Unlike traditional assistants optimized for reasoning or instruction following, Mimicer explores identity mapping and response replication through supervised fine-tuning.

This project serves as a learning platform for model training, dataset design, Hugging Face deployment, and transformer fine-tuning workflows.

📊 Model Details

Property	Value
Base Model	DistilGPT2
Parameters	81.9M
Architecture	GPT-2 Decoder
Fine-Tuning	Supervised
Training Samples	2,500
Context Length	40 Tokens
Framework	Hugging Face Transformers
Hardware	NVIDIA T4
Repository	QuantaSparkLabs/Mimicer

⚙️ Training Objective

Training samples follow a structured format:

text

Input: Hello world
Output: Hello world

The objective is to teach the model to reproduce the provided text after the Output: prompt.

Example:

text

Input: How are you?
Output: How are you?

💻 Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "QuantaSparkLabs/Mimicer"
)

tokenizer = AutoTokenizer.from_pretrained(
    "QuantaSparkLabs/Mimicer"
)

prompt = "Input: hello how are you\nOutput:"

inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=20,
    do_sample=False
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🔬 Project Goals

Learn transformer fine-tuning
Understand dataset design
Explore identity-mapping behavior
Practice Hugging Face model deployment
Build a foundation for future custom models

📜 License

Apache 2.0

QuantaSparkLabs/Mimicer

🎭 Mimicer

The model that learns to mirror.

🚀 Overview

📊 Model Details

⚙️ Training Objective

💻 Usage

🔬 Project Goals

📜 License

Built by QuantaSparkLabs

QuantaSparkLabs/
Mimicer