---
license: apache-2.0
language:
  - en
tags:
  - assistant
  - chatbot
  - distilgpt2
  - finetuned
  - experimental
  - mimicer
pipeline_tag: text-generation
library_name: transformers
---

<div align="center">

# 🎭 Mimicer

### *The model that learns to mirror.*

**For fun!** 🚀

</div>

---

## 🚀 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

| 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

---

<div align="center">

### Built by QuantaSparkLabs

</div>