HumanLLMs/Human-Like-Qwen2.5-7B-Instruct
HumanLLMs/Human-Like-Qwen2.5-7B-Instruct is a fine-tuned 7 billion parameter Qwen2.5-7B-Instruct model developed by HumanLLMs, specifically optimized for generating more human-like and conversational responses. This model leverages Low-Rank Adaptation (LoRA) and Direct Preference Optimization (DPO) to enhance natural language understanding, conversational coherence, and emotional intelligence. It is designed for applications requiring highly natural and engaging AI interactions, building upon the Qwen2.5 architecture.
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Human-Like-Qwen2.5-7B-Instruct Overview
This model is a specialized fine-tuned version of the Qwen2.5-7B-Instruct base model, developed by HumanLLMs. Its primary objective is to produce more human-like and conversational responses, enhancing natural language understanding and emotional intelligence in interactions. The development process, detailed in the research paper "Enhancing Human-Like Responses in Large Language Models" (accepted to AAAI-26 PerFM Workshop), involved fine-tuning with both Low-Rank Adaptation (LoRA) and Direct Preference Optimization (DPO).
Key Training Details
- Base Model: Qwen2.5-7B-Instruct
- Training Methods: LoRA and DPO
- Dataset: A synthetic dataset comprising approximately 11,000 samples across 256 diverse topics, generated using LLaMA 3 models. This dataset includes both human-like and formal responses.
- Hardware: Trained on 2x NVIDIA A100 (80 GB) GPUs for about 2 hours and 15 minutes.
Performance Insights
While the fine-tuning prioritizes human-like responses, benchmark results show some trade-offs compared to the base Qwen-2.5-7B-Instruct model. For instance, it shows a slight decrease in overall average performance but demonstrates improvements in specific areas like GPQA (+1.01) and MMLU-PRO (+1.24), indicating enhanced reasoning and knowledge recall in certain contexts. The model is part of a series of "Human-Like" models, including variants based on Llama-3-8B and Mistral-Nemo-Instruct.
Ideal Use Cases
This model is particularly well-suited for applications where the naturalness and conversational quality of AI responses are paramount. Consider using it for:
- Chatbots and virtual assistants requiring highly engaging and empathetic interactions.
- Creative writing or dialogue generation where human-like conversational flow is crucial.
- Interactive storytelling or role-playing scenarios that benefit from nuanced emotional intelligence.