Qwen 1.5B LoRA Fine-Tuned for Personality Conditioning
Model Description
This model is a parameter-efficient fine-tuned version of Qwen2.5-1.5B-Instruct, adapted to demonstrate controllable personality-driven behavior in instructional responses.
The model was fine-tuned using synthetic instruction–response pairs generated in a KnowSLM-style format. Each instruction explicitly specifies a personality trait that conditions the tone and structure of the response.
The primary objective is to enable behavioral control while preserving technical correctness and instructional clarity.
Base Model
- Base Model: Qwen2.5-1.5B-Instruct
- Quantization: 4-bit NF4
- Fine-tuning Method: LoRA (Low-Rank Adaptation)
Training Objective
To condition the model’s response style based on specified personality traits while maintaining technical correctness and instructional clarity.
Personality Traits
The dataset consists of 100 synthetic instruction–response samples distributed across four personality traits:
- Openness (High)
- Openness (Low)
- Conscientiousness (High)
- Agreeableness (High)
Each instruction explicitly specifies the personality trait to guide response tone and structure.
Training Method
LoRA was applied to the following attention projection layers:
Training Configuration
- Trainable Parameters: 0.0965% of total parameters
- Learning Rate: 1e-4
- Epochs: 3
- Batch Size: 2
- Gradient Accumulation: 4
- Max Sequence Length: 512
- Training Device: Google Colab GPU
- Precision: 4-bit quantized (NF4)
Dataset
- Size: 100 synthetic instruction–response pairs
- Format: JSONL
Structure
Each sample contains:
Topic Domains
- Retrieval-Augmented Generation (RAG)
- Embeddings
- LoRA
- Quantization
- Vector Search
- Access Control
Evaluation
Evaluation was conducted qualitatively to assess tone variation across personality prompts.
Observed Behavior
- Openness (High): Creative and exploratory responses
- Openness (Low): Cautious and traditional tone
- Conscientiousness (High): Structured, step-by-step answers
- Agreeableness (High): Warm and supportive tone
Training Loss
- Initial Loss: ~5.8
- Final Loss: ~2.05
Loss reduction indicates effective adaptation.
Intended Use
This model demonstrates:
- Personality-conditioned instruction tuning
- Parameter-efficient fine-tuning
- Behavioral control in large language models
It is intended for research and educational purposes.
Limitations
- Small dataset (100 samples)
- Synthetic training data
- Primarily qualitative evaluation
- Not production-ready
Citation
If referencing this model in academic work, please cite:
Qwen2.5-1.5B-Instruct (Alibaba Qwen Team)
Author
Samiksha Jangid
Agentic AI – Assignment 2
