The Pikachu Language Model is a unique experimental language project initiated by Clemylia. It is trained completely from scratch and is based solely on the sound corpus of 'Pikachu', demonstrating the ability of small language models to create a native language identity.

Natural Language ProcessingTransformers

Gheya-1 is a new-generation basic language model in the LES-IA-ETOILES ecosystem, with 202 million parameters. It is an upgraded version of the old Small-lamina series. This model is designed specifically for professional fine-tuning and has targeted training in the fields of artificial intelligence, professional language models, and biology.

Natural Language ProcessingTransformersFrench

This project is a medical domain model created using pretrained language model fusion technology. By fusing multiple medical-related pretrained models, it is specifically optimized for medical Q&A tasks, improving performance and effectiveness in medical scenarios.

Natural Language ProcessingSafetensorsEnglish

VibeThinker-1.5B is a 1.5-billion-parameter dense language model launched by Weibo AI. It is fine-tuned based on Qwen2.5-Math-1.5B and is specifically designed for mathematical and algorithmic coding problems. Trained using the 'Spectrum to Signal Principle' framework, it outperforms larger models in multiple math competition tests. The training cost is approximately $7,800, and it supports an output of up to about 40k tokens.

Natural Language ProcessingTransformersMultiple Languages

Olmo 3 is a series of language models developed by the Allen Institute for AI, including two scales of 7B and 32B, with two variants: instructional and reflective. This model performs excellently in long-chain thinking and can effectively improve the performance of reasoning tasks such as mathematics and coding. It adopts a multi-stage training method, including supervised fine-tuning, direct preference optimization, and reinforcement learning with verifiable rewards.

Natural Language ProcessingTransformersEnglish

Olmo 3 is a series of language models developed by the Allen Institute for AI (Ai2), including two specifications of 7B and 32B, with two variants of Instruct and Think. This model is based on the Transformer architecture and has the ability of long-chain thinking, which can effectively improve the performance of reasoning tasks such as mathematics and coding.

Natural Language ProcessingTransformersEnglish

Olmo 3 is a new family of language models developed by the Allen Institute for AI, including two scales of 7B and 32B, with two variants: Instruction and Think. This model uses long-chain thinking to improve performance in reasoning tasks such as mathematics and coding, aiming to promote the scientific development of language models.

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 is the result of merging pretrained language models using mergekit. It combines multiple pretrained models in the medical field, providing stronger support for medical Q&A tasks and effectively improving the accuracy and efficiency of answering medical questions.

Natural Language ProcessingSafetensorsEnglish

gpt-oss-safeguard-20b is a security inference model fine-tuned based on GPT-OSS-20b, specifically designed for input-output filtering of large language models, online content annotation, and offline annotation for trust and security use cases. This model uses the Apache 2.0 license and supports custom policies and a transparent decision-making process.

Natural Language ProcessingTransformers

Fara-7B is a small language model developed by Microsoft Research, specifically designed for computer usage scenarios. It has only 7 billion parameters and achieves excellent performance among models of the same scale. It can perform computer interaction tasks such as web automation and multimodal understanding.

MultimodalTransformersEnglish

GPT-OSS-Safeguard-20B is a security reasoning model with 21 billion parameters built on GPT-OSS, specifically optimized for security-related text content classification and filtering tasks. This model supports custom security policies, provides a transparent reasoning process, and is suitable for security use cases such as input-output filtering of large language models and online content annotation.

Natural Language ProcessingTransformers

FineCat-NLI Large is a natural language inference model fine-tuned based on the tasksource/ModernBERT-large-nli model. It is trained on the high-quality FineCat-NLI dataset, combining the advantages of multiple excellent models and performing outstandingly in NLI tasks while maintaining the efficient characteristics of the ModernBERT architecture.

Natural Language ProcessingSentence-transformersEnglish

PokeeResearch - 7B is a 7 - billion - parameter in - depth research agent model developed by Pokee AI. It combines Reinforcement Learning from AI Feedback (RLAIF) and a powerful reasoning framework, enabling reliable, aligned, and scalable research - level reasoning in tool - enhanced large language models. It is suitable for complex multi - step research workflows.

Natural Language ProcessingTransformersEnglish

This is a pre-trained language model that merges Vistral-24B-Instruct and Cydonia-24B-v4.2.0 using the SLERP method, specifically optimized to support bilingual processing capabilities in Russian and English. The model intelligently combines the advantages of the two base models, enhancing English performance while maintaining Russian capabilities.

Natural Language ProcessingTransformersMultiple Languages

This is a sequence-to-sequence model specifically designed to compress short user prompts (≤64 tokens) and can achieve low-latency operation of less than 100 milliseconds on modern GPUs. It serves as a lightweight preprocessing stage to support high-capacity large language models.

Natural Language ProcessingTransformers

Qwen3-Next-80B-A3B-Thinking-1M-qx64n-mlx is a large language model that adopts a mixture-of-experts architecture and Deckard mixed precision quantization technology. It has 80 billion parameters and a context length of 1M tokens. This model performs excellently in science and technology reasoning and long text processing, showing a 20 - 35% improvement in cognitive benchmark tests compared to instruction models.

Natural Language ProcessingMlx

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

dParallel is a learnable parallel decoding method for large language models that achieves fast sampling by exploiting the intrinsic parallelism of the model. This method can significantly reduce the decoding steps while maintaining the model performance, achieving 8.5 - 10.5 times acceleration in benchmarks such as GSM8K and MBPP.

Natural Language ProcessingTransformers

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