This is a quantized version of the vanta-research/atom-v1-preview-12b model, offering a variety of quantization types. It is suitable for multiple scenarios such as dialogue, collaboration, and education. This model is based on the Gemma architecture and supports English language processing.

MultimodalTransformersEnglish

atom-v1-preview-12b is a quantized version based on the vanta-research/atom-v1-preview-12b base model, offering multiple GGUF quantization format options, suitable for multiple domains such as dialogue, collaboration, and education.

Natural Language ProcessingTransformersEnglish

This is a Dutch patient simulation model fine-tuned based on Google's Gemma 2 9B model, specifically designed for medical education scenarios. The model always responds in a fixed JSON structure, simulating a patient 'Maria' who has just completed surgery and is in the recovery process, supporting nursing students to exercise communication and clinical reasoning skills in a safe environment.

Natural Language ProcessingGgufOther

This project provides a quantized version of the cerebras/MiniMax-M2-REAP-172B-A10B model, aiming to make knowledge accessible to the public. This is a large language model with 172 billion parameters, which has been optimized and quantized to reduce deployment costs and improve inference efficiency.

Natural Language ProcessingGguf

This is the MXFP4_MOE quantized version of the MiniMax-M2-REAP-172B-A10B model, which is a memory-efficient compressed model. Through the REAP (Routing-Weighted Expert Activation Pruning) method, the model is compressed from 230B parameters to 172B parameters while maintaining performance, with a 25% reduction in size. It is suitable for resource-constrained environments, local deployment, and academic research.

Natural Language ProcessingGguf

This is a quantized version of the kldzj/gpt-oss-120b-heretic model, quantized using llamacpp. It offers a variety of quantization types, including special formats such as BF16, Q8_0, and MXFP4_MOE, significantly improving the model's operating efficiency.

Natural Language ProcessingGguf

VibeThinker-1.5B-GGUF is the GGUF quantized version of the WeiboAI/VibeThinker-1.5B model, quantized by MaziyarPanahi. This model is a text generation model with 1.5B parameters, supporting multiple quantization levels (from 2-bit to 8-bit), which facilitates efficient operation on various hardware.

Natural Language ProcessingGguf

VieNeu-TTS-1000h is an advanced Vietnamese edge-side text-to-speech model. It is trained on approximately 1000 hours of high-quality Vietnamese speech data, has an instant voice cloning function, supports seamless switching between Vietnamese and English, and can synthesize 24kHz waveforms in real-time on CPU or GPU.

Audio ProcessingTensorboardOther

This is a quantized version of the Qwen3-4B-Instruct-2507-heretic model. Multiple quantization types of model files are generated using the llama.cpp tool and the imatrix dataset, which can run in various environments and provide users with more options.

Natural Language ProcessingGguf

This is a quantized version of the p-e-w/gpt-oss-20b-heretic model, quantized using the imatrix technology of llama.cpp. The model is a large language model with 20 billion parameters, offering a variety of quantization options ranging from high quality to low quality, with file sizes from 41.86GB to 11.52GB, suitable for different hardware conditions.

Natural Language ProcessingGguf

This is a statically quantized version of the adith-ds/emotion-classifier-v2 model, offering various GGUF quantization formats suitable for emotion classification tasks. The model is optimized for efficient operation in different hardware environments.

Natural Language ProcessingTransformersEnglish

Qwen3-32B is the latest generation of large language models in the Qwen series, with powerful reasoning, instruction-following, intelligent agent interaction, and multilingual processing capabilities. It supports over 100 languages and dialects and can seamlessly switch between thinking and non-thinking modes, providing users with a natural and smooth dialogue experience.

Natural Language ProcessingTransformers

This project provides a static quantized version of the Qwen-4B-Instruct-2507-Self-correct model, supporting tasks such as text generation, bias mitigation, and self-correction. Based on the Qwen-4B architecture, the model has undergone instruction fine-tuning and self-correction training, offering multiple quantized versions to meet different hardware requirements.

Natural Language ProcessingTransformersEnglish

This is an English-Spanish translation model fine-tuned on the Amazon dataset based on the Google mT5-small model, specifically designed for text abstract generation tasks. The model achieved scores of Rouge1: 16.44 and Rouge2: 8.04 on the evaluation set.

Natural Language ProcessingTransformers

Fyodor-StarCoder2-7B-MoE is an enhanced version based on StarCoder2-7B, adopting a Mixture of Experts architecture with 3 MoE layers, 6 expert networks, and a top-2 routing mechanism. It performs excellently in code generation, function calls, and agent tasks.

Natural Language ProcessingTransformersOther

This is a large language model with 30 billion parameters trained on the Claude Sonnet 4.5 reasoning dataset. It is specifically optimized for high-difficulty reasoning tasks and performs excellently in the fields of coding and science.

Natural Language ProcessingTransformersEnglish

This is a Transformer model uploaded to the Hugging Face model hub. Specific functions and features require further information supplementation.

Natural Language ProcessingTransformers

Trouper-12B GGUF is a text generation model developed by DarwinAnim8or based on the original model. It is specifically optimized for role-playing and creative writing scenarios. This model is fine-tuned from Mistral-Nemo-Base-12B and trained using a custom 'Actors' dataset. The generated text is more natural and avoids the common 'AI language' features.

Natural Language ProcessingGgufEnglish

This is an experimental mixed quantization model that uses the MXFP4_MOE mixed weight technology. While maintaining accuracy close to Q8, it achieves a smaller file size and higher inference speed. The model explores the combination of MXFP4 with high-precision embedding/output weights and achieves near-lossless optimization of accuracy on dense models.

Natural Language ProcessingGgufOther

NVIDIA Nemotron Parse v1.1 is an advanced document parsing model specifically designed to understand document semantics and extract text and table elements with spatial positioning. It can convert unstructured documents into machine-readable structured representations, overcoming the limitations of traditional OCR in handling complex document layouts.

MultimodalTransformers