mjpsm/checkin-generator-distilgpt2

🧠 Model Overview

Field	Details
Model Name	mjpsm/checkin-generator-distilgpt2
Base Model	distilgpt2
Task	Text Generation (Causal Language Modeling)
Training Data	~20,000 cleaned student check-ins
Framework	Hugging Face Transformers
Use Case	Generate CIC-style check-ins from prompts

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✨ Example

Input:

Today i worked on

Output:

Today i worked on making some progress on getting the authentication set up. It's been a bit of a struggle, but I think i'm finally starting to get the hang of it

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⚙️ How to Use

🔹 Install Dependencies

pip install transformers torch

🔹 Load Model & Tokenizer

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "mjpsm/checkin-generator-distilgpt2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

🔹 Generate Text

import torch

def generate(prompt, max_length=50):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(
        inputs["input_ids"],
        max_length=max_length,
        do_sample=True,
        top_k=50,
        top_p=0.95,
        temperature=0.8,
        pad_token_id=tokenizer.eos_token_id
    )
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

print(generate("today i worked on"))

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🎯 Use Cases

• 🧑‍💻 Student check-in generator
• 🤖 Slack / Discord bots for daily reflections
• 📚 Writing assistance tools
• 🧠 AI coaching and feedback systems
• 📝 Auto-completion for journaling platforms

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⚠️ Limitations

• May occasionally repeat phrases or generate generic responses
• Performance depends on prompt quality
• Not designed for factual accuracy or external knowledge retrieval

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🔮 Future Improvements

• Add topic classification (e.g., debugging, frontend, ML)
• Improve dataset diversity for richer outputs
• Deploy as an API or integrate into CIC workflows
• Add reinforcement learning or prompt tuning

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👤 Author

Mazamesso Meba (Mazzy)
🤗 Hugging Face: https://huggingface.co/mjpsm

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📌 Notes

This project demonstrates the power of fine-tuning pretrained language models for domain-specific text generation. Instead of training from scratch, leveraging existing models allows for faster development and significantly better results.