MiniMax-M2 is a small Mixture of Experts (MoE) model built specifically for maximizing coding and agent workflows. It has a total of 230 billion parameters, with 1 billion active parameters. This model excels in coding and agent tasks while maintaining strong general intelligence. It is compact, fast, and cost-effective.

Natural Language ProcessingTransformers

MiniMax-M2 is a Mixture of Experts model designed for efficient coding and agent workflows. It has a total of 230 billion parameters and 10 billion active parameters. This model performs excellently in coding and agent tasks, and features low latency, low cost, and high throughput, effectively improving work efficiency.

Natural Language ProcessingTransformers

MiniMax-M2 is a small Mixture of Experts (MoE) model built specifically for maximizing coding and agent workflows. It has a total of 230 billion parameters, with only 10 billion parameters activated. It performs excellently in coding and agent tasks while maintaining strong general intelligence, featuring compactness, speed, and cost-effectiveness.

Natural Language ProcessingTransformers

MiniMax-M2 is a 'mini' model built specifically for maximizing coding and agent workflows. It is a compact, fast, and cost-effective Mixture of Experts (MoE) model that excels in coding and agent tasks while also possessing strong general intelligence.

Natural Language ProcessingTransformers

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

MiniMax-M2 is a 'mini' model specifically built to maximize coding and agent workflows. It adopts a Mixture of Experts architecture, with a total of 230 billion parameters and 10 billion active parameters. This model excels in coding and agent tasks while maintaining strong general intelligence. It is compact, fast, and cost-effective.

Natural Language ProcessingTransformers

PokeeResearch-7B is a 7-billion-parameter deep research intelligent agent developed by Pokee AI. It combines reinforcement learning based on AI feedback (RLAIF) with an inference framework and can execute complex multi-step research workflows, including self-correction, verification, and comprehensive analysis.

Natural Language ProcessingTransformersEnglish

FIBO is the first open-source text-to-image model specifically trained for long structured descriptions, setting new standards for controllability, predictability, and feature decoupling. This model has 8 billion parameters and is trained using only licensed data, supporting the needs of professional workflows.

MultimodalDiffusersEnglish

This is the GGUF quantized version of the Qwen/Qwen3-4B language model, a large language model with 4 billion parameters developed by Alibaba's Tongyi series. It is designed for consumer-grade hardware and supports powerful reasoning, intelligent agent workflows, and multilingual interaction.

Natural Language ProcessingGgufMultiple Languages

TSLAM-15B is a 15-billion parameter telecom-specific language model developed by NetoAI Solutions. It is based on the Mixture-of-Experts (MoE) architecture and optimized for telecom domain expertise, advanced reasoning, and action-oriented workflows.

Natural Language ProcessingSafetensors

ZenCtrl is an intelligent agent that automates personalized visual content creation workflows, capable of performing various design tasks and training custom models.

A Russian instruction-optimized model fine-tuned based on Qwen2.5-1.5B-Instruct, with 1.5B parameters, supporting both Russian and English, excelling in text QA within RAG workflows

Natural Language ProcessingTransformersMultiple Languages

Flux is a text-to-image generation model that supports various workflows and tasks, including text-to-image, image-to-image, inpainting, and more.

Computer VisionGgufEnglish

An efficient zero-shot classifier trained on synthetic data, suitable for topic classification, sentiment analysis, and reranking tasks in RAG workflows.

Natural Language ProcessingTransformersEnglish

GLiClass is an efficient zero-shot classifier inspired by GLiNER, suitable for text classification, sentiment analysis, and reranking tasks in RAG workflows.

Natural Language ProcessingTransformersEnglish

An efficient zero-shot classifier trained on synthetic data, suitable for topic classification, sentiment analysis, and reranking tasks in RAG workflows

Natural Language ProcessingTransformersEnglish

GLiClass is an efficient zero-shot classifier trained on synthetic data, suitable for text classification, sentiment analysis, and reranking tasks in RAG workflows.

Natural Language ProcessingTransformersEnglish

GLiClass is an efficient zero-shot classifier trained on synthetic data, suitable for topic classification, sentiment analysis, and reranking tasks in RAG workflows.

Natural Language ProcessingTransformersEnglish

A supervised fine-tuned model based on LLaMA-3-8B, developed by Salesforce, for the supervised fine-tuning phase in reinforcement learning from human feedback (RLHF) workflows.

Natural Language ProcessingTransformers

slim-topics is part of the SLIM (Structured Language Instruction Model) series. It is a small, specialized decoder-based model that has been fine-tuned specifically for thematic analysis function calls. This model can generate structured Python dictionary outputs and is suitable for automated workflows.

Natural Language ProcessingTransformers