This is a text generation model that is specifically fine-tuned based on the Google Gemma-3 model. After training, it can use external tools to generate step-by-step plans. This model is trained on the rewoo/planner_instruction_tuning_2k dataset and uses LoRA technology for efficient fine-tuning.

Natural Language ProcessingTransformers

Nanbeige4-3B-Thinking is a 3-billion parameter inference model in the fourth-generation Nanbeige large language model family. It achieves advanced inference capabilities by improving data quality and training methods. This model performs excellently in multiple fields such as mathematics, science, creative writing, and tool usage, and supports multi-stage curriculum learning and reinforcement learning training.

Natural Language ProcessingTransformersMultiple Languages

This is a fine-tuned version of the mirth/chonky_distilbert_base_uncased_1 model. The model performance is improved by training on more data, and it is mainly used for text chunking and RAG-related tasks.

Natural Language ProcessingTransformersEnglish

Apertus is a fully open multi-language language model with a parameter scale of 70B and 8B. It supports over 1000 languages and long contexts, and only uses fully compliant and open training data. Its performance can be comparable to that of models trained with closed-source data.

Natural Language ProcessingTransformersOther

Apertus is a fully open multilingual large language model developed by Swiss AI, offering two parameter scales of 7 billion and 8 billion. This model supports over 1000 languages, uses fully compliant and open training data, and its performance can rival that of closed-source models. Apertus was pre-trained on 15T tokens and adopts a phased curriculum training method, supporting a context length of up to 65,536 tokens.

Natural Language ProcessingTransformersOther

Apertus is a fully open multilingual large language model developed by Swiss AI. It has parameter scales of 8 billion and 70 billion, supports over 1000 languages and long context processing, and only uses fully compliant open training data. Its performance can rival that of closed-source models.

Natural Language ProcessingTransformersOther

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

WEBGEN DEVSTRAL IMAGES is an AI model focused on web page generation. It can generate single-page web pages using HTML, CSS, JS, and Tailwind technologies. This project is trained based on custom templates and uses the supervised fine-tuning method, training with a dataset generated by GPT-OSS-120B.

Natural Language ProcessingTransformersEnglish

MiMo Audio is an audio language model based on large-scale pre-training, achieving SOTA performance among open-source models in speech intelligence and audio understanding benchmark tests. This model demonstrates strong few-shot learning ability and can generalize to tasks not included in the training data, supporting various audio tasks such as speech conversion, style transfer, and speech editing.

Audio ProcessingSafetensors

MobileLLM-R1 is a series of efficient inference language models released by Meta, focusing on solving mathematical, programming, and scientific problems. This model can still achieve excellent performance with a relatively small parameter scale, and provides a complete training recipe and data sources to support reproducible research.

Natural Language ProcessingTransformersEnglish

MobileLLM-R1 is a series of efficient inference models focused on mathematics, programming, and scientific problems, achieving excellent performance with less training data and providing a complete training recipe and data source.

Natural Language ProcessingTransformersEnglish

MiniCPM4.1-8B GGUF is an efficient large language model designed specifically for edge devices. It has carried out systematic innovations in four key dimensions: model architecture, training data, training algorithm, and inference system, achieving extreme efficiency improvement. It supports a context length of 65,536 tokens and a hybrid inference mode.

Natural Language ProcessingTransformersMultiple Languages

VaultGemma is a lightweight language model pre-trained by Google based on differential privacy technology, with powerful privacy protection capabilities. It uses differential privacy stochastic gradient descent (DP-SGD) for pre-training, providing mathematical privacy guarantees for the training data. It has less than 1 billion parameters and is suitable for a variety of natural language processing tasks.

Natural Language ProcessingTransformers

Kuaishou Keye-VL is a cutting - edge multimodal large language model developed by the Kuaishou Keye team, which performs excellently in video understanding, visual perception, and reasoning tasks. The 1.5 version reaches a new height in video understanding, image perception, and reasoning ability through innovative fast - slow video encoding strategies, LongCoT cold - start data pipelines, and reinforcement learning training strategies, and supports an extended context length of up to 128k tokens.

MultimodalSafetensorsMultiple Languages

Discord-Hermes-3-8B is a large language model fine-tuned based on NousResearch/Hermes-3-Llama-3.1-8B, specifically optimized for conversation scenarios. Through training on a carefully curated Discord conversation dataset, it improves conversation ability and the quality of chat interaction.

Natural Language ProcessingTransformers

A speculative decoding model specifically designed for Qwen/Qwen3-8B. It uses the EAGLE-3 algorithm to improve the efficiency and quality of text generation and obtains excellent generalization ability through training on multiple high-quality datasets.

Natural Language ProcessingTransformers

Qwen3-14B-Base is the latest generation of large language models in the Tongyi series, offering a comprehensive range of dense models and mixture-of-experts (MoE) models, and achieving significant progress in training data, model architecture, and optimization techniques.

Natural Language ProcessingTransformers

MiniCPM4-8B is an efficient large language model designed specifically for edge devices. Through innovations in four dimensions: model architecture, training data, training algorithms, and inference systems, it achieves extreme efficiency improvements.

Natural Language ProcessingTransformersMultiple Languages

MiniCPM4 is an efficient large - language model designed specifically for edge devices. Through systematic innovation, it achieves extreme efficiency improvements in four key dimensions: model architecture, training data, training algorithm, and inference system.

Natural Language ProcessingTransformersMultiple Languages

MiniCPM4 is an efficient large language model designed specifically for edge devices. Through systematic innovation, it achieves extreme efficiency improvements in four dimensions: model architecture, training data, training algorithm, and inference system. It can achieve over 5 times faster generation speed on edge chips.

Natural Language ProcessingTransformersMultiple Languages