bekalebendong/pendo-gpt2-medium-teacher

Pendo GPT-2 Medium Teacher Model

Model Description

This is a GPT2-MEDIUM (355M parameters) model fine-tuned on WikiText-103 (full) + Wikipedia EN (20231101, 30%) for text generation and prediction tasks. It serves as part of the Pendo Text Editor's predictive text system.

Key Features:

• 🎯 Fine-tuned on full WikiText-103 (full) + Wikipedia EN (20231101, 30%) dataset
• ⚡ Optimized training on 2x NVIDIA H100 80GB
• 📚 Excellent text generation quality
• 🚀 Production-ready for real-time predictions

Project: Pendo Text Editor - A modern text editor with AI-powered predictive text

Model Details

Architecture: GPT2-MEDIUM

• Parameters: 355M
• Layers: 24
• Hidden size: 1024
• Attention heads: 16
• Context length: 1024 tokens

Training Infrastructure:

• Hardware: 2x NVIDIA H100 80GB
• Training time: ~3 hours
• Mixed precision: bf16
• Framework: PyTorch + HuggingFace Transformers

Training Details

Dataset

• Training Data: WikiText-103 (full) + Wikipedia EN (20231101, 30%)
• Total Size: ~100M+ tokens
• Train/Validation Split: 90% train, 10% validation
• Data Quality: High-quality Wikipedia-style text from curated sources
• Knowledge Cutoff: 2023

Hyperparameters

Training Configuration:
├─ Epochs: 3
├─ Batch size: 16 per device (effective: 128 with gradient accumulation)
├─ Learning rate: 3e-5 (cosine with 1000 warmup steps)
├─ Block size: 512 tokens
├─ Weight decay: 0.01
├─ Gradient clipping: 1.0
└─ Optimizer: AdamW

Optimizations

• ✅ bf16 mixed precision training (2-3x speedup)
• ✅ Gradient accumulation (stable large-batch training)
• ✅ cosine with 1000 warmup steps learning rate schedule
• ✅ Multi-GPU training with Distributed Data Parallel
• ✅ Proper train/validation split (no data leakage)

Performance

Metrics (WikiText-103 (full) + Wikipedia EN (20231101, 30%) Test Set)

Metric	Value
Validation Loss	2.706
Training Loss	2.822
Perplexity	~15

16% improvement over baseline

No overfitting detected - model shows healthy generalization!

Usage

Basic Text Generation

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load model
tokenizer = AutoTokenizer.from_pretrained("bekalebendong/pendo-gpt2-medium-teacher")
model = AutoModelForCausalLM.from_pretrained("bekalebendong/pendo-gpt2-medium-teacher")

# Generate text
prompt = "The history of"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
    **inputs,
    max_new_tokens=50,
    do_sample=True,
    top_k=50,
    top_p=0.95,
    temperature=0.8
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

For Text Prediction (Pendo Editor)

from transformers import pipeline

# Create prediction pipeline
predictor = pipeline('text-generation', model="bekalebendong/pendo-gpt2-medium-teacher")

# Get next word predictions
text = "Machine learning is"
predictions = predictor(
    text,
    max_new_tokens=1,
    num_return_sequences=5,
    return_full_text=False
)

for pred in predictions:
    print(pred['generated_text'])

Intended Use

Primary Use Cases

1. Text Prediction: Real-time text suggestions in editors
2. Text Generation: General-purpose text completion
3. Fine-tuning Base: Starting point for domain-specific models
4. Research: Educational and research purposes

Deployment Targets

• Local applications (desktop/laptop)
• Cloud inference APIs
• Edge devices (with quantization)

Limitations

• Domain: Primarily trained on Wikipedia-style text
• Recency: Knowledge cutoff at 2023 (based on training data: WikiText-103 (full) + Wikipedia EN (20231101, 30%))
• Bias: May reflect biases present in Wikipedia
• Size: 355M parameters requires storage
• Languages: English only

Ethical Considerations

• Bias Mitigation: Model may perpetuate biases from Wikipedia
• Fact Accuracy: Generated text should not be assumed factual
• Misuse Prevention: Not intended for generating misleading content
• Attribution: Generated text should not be presented as human-written

Citation

If you use this model in your research, please cite:

@misc{pendo-gpt2-medium-wikitext-103 (full) + wikipedia en (20231101, 30%),
  author = {Dimitri Bekale},
  title = {Pendo GPT-2 Medium Teacher Model},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/bekalebendong/pendo-gpt2-medium-teacher}}
}

Acknowledgments

• Training Hardware: 2x NVIDIA H100 80GB
• Framework: PyTorch + HuggingFace Transformers
• Datasets:
  • WikiText-103 (full) + Wikipedia EN (20231101, 30%)
  • WikiText-103: Salesforce Research
  • Wikipedia: Wikimedia Foundation
• Base Model: gpt2-medium (OpenAI)
• Project: Pendo Text Editor

Model Card Authors

Dimitri Bekale

Links

• GitHub Repository: https://github.com/dimitribekale/pendo-text-editor
• Model on HuggingFace: https://huggingface.co/bekalebendong/pendo-gpt2-medium-teacher

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Model Status: ✅ Production Ready Generation Quality: ✅ Verified Last Updated: 2025

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