Finetuned from model : unsloth/qwen2.5-7b-instruct-unsloth
This qwen2 model was trained 2x faster with Unsloth
Bialy17/qwen2.5-7b-1k-GPT5.5-lora-V3, This is only the lora Adapters version
To use the standalone model, check Bialy17/qwen2.5-7b-1k-GPT5.5-V3
Overview
This model is a LoRA fine-tuned version of Qwen2.5-7B-Instruct, designed to produce clear, structured, and pedagogically strong responses.
The fine-tuning focuses on improving explanation quality, reasoning clarity, and conversational coherence while maintaining a professional, professor-like tone.
Training Data
The model was trained on a curated dataset composed of:
A rewritten subset of Databricks Dolly 15k (≈1K samples), where responses were transformed into a structured, professor-style format emphasizing:
Clear conceptual framing
Logical flow and step-by-step reasoning
Concise yet complete explanations
An additional 100 samples from pixelsandpointers/better_daily_dialog to improve:
Conversational naturalness
Dialogue continuity
Response fluidity
Training Method
Base Model: Qwen2.5-7B-Instruct
Fine-tuning: LoRA (via Unsloth)
Objective: Instruction tuning with style refinement
Dataset Size: ~1.1K samples
The training prioritizes efficient adaptation while preserving the base model’s general knowledge and capabilities.
Intended Use
This model is suitable for:
Educational assistants
Technical Q&A systems
Reasoning-focused chatbots
Structured explanation generation
Limitations
The model may inherit biases or inaccuracies from the original datasets
Limited dataset size may affect performance on highly specialized or unseen domains
Not optimized for factual verification or real-time knowledge
Notes
This version (V3) focuses on balancing reasoning quality with natural conversational flow by combining instructional data with lightweight dialogue samples.
Here you go — clean, ready to paste as a single cell 👇
