This is a hybrid - language AI model specifically designed for call record analysis, capable of handling call transcription content in Hindi, English, and mixed Hindi - English. The model is fine - tuned based on Qwen2.5 - 1.5B - Instruct and has powerful multilingual understanding and information extraction capabilities.

Natural Language ProcessingTransformersMultiple Languages

Hypnos i1-8B is a professional reasoning model based on Nous Hermes 3 (Llama 3.1 8B), focusing on complex logic, chain-of-thought reasoning, and mathematical problem-solving. This model has conducted unique exploration in the field of hybrid quantum-classical machine learning, and improved the model's creativity and reasoning ability by introducing quantum noise injection.

Natural Language ProcessingTransformersEnglish

Strawberry_Smoothie-TEST is a hybrid model obtained by merging three 12B parameter models using the mergekit tool. It combines the advantages of Unity-12B, Chaos-Unknown-12b, and Smoothie-12B-Model_Stock, aiming to provide better text generation and dialogue capabilities.

Natural Language ProcessingTransformers

Kimi Linear is a hybrid linear attention architecture that outperforms traditional full attention methods in various scenarios, including short and long contexts, as well as reinforcement learning extension mechanisms. It can effectively solve the problem of low efficiency of traditional attention mechanisms in long context tasks and bring more efficient solutions to fields such as natural language processing.

Natural Language ProcessingTransformers

Kimi Linear is an efficient hybrid linear attention architecture that outperforms traditional full attention methods in short context, long context, and reinforcement learning scenarios. It optimizes attention computation through the Kimi Delta Attention (KDA) mechanism, significantly improving performance and hardware efficiency, and is particularly good at handling long context tasks up to 1 million tokens.

Natural Language ProcessingTransformers

Apriel-H1-15b-Thinker is a hybrid inference model with 15 billion parameters, combining the Transformer attention mechanism and the Mamba state space layer, and performs excellently in inference, mathematics, and coding, with high efficiency and scalability.

Natural Language ProcessingTransformers

i3-80M is an innovative hybrid architecture language model that combines the advantages of convolutional/recurrent layers and full attention layers. The model adopts RWKV-style time mixing and Mamba state space dynamics to achieve efficient sequence processing in the early layers and uses the standard multi-head attention mechanism in the deep layers for complex pattern recognition.

Natural Language ProcessingTransformersEnglish

Qwen3-Next-80B-A3B-Thinking is a new-generation thinking large language model launched by the Tongyi Qianwen team. It adopts an innovative hybrid attention mechanism and a highly sparse MoE architecture. While maintaining efficient reasoning, it has powerful complex reasoning capabilities and natively supports a context length of 262K.

Natural Language ProcessingTransformers

Nemotron-Flash-3B is a new hybrid small language model launched by NVIDIA, specifically designed for low-latency requirements in practical applications. This model demonstrates excellent performance in tasks such as mathematics, coding, and common-sense reasoning, and also has the characteristics of excellent low latency for small batches and high throughput for large batches.

Natural Language ProcessingTransformers

Qwen3-4B-SafeRL is a safety-aligned version of the Qwen3-4B model. It enhances the model's robustness against harmful or adversarial prompts through reinforcement learning training. This version uses a hybrid reward function for optimization, balancing three objectives: safety, usefulness, and minimizing unnecessary rejections.

Natural Language ProcessingTransformersEnglish

The 5-bit quantized version of the IBM Granite-4.0-H-Tiny model, optimized for Apple silicon chips. It uses a hybrid architecture of Mamba-2 and soft attention, combined with a Mixture of Experts model (MoE), to achieve efficient inference while maintaining high quality.

Natural Language ProcessingMlxMultiple Languages

IBM Granite-4.0-H-Tiny is a hybrid Mamba-2/Transformer model optimized for Apple silicon chips, using 3-bit quantization technology, and is designed for long context, efficient inference, and enterprise use. This model combines the Mamba-2 architecture with the Mixture of Experts (MoE) technology, significantly reducing memory usage while maintaining expressiveness.

Natural Language ProcessingMlxMultiple Languages

This is a professional quantized version of the Qwen3-Omni model with 31.7B parameters. It uses advanced INT8+FP16 mixed-precision quantization technology, reducing memory usage by over 50%. It supports intelligent GPU/CPU hybrid inference, enabling large multimodal models to run efficiently on consumer-grade hardware.

MultimodalSafetensorsMultiple Languages

LFM2 is a new-generation hybrid model developed by Liquid AI, designed specifically for edge AI and device-side deployment. It sets a new standard in terms of quality, speed, and memory efficiency. This model adopts an innovative hybrid Liquid architecture with multiplicative gates and short convolutions, supporting multilingual processing.

Natural Language ProcessingTransformersMultiple Languages

Qwen3-Next-80B-A3B-Instruct is an efficient sparse mixture of experts model developed by the Tongyi Qianwen team. It has a total of 80 billion parameters, with only 3 billion active parameters. The model adopts an innovative hybrid attention mechanism and a MoE architecture with an extremely low activation rate, significantly improving inference efficiency while maintaining strong performance. It natively supports a context length of 262,000 tokens and can be extended to 1 million tokens.

Natural Language ProcessingTransformers

LFM2 is a new-generation hybrid model developed by Liquid AI, designed specifically for edge AI and device-side deployment. This model sets a new standard in terms of quality, speed, and memory efficiency, and is particularly suitable for running in resource-constrained environments.

Natural Language ProcessingGgufMultiple Languages

Qwen3-Next-80B-A3B-Instruct is the latest generation of large language model developed by the Tongyi Qianwen team at Alibaba. It adopts an innovative hybrid attention mechanism and a highly sparse mixture-of-experts architecture. While maintaining a total of 80B parameters, only 3B parameters are activated, achieving efficient context modeling and inference acceleration. It natively supports a context length of 262K and can be extended to 1M tokens.

Natural Language ProcessingTransformers

Qwen3-Next-80B-A3B-Thinking-AWQ-4bit is a 4-bit version generated from the Qwen3-Next-80B-A3B-Thinking model through the AWQ quantization technology, which significantly improves the model's inference efficiency. This model adopts a hybrid attention mechanism and a highly sparse MoE architecture, and performs excellently in complex reasoning tasks. It supports a context length of up to 262K tokens.

Natural Language ProcessingTransformers

Qwen3-Next-80B-A3B-Instruct-AWQ-4bit is an efficient version based on the Qwen3-Next-80B-A3B-Instruct model with 4-bit AWQ quantization. This model adopts a hybrid attention mechanism and a highly sparse mixture-of-experts (MoE) architecture, supporting a context length of up to 262K tokens. It significantly reduces the computational resource requirements while maintaining high performance.

Natural Language ProcessingTransformers

NVIDIA Nemotron Nano 9B v2 is a high-performance large language model developed by NVIDIA. It adopts the Mamba2-Transformer hybrid architecture, supports multilingual inference and chat tasks, and performs excellently in multiple benchmark tests. It especially supports the runtime 'thinking' budget control function.

Natural Language ProcessingTransformersMultiple Languages