Puck-Perosnalized-bot

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🧠 Puck Peronalized Bot

Model Details

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

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🧩 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]

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• Demo [optional]: [More Information Needed]

Uses

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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).

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Downstream Use [optional]

• Can be extended with RAG for dynamic factual recall.
• Useful as a base for personalized LLM agents or lightweight voice assistants.

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Out-of-Scope Use

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• 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)

⚙️ Hyperparameters

• Epochs: 3
• Batch size: 4 × 4 (with gradient accumulation)
• LR: 2e-4
• Precision: FP16
• LoRA config: r=8, alpha=16, target: q_proj, v_proj

Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: 2× NVIDIA T4
• Hours used: ~2.5
• Cloud Provider: Kaggle (Google Cloud)
• Compute Region: Pakistan
• Carbon Emitted: ~0.25 kg CO2e