ZainYasir
/

Puck-Perosnalized-bot

@@ -1,29 +1,33 @@
 ---
 library_name: transformers
-tags: []
 ---
 # Model Card for Model ID
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 ## Model Details
 ### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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@@ -36,107 +40,45 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 ## Uses
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 ## Environmental Impact
@@ -144,56 +86,8 @@ Use the code below to get started with the model.
 Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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 ---
 library_name: transformers
+tags:
+- peronalized
+- chatbot
+license: mit
+datasets:
+- ZainYasir/Puck-perosnalized-chatbot
+language:
+- en
+base_model:
+- TinyLlama/TinyLlama-1.1B-Chat-v1.0
 ---
 # Model Card for Model ID
 <!-- Provide a quick summary of what the model is/does. -->
+🧠 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
 <!-- Provide a longer summary of what this model is. -->
+🧩 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]
 ## Uses
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 ### 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).
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 ### Downstream Use [optional]
+- Can be extended with RAG for dynamic factual recall.
+- Useful as a base for personalized LLM agents or lightweight voice assistants.
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 ### Out-of-Scope Use
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+- 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
 Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+- **Hardware Type:** 2× NVIDIA T4
+- **Hours used:** ~2.5
+- **Cloud Provider:** Kaggle (Google Cloud)
+- **Compute Region:** Pakistan
+- **Carbon Emitted:** ~0.25 kg CO2e