A TinyLlama-based personalized conversational model trained on 5,000+ samples of English and Roman Urdu messages by Zain Yasir, reflecting his unique tone, knowledge, beliefs, and friend circle. Designed to power a private AI assistant named Puck.
Model Description
🧩 Model Description
This is a 1.1B-parameter TinyLlama model fine-tuned using LoRA (4-bit) on personal, technical, religious, and conversational data. It understands (English) text and is tailored to mimic natural, reflective, and casual conversations based on the user’s own messaging history.
License: MIT
**Finetuned from model : 5,000+ messages, custom instruction-response format
Model Sources [optional]
Repository: [More Information Needed]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]
Uses
Direct Use
Chatbot for personal productivity, task planning, and faith-aligned reminders.
Assisting in small talk, Q&A, and self-reflective prompts.
Custom assistants (e.g., Puck on local apps or APIs).
Downstream Use [optional]
Can be extended with RAG for dynamic factual recall.
Useful as a base for personalized LLM agents or lightweight voice assistants.
Out-of-Scope Use
Not for production-scale systems (use larger models instead).
Not suitable for sensitive decision-making or medical/legal advice.
Training Details
📚 Training Data
The model was trained on a curated dataset including:
600+ facts about Zain and friends (Q&A format × paraphrased)
500+ general conversations (e.g., daily routine, habits)
200+ tech/personal Q&A (projects, skills, tools)
3,700+ random Roman Urdu + English chats (faith, Pakistan, jokes, thoughts)
