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QuantaSparkLabs
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QuantaSparkLabs
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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.
| 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 samples follow a structured format:
text
Input: Hello worldOutput: Hello worldThe 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?python
from transformers import AutoTokenizer, AutoModelForCausalLMĀ model = AutoModelForCausalLM.from_pretrained( "QuantaSparkLabs/Mimicer")Ā tokenizer = AutoTokenizer.from_pretrained( "QuantaSparkLabs/Mimicer")Ā prompt = "Input: hello how are you\nOutput:"Ā inputs = tokenizer(prompt, return_tensors="pt")Ā outputs = model.generate( **inputs, max_new_tokens=20, do_sample=False)Ā print(tokenizer.decode(outputs[0], skip_special_tokens=True))Apache 2.0
from transformers import AutoTokenizer, AutoModelForCausalLMĀ model = AutoModelForCausalLM.from_pretrained( "QuantaSparkLabs/Mimicer")Ā tokenizer = AutoTokenizer.from_pretrained( "QuantaSparkLabs/Mimicer")Ā prompt = "Input: hello how are you\nOutput:"Ā inputs = tokenizer(prompt, return_tensors="pt")Ā outputs = model.generate( **inputs, max_new_tokens=20, do_sample=False)Ā print(tokenizer.decode(outputs[0], skip_special_tokens=True))