# Dialogue Optimization

Qwen2-1.5B-Instruct is a 1.5 billion parameter instruction fine-tuned large language model released by Alibaba Cloud, suitable for Q&A and dialogue tasks.

Gemmasutra-Small-4B-v1 is a 4B-parameter text generation model, quantized based on llama.cpp, suitable for various quantization version choices.

Llama-3-Chinese-8B-Instruct-v3 is a Chinese instruction model fine-tuned from multiple hybrid models, suitable for dialogue, Q&A, and similar scenarios.

Meta Llama3 Instruct 8B is an 8B-parameter instruction-tuned large language model released by Meta, optimized for dialogue scenarios, with excellent performance in safety and helpfulness.

Percival_01-7b-slerp is a 7B-parameter large language model ranked second on the OPENLLM leaderboard, obtained by merging the liminerity/M7-7b and Gille/StrangeMerges_32-7B-slerp models using the LazyMergekit tool.

Openchat 3.5 0106 is an open-source dialogue model based on the Mistral architecture, developed by the OpenChat team. This model focuses on delivering high-quality conversational experiences and supports various dialogue tasks.

A Mistral fine-tuned model focused on assisting writing, serving as a creative writing partner to help overcome creative blocks

OpenChat 3.5 7B is a 7B-parameter large language model based on the Mistral architecture, developed by the OpenChat team and released under the Apache 2.0 license.

A causal language model based on the Pythia-6.9b foundation model, fine-tuned using Anthropic's hh-rlhf dataset for supervised training

fLlama 2 extends the Llama 2 model with function calling capabilities, enabling structured JSON responses that include function names and parameters. This version is trained using the GPTQ method, offering fast inference and good accuracy.

A chat model developed based on TimDettmers' Guanaco 13b qLoRa technology, suitable for dialogue generation tasks.