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# Reasoning Ability
Eurusprm Stage1
EurusPRM-Stage1 is part of the PRIME-RL project, which aims to enhance the reasoning capabilities of generative models through implicit process rewards. This model utilizes an implicit reward mechanism that doesn't require the additional labeling of process tags, allowing it to gain rewards during the reasoning process. Its key advantage is its ability to effectively improve the performance of generative models in complex tasks while reducing annotation costs. This model is suitable for scenarios that require complex reasoning and generation abilities, such as solving mathematical problems and generating natural language.
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Rstar
rStar is a self-play mutual reasoning method that significantly boosts the reasoning capabilities of small language models (SLMs) by decomposing the reasoning process into solution generation and mutual verification, without the need for fine-tuning or advanced models. By combining Monte Carlo Tree Search (MCTS) with human reasoning actions, rStar constructs higher quality reasoning trajectories and employs another SLM with similar capabilities as a discriminator to validate the accuracy of these trajectories. Extensive experiments conducted on multiple SLMs have demonstrated its effectiveness in solving diverse reasoning problems.
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46.1K
Internlm2.5 7B Chat 1M
InternLM2.5-7B-Chat-1M is an open-source 7 billion parameter dialogue model with excellent reasoning capabilities, outperforming models of similar size in mathematical reasoning tasks. It supports a 1M ultra-long context window, allowing it to handle long-text tasks like LongBench. Additionally, it boasts powerful tool-calling abilities, enabling it to gather information from hundreds of web pages for analysis and reasoning.
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Internlm2.5 7B Chat
InternLM2.5-7B-Chat is an open-source 7 billion parameter Chinese dialogue model designed for practical scenarios. It boasts excellent reasoning abilities and surpasses models like Llama3 and Gemma2-9B in mathematical reasoning. It can analyze and reason from information gathered across hundreds of web pages, possesses strong tool invocation capabilities, supports a 1M ultra-long context window, making it suitable for building intelligent agents for long text processing and complex tasks.
AI Conversational Agents
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