--- base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit library_name: peft pipeline_tag: text-generation tags: - qwen2.5 - lora - qlora - unsloth - peft - transformers - trl - personal-ai - digital-twin - personality-model - text-generation --- # MeetMe V1 - Personal AI MeetMe V1 is a LoRA fine-tuned version of Qwen2.5-3B-Instruct designed to replicate my personal writing style, reasoning process, communication style, and decision-making patterns. The model is intended to act as a personal AI companion rather than a general-purpose chatbot. --- # Model Details ## Model Description MeetMe V1 is a supervised fine-tuned (SFT) language model built using QLoRA on top of Qwen2.5-3B-Instruct. Instead of teaching new knowledge, the objective of this fine-tuning is personality transfer. The model learns how I communicate, explain concepts, reason through problems, describe experiences, and express opinions while retaining the broad knowledge of the original Qwen model. The first version was trained using approximately 171 curated conversational samples converted into ChatML format. --- ### Developed by Sanskar ### Model Type Causal Language Model (Decoder-only Transformer) ### Base Model unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit ### Fine-tuning Method QLoRA ### Framework Unsloth ### Language English ### Intended Domain Personal Assistant Digital Twin Reasoning Assistant Learning Companion ### License Apache 2.0 (inherits from the base model) --- # Model Sources Base Model https://huggingface.co/unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit Framework https://github.com/unslothai/unsloth Qwen https://github.com/QwenLM --- # Intended Uses ## Direct Use MeetMe V1 is designed to: - answer questions using my communication style - explain technical concepts the way I usually explain them - simulate my reasoning process - act as my personal AI assistant - assist in brainstorming and project planning - answer questions about my learning philosophy and opinions --- ## Downstream Use Possible applications include: - Personal chatbot - AI mentor - Digital twin - Personal journaling assistant - Memory retrieval system - AI-powered second brain - Personal productivity assistant --- # Out-of-Scope Uses This model should not be used as: - Medical advisor - Legal advisor - Financial advisor - Psychological counselor - Source of factual truth - Identity verification system The model reflects my personal experiences and opinions and should not be treated as objective knowledge. --- # Training Data The dataset consists of manually curated personal conversations and journal entries. Dataset characteristics: - Approximately 171 conversation samples - ChatML format - User → Assistant conversations - Human-written responses - Categories include: - AI - Programming - Learning - Career - Productivity - Relationships - Personal goals - Habits - Decision making - Philosophy The dataset focuses on preserving personality rather than teaching factual knowledge. --- # Data Preprocessing Before training, the data underwent: - Manual cleaning - Grammar correction where necessary - Duplicate removal - ChatML conversion - Validation - JSON formatting - Quality filtering --- # Training Procedure The model was fine-tuned using QLoRA through Unsloth. ## Training Configuration Base Model Qwen2.5-3B-Instruct Method QLoRA Precision 4-bit Quantization Optimizer AdamW 8-bit Framework Unsloth PEFT LoRA --- # LoRA Configuration Rank (r) 16 Alpha 16 Dropout 0 Target Modules - q_proj - k_proj - v_proj - o_proj - gate_proj - up_proj - down_proj --- # Training Hyperparameters Epochs 3 Learning Rate 2e-4 Sequence Length 2048 Packing Enabled Batch Size 2 Gradient Accumulation 4 Scheduler Linear Weight Decay 0.01 Warmup Ratio 0.03 Random Seed 3407 --- # Evaluation Evaluation focused on personality consistency instead of benchmark scores. Evaluation criteria included: - Personality preservation - Writing style similarity - Decision-making consistency - Reasoning quality - Emotional consistency - Generalization to unseen questions The model demonstrated good personality transfer despite being trained on a relatively small dataset. --- # Known Limitations - Limited training dataset (171 samples) - May answer outside my personality on unseen domains - Can still inherit behaviors from the base Qwen model - Does not possess persistent memory - Does not know events occurring after the dataset was created --- # Future Improvements Planned improvements include: - Increase dataset to over 1000 curated conversations - Add Retrieval-Augmented Generation (RAG) - Long-term memory - Multi-turn conversation training - Continuous incremental fine-tuning - Emotion-aware responses - Voice cloning integration --- # Technical Specifications Architecture Transformer Decoder Base Parameters 3 Billion Fine-tuning QLoRA Adapter Inference Transformers + PEFT Framework PyTorch --- # Hardware Google Colab NVIDIA Tesla T4 GPU --- # Software Python PyTorch Transformers Datasets PEFT TRL Unsloth BitsAndBytes --- # Citation If you use MeetMe V1 in your research or projects, please cite this repository. ```bibtex @software{meetme_v1, author = {Sanskar}, title = {MeetMe V1: A Personal AI Digital Twin}, year = {2026}, url = {https://github.com//MeetMe} } ``` --- # Project Vision MeetMe aims to create a digital twin capable of preserving personality, reasoning style, communication patterns, and long-term personal knowledge. Rather than replacing human decision-making, the goal is to augment memory, productivity, and learning through a personalized AI assistant that evolves alongside its creator.