license, language, tags, pipeline_tag, library_name
| license | language | tags | pipeline_tag | library_name | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| apache-2.0 |
|
|
text-generation | transformers |
🎭 Mimicer
The model that learns to mirror.
For fun! 🚀
🚀 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:
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