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 distilled model based on the Qwen3 30B A3B model, trained on the Claude Sonnet 4.5 high-reasoning-difficulty dataset. This model is specifically optimized for complex reasoning tasks and performs excellently in the fields of coding and science, with strong logical reasoning ability.

Natural Language ProcessingTransformersEnglish

GLM 4.5 Air is an efficient text generation model developed by Zai-Org. It has been quantized by bartowski using llama.cpp in the GGUF format, significantly reducing hardware requirements while maintaining performance, making it suitable for running on consumer-grade hardware.

Natural Language ProcessingGguf

AFM-4.5B is a 4.5-billion parameter instruction-tuned model developed by Arcee.ai, designed specifically for enterprise-level applications. It supports multilingual processing and demonstrates excellent performance in various deployment environments from the cloud to the edge.

Natural Language ProcessingTransformersMultiple Languages

ERNIE-4.5-0.3B-PT is a text generation model based on ERNIE 4.5 technology with a parameter scale of 0.36B, capable of efficiently handling text generation tasks.

Natural Language ProcessingTransformersMultiple Languages

An advanced Vision Transformer model that can simultaneously perform high-precision age estimation and gender classification, achieving a gender classification accuracy of 94.3% and an average absolute age error of 4.5 years on the UTKFace dataset.

Computer VisionPytorchEnglish

Baidu ERNIE 4.5 0.3B is a bilingual text generation model based on the Transformer architecture, which performs excellently in general language understanding and generation tasks. It supports Chinese-English bilingual processing, and the context length can reach 128k tokens.

Natural Language ProcessingGgufMultiple Languages

The quantized version of Bielik-4.5B-v3.0-Instruct, based on a large Polish instruction fine-tuned language model with 4.5 billion parameters, optimized for efficient inference and deployment, and built with the concept of 'making knowledge accessible to everyone'.

Natural Language ProcessingGguf

Llama-SEA-LION-v3-70B-IT is a multilingual large language model specifically optimized for Southeast Asian languages. The model was fine-tuned in two stages on a dataset of approximately 12.3 million English instruction-completion pairs and 4.5 million Southeast Asian language instruction-completion pairs, supporting 13 languages including Burmese, Chinese, English, Filipino, Indonesian, etc.

Natural Language ProcessingTransformersMultiple Languages

A speech encoder based on the Conformer architecture, pretrained on 4.5 million hours of unlabeled audio data, supporting over 143 languages

Audio ProcessingTransformersMultiple Languages

TituLM-1B-BN-V1 is a large language model specifically trained for generating and understanding Bengali text, extensively trained on a dataset containing 4.51 billion Bengali tokens.

Natural Language ProcessingTransformersOther

This is a Bengali automatic speech recognition model based on the Whisper small architecture, fine-tuned on approximately 400 hours of Mozilla Common Voice dataset with a word error rate of 4.58%

Audio ProcessingTransformers

Monolingual RoBERTa-base model for Slovak, pretrained on a 4.5GB cleaned Slovak news corpus

Natural Language ProcessingTransformersOther

XGLM-4.5B is a multilingual autoregressive language model with 4.5 billion parameters, trained on a balanced corpus of 134 languages.

Natural Language ProcessingTransformersMultiple Languages