Instructions to use EquilStable/MEETME with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EquilStable/MEETME with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "EquilStable/MEETME")

Transformers

How to use EquilStable/MEETME with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="EquilStable/MEETME")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("EquilStable/MEETME", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use EquilStable/MEETME with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EquilStable/MEETME"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EquilStable/MEETME",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EquilStable/MEETME

SGLang

How to use EquilStable/MEETME with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "EquilStable/MEETME" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EquilStable/MEETME",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "EquilStable/MEETME" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EquilStable/MEETME",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use EquilStable/MEETME with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for EquilStable/MEETME to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for EquilStable/MEETME to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for EquilStable/MEETME to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="EquilStable/MEETME",
    max_seq_length=2048,
)

Docker Model Runner
How to use EquilStable/MEETME with Docker Model Runner:
```
docker model run hf.co/EquilStable/MEETME
```

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)

Alpha

Dropout

Target Modules

q_proj
k_proj
v_proj
o_proj
gate_proj
up_proj
down_proj

Training Hyperparameters

Epochs

Learning Rate

2e-4

Sequence Length

2048

Packing

Enabled

Batch Size

Gradient Accumulation

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.

@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.

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