| --- |
| 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> |
|
|
