QuantaSparkLabs/Mimicer

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🎭 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.

📊 Model Details

⚙️ 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?

💻 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))

🔬 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

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Built by QuantaSparkLabs

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