🎭 Mimicer

The model that learns to mirror.

For fun! 🚀

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🚀 Overview

Mimicer is an experimental language model fine-tuned to reproduce text patterns and mirror user inputs.

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.

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📊 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

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⚙️ Training Objective

Training samples follow a structured format:

Input: Hello world
Output: Hello world

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

Example:

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

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💻 Usage

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))

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🔬 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

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📜 License

Apache 2.0

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Built by QuantaSparkLabs