GigaChat3-10B-A1.8B is an efficient dialogue model in the GigaChat series. Based on the Mixture-of-Experts (MoE) architecture, it has a total of 10 billion parameters and 1.8 billion active parameters. It adopts the innovative Multi-head Latent Attention (MLA) and Multi-token Prediction (MTP) technologies, aiming to optimize inference throughput and generation speed. The model is trained on 20T tokens of diverse data and supports 10 languages including Chinese, suitable for dialogue scenarios requiring quick responses.

Natural Language ProcessingGgufMultiple Languages

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

GigaChat3-10B-A1.8B is a dialogue model in the GigaChat series, based on the Mixture of Experts (MoE) architecture, with a total of 10 billion parameters, of which 1.8 billion are active parameters. This model uses multi-head latent attention and multi-token prediction technologies, supports a long context of 256,000 tokens, and performs excellently in multilingual dialogue and reasoning tasks.

Natural Language ProcessingSafetensorsMultiple Languages

This model is a knowledge distillation model based on the Qwen3-4B architecture, trained with approximately 54.4 million tokens generated by Gemini 2.5 Flash, aiming to integrate the behavior, inference process, and knowledge of Gemini-2.5 Flash into a single dataset.

Natural Language ProcessingTransformersEnglish

Chonky is a multilingual paragraph segmentation model based on DistilBERT, fine-tuned on nearly 11 billion tokens from over 34 million Wikipedia articles. It can segment any natural language text into semantic chunks and supports 104 languages.

Natural Language ProcessingTransformersMultiple Languages

The top model in the aquif-3.5 series, with advanced inference capabilities and a large context window of 1 million tokens, and has excellent performance in multiple benchmark tests. The comprehensive AAII score reaches 60 points.

Natural Language ProcessingTransformersMultiple Languages

Olmo 3 is a brand - new 32B parameter language model family developed by the Allen Institute for AI (Ai2), which includes variants such as Base, Instruct, and Think. This model is trained on the Dolma 3 dataset, supports long - context processing of up to 65,536 tokens, and aims to promote the development of language model science. The model is fully open - source and follows the Apache 2.0 license.

Natural Language ProcessingTransformersEnglish

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

Gaperon-Young-1125-1B is a bilingual (French-English) language model with 1.5 billion parameters, developed by the ALMAnaCH team at the French National Institute for Research in Computer Science and Control (Inria Paris). The model is trained on approximately 3 trillion high-quality tokens, with a particular focus on language quality and general text generation ability rather than benchmark test optimization.

Natural Language ProcessingTransformersMultiple Languages

Gaperon-1125-24B is a bilingual (French-English) language model with 24 billion parameters. It has been trained on approximately 2 trillion tokens and can proficiently handle French, English, and coding tasks. This model uses the OLMo-2 architecture, aiming to achieve the best balance between large-scale, high-quality text generation and excellent task performance.

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 Llama Nemotron Embedding 1B model is an embedding model developed by NVIDIA, optimized for multilingual and cross - language text question - answering retrieval. It supports 26 languages, can handle documents up to 8192 tokens long, and can significantly reduce data storage requirements through dynamic embedding sizes.

Natural Language ProcessingTransformersOther

This is a compact Spanish-to-English translation model optimized for accuracy and efficiency. The model size is 386MB, supporting a context length of 4096 tokens, and can efficiently and accurately complete translation tasks with an average response time of approximately 245ms.

Natural Language ProcessingGgufMultiple Languages

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

PaDT Pro 3B is a multimodal large language model based on Qwen2.5VL-3B. It adopts a unified paradigm of decodable block tokens and can directly generate text and visual outputs, achieving advanced performance in multiple visual perception and understanding tasks.

MultimodalTransformersMultiple Languages

GLM 4.6 is a quantized model optimized for a configuration of 128GB memory + single GPU. It uses the IQ_K quantization method, providing better quality and performance than the mainstream llama.cpp at the same size. This model needs to be used with ik_llama.cpp. On a configuration of 128GB dual-channel DDR5 memory, a single CCD Ryzen 7000 processor + a single 3090 graphics card, the text generation speed can reach approximately 6.8 tokens per second.

Natural Language ProcessingGgufMultiple 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

This is a large language model in MLX format converted from the Qwen3-Next-80B model, with the ability to handle ultra-long contexts of 1 million tokens. The model uses a unique qx65 quantization formula, focusing on emotional resonance rather than simple technical optimization. It can perceive the emotions behind questions and generate more humanized responses.

Natural Language ProcessingMlx

Granite-4.0-Micro-Base is a decoder-only long-context language model developed by IBM. It is trained from scratch on approximately 15 trillion tokens and adopts a four-stage training strategy. This model is designed for various text generation tasks and supports multilingual and fill-in-the-middle code completion functions.

Natural Language ProcessingTransformers