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Model: QuantaSparkLabs/Mimicer Source: Original Platform
2026-08-24 21:17:52 +08:00
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+---
+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>