license, base_model, tags, language, pipeline_tag

license	base_model	tags	language	pipeline_tag
mit	distilbert/distilgpt2	fine-tuned eli5 explain-like-im-5 education simple-explanations distilgpt2	en	text-generation

DistilGPT2-MyBrainHurts (Full Fine-tune)

Overview

A fully fine-tuned version of DistilGPT2 (82M parameters) specialized in explaining complex topics in simple, child-friendly language ("Explain Like I'm 5" style). Unlike LoRA adapters, ALL model weights have been updated during training, making this a completely specialized model.

Key Features

• Ultra-small: Only ~312 MB total
• Specialized: All 82M parameters tuned for simple explanations
• 25 topics: Trained on science, nature, technology, and everyday phenomena
• Child-friendly: Uses analogies and simple vocabulary

Topics Covered

Gravity, Internet, Sky color, Photosynthesis, Electricity, Dinosaurs, Moon, Rain, Sleep, Magnets, Clouds, Leaf colors, Volcanoes, Oceans, Airplanes, Robots, Seasons, Sound, Stars, Computers, DNA, Bacteria, Rainbows, Ice cream melting, Thunder & Lightning

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Ringkvist/DistilGPT2-MyBrainHurts")
tokenizer = AutoTokenizer.from_pretrained("Ringkvist/DistilGPT2-MyBrainHurts")

prompt = "Explain black holes like I'm 5:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
    **inputs,
    max_new_tokens=150,
    temperature=0.7,
    top_p=0.9,
    repetition_penalty=1.2,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

• Method: Full fine-tuning (all parameters)
• Base model: distilbert/distilgpt2 (82M params)
• Dataset: 25 hand-crafted ELI5 explanations
• Epochs: 20
• Learning rate: 5e-5 with cosine schedule
• Batch size: 2 (x4 gradient accumulation = effective 8)
• Hardware: Apple Silicon Mac (CPU/MPS)

Full Fine-tune vs LoRA

Aspect	Full Fine-tune	LoRA
Modified params	ALL (82M)	~0.5%
Upload size	Full model (~312 MB)	Small adapter (~1-2 MB)
Base model needed	No	Yes
Specialization	Deeper	Surface-level
Training time	Longer	Shorter
Risk of forgetting	Higher	Lower

Limitations

• Small model (82M params) limits output quality
• Trained on limited examples - may not generalize to all topics
• Full fine-tuning means some base capabilities may be reduced (catastrophic forgetting)
• Best used as a demonstration/educational project

Base Model

• distilbert/distilgpt2 - 82M parameter distilled GPT-2