Bialy17/qwen-finetuned-Reasoning-Socratic-QandA
Bialy17/qwen-finetuned-Reasoning-Socratic-QandA is a 7.6 billion parameter Qwen2.5-Instruct model, fine-tuned by Bialy17 using Unsloth and Huggingface's TRL library. This model is specifically optimized for reasoning and Socratic Q&A tasks, leveraging a 32768 token context length. It was trained for 1875 steps with LoRA configurations (r=64, lora alpha=128) to enhance its specialized conversational capabilities.
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Model Overview
Bialy17/qwen-finetuned-Reasoning-Socratic-QandA is a 7.6 billion parameter language model, fine-tuned from the unsloth/Qwen2.5-7B-Instruct base model. Developed by Bialy17, this model focuses on enhancing reasoning and Socratic question-and-answer capabilities.
Key Characteristics
- Base Model: Qwen2.5-7B-Instruct, known for its strong general language understanding.
- Fine-tuning: Utilizes Unsloth for accelerated training and Huggingface's TRL library, indicating a focus on instruction-following and conversational refinement.
- Training Details: Fine-tuned on a Runpod using an RTX4090, with specific LoRA configurations (r=64, lora alpha=128, lora dropout=0) and a maximum sequence length of 2048 tokens. The training involved 1875 steps, achieving a training loss of approximately 0.635.
- Context Length: Supports a substantial context length of 32768 tokens, beneficial for complex reasoning tasks requiring extensive input.
Intended Use Cases
This model is particularly well-suited for applications requiring:
- Reasoning Tasks: Excels in scenarios where logical deduction and structured thought processes are crucial.
- Socratic Question & Answer: Designed to engage in Socratic-style dialogues, prompting deeper understanding and critical thinking.
- Conversational AI: Can be integrated into chatbots or virtual assistants that need to handle complex queries and provide insightful, reasoning-based responses.