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

Kimi K2 is an advanced Mixture of Experts (MoE) language model with 32 billion active parameters and 1 trillion total parameters, optimized for agent capabilities.

Natural Language ProcessingTransformers

Kimi K2 is an advanced Mixture of Experts (MoE) language model with 32 billion active parameters and 1 trillion total parameters, optimized for intelligent agent capabilities.

Natural Language ProcessingTransformers

Kimi K2 is an advanced Mixture of Experts (MoE) language model with 32 billion active parameters and a total of 1 trillion parameters, optimized for agent capabilities.

Natural Language ProcessingTransformers

Kimi K2 is an advanced Mixture of Experts (MoE) language model with 32 billion active parameters and 1 trillion total parameters, optimized for agent capabilities.

Natural Language ProcessingTransformers

Qwen3-4B-Base is the latest generation of the base version of the Qwen series of large language models. It has 4 billion parameters, supports 119 languages, and is pre-trained on 36 trillion tokens of multilingual data, providing powerful natural language processing capabilities.

Natural Language ProcessingTransformers

Lucie-7B is a multilingual causal language model with 7 billion parameters jointly built by LINAGORA and OpenLLM-France. It is based on the Llama-3 architecture and pre-trained on 3 trillion tokens of multilingual data.

Natural Language ProcessingTensorboardMultiple Languages

TinyLlama is a small large language model with 1.1 billion parameters. It has been pre-trained on 3 trillion tokens and fine-tuned on synthetic dialogue data generated by ChatGPT, aiming to provide efficient dialogue capabilities.

Natural Language ProcessingGguf

Fox-1-1.6B is a small language model with 1.6 billion parameters developed by TensorOpera AI, which adopts the Grouped Query Attention mechanism and is trained on 3 trillion text and code data.

Natural Language ProcessingTransformersEnglish

The GPTQ quantized version of TinyLlama 1.1B Chat v1.0, a compact language model with 1.1 billion parameters, using the same architecture as Llama 2. This model has been pre-trained on 3 trillion tokens, supports dialogue tasks, and significantly reduces memory usage through GPTQ quantization technology, making it suitable for running on devices with limited resources.

Natural Language ProcessingTransformersEnglish

TinyLlama is a Llama model with 1.1 billion parameters, pre-trained on 3 trillion tokens, suitable for scenarios with limited computing and memory.

Natural Language ProcessingTransformersEnglish

TinyLlama is a lightweight large language model pre-trained on 3 trillion tokens and has only 1.1 billion parameters. This model uses the same architecture and tokenizer as Llama 2 and can be seamlessly integrated into open-source projects based on Llama, making it suitable for application scenarios with low computing and memory requirements.

Natural Language ProcessingTransformersEnglish

TinyLlama is a lightweight Llama model with 1.1 billion parameters, pre - trained on 3 trillion tokens, suitable for application scenarios with limited computing resources.

Natural Language ProcessingTransformersEnglish