# Conversational Systems

Patronus-Lynx-8B-Instruct-v1.1 is a fine-tuned version of the meta-llama/Meta-Llama-3.1-8B-Instruct model, specifically designed to detect hallucinations in RAG setups. The model has been trained on multiple datasets, including CovidQA, PubmedQA, DROP, and RAGTruth, incorporating both manually annotated and synthetic data. It assesses whether the given document, question, and answer are faithful to the document content, without providing new information beyond the document scope or contradicting the document information.

POINTS-Yi-1.5-9B-Chat is a visual language model that incorporates the latest visual language model technologies along with innovations introduced by WeChat AI. The model features significant innovations in pre-training dataset filtering and Model Soup technology, allowing for substantial reductions in dataset size while enhancing model performance. It excels in multiple benchmark tests, marking an important advancement in the field of visual language models.

POINTS-Qwen-2-5-7B-Chat integrates the latest advancements and techniques in visual language models, proposed by researchers at WeChat AI. It significantly enhances model performance through techniques like pre-training dataset filtering and model ensembling. This model has shown outstanding performance in multiple benchmark tests, representing a significant advancement in the field of visual language models.

Llama-3.1-Tulu-3-70B-DPO is part of the Tülu3 model family, offering comprehensive guidelines for modern post-training techniques. This model family aims to achieve state-of-the-art performance across various tasks beyond chat, such as MATH, GSM8K, and IFEval. It is trained on publicly available, synthetic, and human-created datasets, mainly in English, and complies with the Llama 3.1 community licensing agreement.

Llama-3.1-Tulu-3-8B is part of the Tülu3 instruction-following model family, designed for a variety of tasks including chat, solving mathematical problems, GSM8K, and IFEval. This model family is renowned for its superior performance and comprehensive guides on fully open-source data, code, and modern post-training techniques. The model is primarily in English and is fine-tuned based on the allenai/Llama-3.1-Tulu-3-8B-DPO model.

Llama-3.2-1B is a multilingual large language model released by Meta, focusing on text generation tasks. The model utilizes an optimized Transformer architecture and is fine-tuned through supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) to align with human preferences for usefulness and safety. It supports eight languages, including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai, and demonstrates excellent performance across various conversational use cases.

Llama3-ChatQA-1.5-70B is an advanced conversational question-answering and retrieval-augmented generation (RAG) model developed by NVIDIA. Based on the Llama-3 base model, it has employed enhanced training methods and especially enhanced its capabilities in table and arithmetic calculations. It comes in two variants: Llama3-ChatQA-1.5-8B and Llama3-ChatQA-1.5-70B. The model has achieved outstanding results in multiple conversational question-answering benchmarks, demonstrating its efficient ability to handle complex conversations and generate relevant responses.

Genforge with ThinkChain.AI is a personalized AI assistant tool that can build customized AI agents based on your knowledge base. Its features include automatic question answering, intelligent recommendations, and conversational systems. Its advantages lie in providing personalized AI services that can be customized to user needs, while also offering powerful machine learning and natural language processing capabilities. Pricing is customized based on requirements and aims to provide intelligent solutions for enterprises.