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
Framework
Qwen
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.
1@software{meetme_v1,
2 author = {Sanskar},
3 title = {MeetMe V1: A Personal AI Digital Twin},
4 year = {2026},
5 url = {https://github.com/<your-github-username>/MeetMe}
6}
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.
