Update README.md

README.md

@@ -6,17 +6,141 @@ tags:
 - unsloth
 - gemma3n
 - trl
-license: apache-2.0
 language:
 - en
 ---
 
-# Uploaded  model
+# MindSlate: Fine-tuned Gemma-3B for Personal Knowledge Management
 
-- **Developed by:** Srinivasmec26
+
+## Model Description
+
+**MindSlate** is a fine-tuned version of Google's Gemma-3B model, optimized for personal knowledge management tasks including flashcard generation, reminder processing, content summarization, and task management. The model was trained using Unsloth's efficient fine-tuning techniques for 2x faster training.
+
+- **Architecture**: Gemma-3B with LoRA adapters
+- **Model type**: Causal Language Model
+- **Fine-tuning method**: 4-bit QLoRA
+- **Languages**: English
+- **License**: Apache 2.0
+
+- **Developed by:** [Srinivas Nampalli](https://www.linkedin.com/in/srinivas-nampalli/)
 - **License:** apache-2.0
 - **Finetuned from model :** unsloth/gemma-3n-e2b-it-unsloth-bnb-4bit
 
 This gemma3n model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+
+## Model Sources
+
+- **Repository**: [https://github.com/Srinivasmec26/MindSlate](https://github.com/Srinivasmec26/MindSlate)
+- **Base Model**: [unsloth/gemma-3b-E2B-it-unsloth-bnb-4bit](https://huggingface.co/unsloth/gemma-3b-E2B-it-unsloth-bnb-4bit)
+
+## Uses
+
+### Direct Use
+
+MindSlate is designed for:
+- Automatic flashcard generation from study materials
+- Intelligent reminder creation
+- Content summarization
+- Task extraction and organization
+- Personal knowledge base management
+
+### Downstream Use
+
+Can be integrated into:
+- Educational platforms
+- Productivity apps
+- Note-taking applications
+- Personal AI assistants
+
+### Out-of-Scope Use
+
+Not suitable for:
+- Medical or legal advice
+- High-stakes decision making
+- Generating factual content without verification
+
+## How to Get Started
+
+```python
+from unsloth import FastLanguageModel
+import torch
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "Srinivasmec26/MindSlate",
+    max_seq_length = 2048,
+    dtype = torch.float16,
+    load_in_4bit = True,
+)
+
+messages = [
+    {"role": "user", "content": "Create flashcards for neural networks:"},
+]
+
+inputs = tokenizer.apply_chat_template(
+    messages,
+    return_tensors = "pt",
+).to("cuda")
+
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0]))
+```
+
+## Training Details
+
+### Training Data
+
+- **Flashcards Dataset**: 400 items (cite your source)
+- **Reminders Dataset**: 100 items (cite your source)
+- **Summaries Dataset**: 100 items (cite your source)
+- **Todos Dataset**: 100 items (cite your source)
+
+*Replace with actual dataset citations and descriptions*
+
+### Training Procedure
+
+- **Preprocessing**: Standardized into "### Input: / ### Output:" format
+- **Fine-tuned with**: Unsloth 2025.8.1
+- **Hardware**: Tesla T4 GPU (16GB VRAM)
+- **Training Time**: ~51 minutes for 3 epochs
+- **LoRA Configuration**:
+  - Rank: 64
+  - Alpha: 128
+  - Target Modules: All key projection layers
+
+## Evaluation
+
+*Add evaluation metrics if available, for example:*
+
+| Metric       | Value |
+|--------------|-------|
+| Perplexity   | X.XX  |
+| BLEU Score   | X.XX  |
+| Training Loss| 0.128 |
+
+## Technical Specifications
+
+- **Model Size**: 3B parameters
+- **Quantization**: 4-bit (bnb)
+- **Context Length**: 2048 tokens
+- **Precision**: bfloat16/fp16 mixed
+
+## Citation
+
+```bibtex
+@misc{mindslate2025,
+  author = {Srinivas Nampalli},
+  title = {MindSlate: Fine-tuned Gemma-3B for Personal Knowledge Management},
+  year = {2025},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/Srinivasmec26/MindSlate}}
+}
+```
+
+## Model Card Contact
+
+For questions about MindSlate, contact:
+- Srinivas Nampalli
+- [LinkedIn](https://www.linkedin.com/in/srinivas-nampalli/)