README.md

---
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/<your-github-username>/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.