This is a multi-task fine-tuned model based on DeepSeek-R1-Distill-Qwen-1.5B. It is trained on multiple datasets through the LoRA adapter and has powerful multi-task generalization and reasoning capabilities. It can handle a wide range of natural language and reasoning-based tasks.

Natural Language ProcessingPeftEnglish

This is a quantized version of the internlm's JanusCoder-14B model. It is quantized using specific tools and datasets, providing a variety of quantization type files from low quality to high quality. It can run in LM Studio or projects based on llama.cpp.

Natural Language ProcessingGguf

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

PyLate is a tool library focused on sentence similarity calculation and information retrieval. It can perform efficient information retrieval tasks on multiple datasets, providing strong support for research and applications in related fields. This model supports 8 languages and performs excellently in multiple benchmark tests.

Natural Language ProcessingSafetensorsMultiple Languages

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

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

OLGA is an online reinforcement learning positioning agent built on Qwen3-VL-30B-A3B-Instruct, using a mixture-of-experts model with 3.3 billion active parameters. It is trained through a new data recipe that combines existing datasets, new data collection, automatic filtering, and online reinforcement learning, achieving advanced positioning performance among open-source models.

MultimodalTransformersEnglish

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

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 supervised fine-tuning, reinforcement learning, and model merging techniques. It has powerful instruction-following capabilities and tool invocation functions.

Natural Language ProcessingTransformers

Granite-4.0-1B is a lightweight instruction model developed by IBM. It is fine-tuned based on Granite-4.0-1B-Base, combining open-source instruction datasets and internal synthetic datasets, and developed using supervised fine-tuning, reinforcement learning, and model merging techniques.

Natural Language ProcessingTransformers

Granite-4.0-H-350M is a lightweight instruction model developed by IBM, fine-tuned based on Granite-4.0-H-350M-Base. This model combines open-source instruction datasets and internal synthetic datasets, and is developed using various technologies such as supervised fine-tuning, reinforcement learning, and model merging. It has powerful instruction-following capabilities and multilingual support.

Natural Language ProcessingTransformers

Wan2.2 is a significant upgrade version of the basic video model. It introduces the Mixture of Experts (MoE) architecture, incorporates carefully curated aesthetic data, and is trained on larger datasets to enhance the ability to generate complex motions. This model supports generating 5-second videos with 480P and 720P resolutions, and has significant improvements in video generation quality and performance.

MultimodalDiffusers

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

Granite-4.0-H-Tiny is a long-context instruction model developed by IBM with 7 billion parameters, fine-tuned based on Granite-4.0-H-Tiny-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 enhanced instruction-following and tool invocation capabilities, making it particularly suitable for enterprise-level applications.

Natural Language ProcessingTransformers

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-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

GTA1 is a state-of-the-art GUI grounding model trained based on reinforcement learning (GRPO), specifically designed for graphical user interface automation tasks. Different from methods that rely on long thought-chain reasoning, GRPO directly incentivizes actionable and well-founded responses, demonstrating excellent grounding performance and agent performance on multiple challenging datasets.

MultimodalTransformersEnglish

This is a Japanese pre - trained language model based on the Mistral 300M architecture. It is trained using the Wikipedia and cc100 datasets, and the byte - fallback technique in the SentencePiece tokenizer is adopted to suppress the generation of unknown words.

Natural Language ProcessingTransformers

Granite-4.0-H-Tiny is a 7-billion parameter long context instruction model developed by IBM, fine-tuned from Granite-4.0-H-Tiny-Base. This model is trained by combining open-source instruction datasets and internal synthetic datasets, and has the ability to provide professional, accurate, and secure responses. It supports multiple languages and tool invocation, and is suitable for enterprise-level applications.

Natural Language ProcessingTransformers