The INL architecture is a production-level neural architecture based on integral neuron dynamics, replacing traditional feed-forward network layers with iterative dynamics. It is suitable for various types of models such as large language models, vision transformers, and multi-modal models.

Natural Language ProcessingSafetensorsEnglish

Bee-8B is an advanced fully open-source multimodal large language model that focuses on data quality and aims to narrow the performance gap with proprietary models. By using the high-quality Honey-Data-15M corpus and the advanced data processing pipeline HoneyPipe, it demonstrates excellent performance in complex reasoning and other aspects.

MultimodalTransformersEnglish

This is the first - generation powerful vibe - code large language model, optimized to directly generate natural language and code completion based on loosely structured 'atmosphere coding' prompts. Compared with early models, it has lower prompt engineering overhead, smoother latent space interpolation, and is easier to guide the generation of usable code.

Natural Language ProcessingTransformersEnglish

The GA Guard series is an open-source weight review model designed to help developers and organizations maintain the security, compliance, and consistency of language models with the real world. This model can detect seven violation categories, including illegal activities, hate and abuse, personal identity information and intellectual property, prompt security, pornographic content, false information, and violence and self-harm.

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

GA Guard Thinking is an open-source moderation model with 4 billion parameters developed by General Analysis. It is specifically designed to detect and prevent language models from generating unsafe and non-compliant content. The model is trained on seven security categories and can effectively identify risky content such as illegal activities, hate speech, and personal information leakage.

Natural Language ProcessingTransformersEnglish

Granite 4.0 H-Small (FP8) is a small FP8 quantized version of the Granite series of language models developed by IBM, designed to provide professional, accurate, and secure responses. The model uses the FP8 precision format, optimizing inference efficiency while maintaining performance.

Natural Language ProcessingTransformers

trlm-135m is a small research prototype language model with 135 million parameters. It is built on SmolLM2-135M-Instruct and has undergone a three-stage fine-tuning process to explore how small language models can acquire step-by-step reasoning abilities.

Natural Language ProcessingTransformersEnglish

The Transformer Library is a powerful natural language processing tool library that provides a rich set of pre-trained models and convenient usage interfaces. It is widely used in NLP tasks such as text generation, classification, and translation, greatly improving development efficiency.

Natural Language ProcessingTransformers

PatchFM is a basic model for univariate time series forecasting based on the Transformer architecture. It uses a block-by-block forecasting method, drawing on the training ideas of large language models to divide the time series into blocks for next-block forecasting. It supports multi-quantile output and uncertainty estimation.

Scientific ComputingSafetensors

QuestA is an innovative framework that enhances the reasoning ability of large language models through a question-enhanced approach. It integrates partial solutions during the reinforcement learning training process, significantly improving the model's performance on complex tasks such as mathematical reasoning, especially achieving optimal results on small-parameter models.

Natural Language ProcessingTransformers

CoDA is a code generation language model based on diffusion models developed by Salesforce AI Research. It has the ability to understand bidirectional context and is specifically designed for powerful code generation and completion tasks. This model only has 1.7 billion parameters and achieves excellent code generation performance while maintaining low computational requirements.

Natural Language ProcessingTransformersEnglish

ColModernVBERT is a late interaction version of ModernVBERT, specifically fine - tuned for the visual document retrieval task and is the best - performing model on this task. It is a compact vision - language encoder with 250 million parameters, achieving performance close to that of models with 10 times more parameters in visual document benchmark tests, while having a considerable inference speed on the CPU.

MultimodalSafetensorsEnglish

VLA-Adapter is a micro vision-language-action model trained on Libero-Spatial. It adopts the Prismatic-VLM architecture and only uses Qwen2.5-0.5B as the large language model backbone. This model outperforms open-source VLA models with larger parameter scales in robot benchmarks, achieving high-performance vision-language-action understanding and execution.

MultimodalSafetensorsEnglish

AcTora is an advanced language model and Python library designed to predict user interactions on social media. This project was developed by AmrTweg with the support of RdivxeAI and MechaML, aiming to apply language models to marketing research.

Natural Language ProcessingTransformers

Isaac 0.1 is the first perceptual language model launched by PerceptronAI. It has 2 billion parameters and is designed for real-world applications. This model sets a new standard in efficiency, with performance reaching or exceeding that of models more than 50 times larger. It has powerful visual understanding and spatial reasoning capabilities.

MultimodalTransformers

MiMo Audio is an audio language model developed by Xiaomi, which demonstrates strong few-shot learning ability through large-scale pre-training. This model breaks through the limitations of traditional models that rely on fine-tuning for specific tasks and performs excellently in tasks such as speech intelligence and audio understanding, reaching an advanced level among open-source models.

Audio ProcessingSafetensors

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