA 3B v2 is an open-source large language model based on the Transformer architecture, with 3 billion parameters. This model uses the MIT license and is mainly used for English text generation tasks, supporting various application scenarios such as chatbots.

Natural Language ProcessingPeftEnglish

Granite-4.0-H-Small is a long-context instruction model with 32 billion parameters developed by IBM, fine-tuned based on Granite-4.0-H-Small-Base. This model combines open-source instruction datasets and internal synthetic datasets and is developed using techniques such as supervised fine-tuning, reinforcement learning alignment, and model merging, with significant improvements in instruction following and tool invocation capabilities.

Natural Language ProcessingTransformers

Granite-4.0-Micro is a long-context instruction model developed by IBM with 3 billion parameters, fine-tuned from Granite-4.0-Micro-Base. This model uses an open-source instruction dataset and an internal synthetic dataset, and has enhanced instruction-following and tool invocation capabilities. It supports multilingual tasks and can serve as a base model for AI assistants in various fields.

Natural Language ProcessingTransformers

PaDT Pro 3B is a multimodal large language model based on Qwen2.5VL-3B. It adopts a unified paradigm of decodable block tokens and can directly generate text and visual outputs, achieving advanced performance in multiple visual perception and understanding tasks.

MultimodalTransformersMultiple Languages