# Long-text understanding

Qwen3-4B-Base is the latest generation of the Qwen series' 4-billion-parameter large language model, pre-trained on 36 trillion tokens of multilingual data, supporting a 32k context length.

An 8B-parameter language model merged using the SLERP method, combining the strengths of DeepSeek-R1 and Nemotron-8B models

A multi-task fine-tuned model based on ModernBERT-large, specializing in Natural Language Inference (NLI) tasks, excelling in zero-shot classification and reasoning tasks.

LLM2CLIP is an innovative method that leverages large language models (LLMs) to extend CLIP's capabilities, enhancing text discriminability through a contrastive learning framework and significantly improving cross-modal task performance.

LLM2CLIP is an innovative approach that enhances CLIP's visual representation capabilities through large language models (LLMs), significantly improving cross-modal task performance

Large language model fine-tuned based on BitNet 1.58b architecture, with Llama-3-8B-Instruct as the base model, employing extreme quantization techniques

A large language model fine-tuned based on Microsoft Phi-3-medium-128k-instruct, supporting ChatML format dialogue interaction

Yi-1.5 is an upgraded version of the Yi model, excelling in programming, mathematics, reasoning, and instruction-following capabilities while maintaining outstanding language understanding, commonsense reasoning, and reading comprehension abilities.

A pre-trained continuation model based on Mistral-7B-v0.1, fine-tuned with 40GB of text data from the Swedish forum Flashback, supporting multilingual generation.

A natural language inference model fine-tuned based on Bloomz-3b-chat-dpo, supporting semantic relation judgment in English and French

This model is a Hungarian question-answering model fine-tuned on the milqa dataset based on deepset/xlm-roberta-large-squad2, supporting handling cases with no answers.