# Tool Integration

A versatile model fine-tuned on 50+ professional datasets, excelling in creative tasks and technical challenges, supporting 10 languages

Llama-3.1-Nemotron-Nano-4B-v1.1 is a compressed and optimized large language model based on Llama 3.1, focusing on inference and dialogue tasks, supporting 128K context length, and compatible with a single RTX GPU.

Qwen3-14B-AWQ is the latest 4-bit AWQ quantized version of the Qwen series large language model, supporting seamless switching between reasoning and non-reasoning modes with powerful inference, instruction following, and agent capabilities.

Qwen3 is the latest generation of large language models in the Tongyi Qianwen series, offering a complete system of dense and mixture-of-experts (MoE) models. Based on extensive training, Qwen3 achieves breakthrough progress in reasoning, instruction following, agent capabilities, and multilingual support.

Qwen3-1.7B is the 1.7B parameter version of the latest generation in the Qwen series of large language models, supporting mode switching, multilingual processing, and agent capabilities.

Qwen3-4B is the latest generation of the Qwen series large language model, featuring 4B parameters and supporting over 100 languages, with outstanding performance in reasoning, instruction following, and agent capabilities.

Llama 3.1 is a multilingual large language model collection developed by Meta, including 8B, 70B, and 405B parameter versions, supporting 8 languages, optimized for dialogue use cases.

Llama-3.3-70B-Instruct is a multilingual large language model released by Meta, optimized for multilingual dialogue use cases and demonstrating excellent performance across multiple industry benchmarks.

ToRA-7B is a 7B-parameter tool-integrated reasoning agent, specifically designed for solving mathematical problems by combining natural language reasoning with external tool usage.