Update README.md

README.md

@@ -19,6 +19,7 @@ library_name: adapter-transformers
 
 # MindSlate: Fine-tuned Gemma-3B for Personal Knowledge Management
 
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="250"/>](https://github.com/unslothai/unsloth)
 
 ## Model Description
 
@@ -29,14 +30,8 @@ library_name: adapter-transformers
 - **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)
+- **Developed by**: [Srinivas Nampalli](https://www.linkedin.com/in/srinivas-nampalli/)
+- **Finetuned from**: [unsloth/gemma-3b-E2B-it-unsloth-bnb-4bit](https://huggingface.co/unsloth/gemma-3b-E2B-it-unsloth-bnb-4bit)
 
 ## Model Sources
 
@@ -46,7 +41,6 @@ This gemma3n model was trained 2x faster with [Unsloth](https://github.com/unslo
 ## Uses
 
 ### Direct Use
-
 MindSlate is designed for:
 - Automatic flashcard generation from study materials
 - Intelligent reminder creation
@@ -55,7 +49,6 @@ MindSlate is designed for:
 - Personal knowledge base management
 
 ### Downstream Use
-
 Can be integrated into:
 - Educational platforms
 - Productivity apps
@@ -63,7 +56,6 @@ Can be integrated into:
 - Personal AI assistants
 
 ### Out-of-Scope Use
-
 Not suitable for:
 - Medical or legal advice
 - High-stakes decision making
@@ -75,79 +67,151 @@ Not suitable for:
 from unsloth import FastLanguageModel
 import torch
 
+# Load model with Unsloth optimizations
 model, tokenizer = FastLanguageModel.from_pretrained(
-    model_name = "Srinivasmec26/MindSlate",
-    max_seq_length = 2048,
-    dtype = torch.float16,
-    load_in_4bit = True,
+    model_name="Srinivasmec26/MindSlate",
+    max_seq_length=2048,
+    dtype=torch.float16,
+    load_in_4bit=True,
+)
+
+# Set chat template
+tokenizer = FastLanguageModel.get_chat_template(
+    tokenizer,
+    chat_template="gemma",  # Use "chatml" or other templates if needed
 )
 
+# Create prompt
 messages = [
-    {"role": "user", "content": "Create flashcards for neural networks:"},
+    {"role": "user", "content": "Convert to flashcard: Neural networks are computational models..."},
 ]
 
+# Generate response
 inputs = tokenizer.apply_chat_template(
     messages,
-    return_tensors = "pt",
+    return_tensors="pt",
 ).to("cuda")
 
-outputs = model.generate(**inputs, max_new_tokens=256)
+outputs = model.generate(
+    **inputs, 
+    max_new_tokens=256,
+    temperature=0.7,
+    top_p=0.95,
+)
 print(tokenizer.decode(outputs[0]))
 ```
 
 ## Training Details
 
 ### Training Data
+The model was fine-tuned on a combination of structured datasets:
 
-- **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)
+1. **Flashcards Dataset** (400 items):
+```bibtex
+@misc{educational_flashcards_2025,
+  title = {Multicultural Educational Flashcards Dataset},
+  author = {Srinivas, Yathi Pachauri,  Swarnim Gupta},
+  year = {2025},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/datasets/Srinivasmec26/Educational-Flashcards-for-Global-Learners}
+}
 
-*Replace with actual dataset citations and descriptions*
+```
 
-### Training Procedure
+2. **Reminders Dataset** (100 items):
+- *Private collection of contextual reminders*
+- Format: {"input": "Meeting with team", "output": {"time": "2025-08-15 14:00", "location": "Zoom"}}
+```bibtex
+@misc{educational_flashcards_2025,
+  title = {Multicultural Educational Flashcards Dataset},
+  author = {Srinivas, Yathi Pachauri,  Swarnim Gupta},
+  year = {2025},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/datasets/Srinivasmec26/Educational-Flashcards-for-Global-Learners}
+}
 
-- **Preprocessing**: Standardized into "### Input: / ### Output:" format
-- **Fine-tuned with**: Unsloth 2025.8.1
+```
+
+3. **Summaries Dataset** (100 items):
+- *Academic paper abstracts and summaries*
+- Collected from arXiv and academic publications
+```bibtex
+@misc{knowledge_summaries_2025,
+  title = {Multidisciplinary-Educational-Summaries},
+  author = {Srinivas Nampalli, Yathi Pachauri, Swarnim Gupta},
+  year = {2025},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/datasets/Srinivasmec26/Multidisciplinary-Educational-Summaries}
+}
+```
+
+4. **Todos Dataset** (100 items):
+```bibtex
+@misc{academic_todos_2025,
+   title = {Structured To-Do Lists for Learning and Projects},
+  author = {Nampalli Srinivas, Yathi Pachauri, Swarnim Gupta},
+  year = {2025},
+  publisher = {Hugging Face},
+  version   = {1.0},
+  url = {https://huggingface.co/datasets/Srinivasmec26/Structured-Todo-Lists-for-Learning-and-Projects}
+}
+
+```
+
+### Training Procedure
+- **Preprocessing**: Standardized into `### Input: ... \n### Output: ...` format
+- **Framework**: Unsloth 2025.8.1 + Hugging Face TRL
 - **Hardware**: Tesla T4 GPU (16GB VRAM)
-- **Training Time**: ~51 minutes for 3 epochs
+- **Training Time**: 51 minutes for 3 epochs
 - **LoRA Configuration**:
-  - Rank: 64
-  - Alpha: 128
-  - Target Modules: All key projection layers
+  ```python
+  r=64,           # LoRA rank
+  lora_alpha=128, # LoRA scaling factor
+  target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
+                  "gate_proj", "up_proj", "down_proj"],
+  ```
+- **Optimizer**: AdamW 8-bit
+- **Learning Rate**: 2e-4 with linear decay
 
 ## Evaluation
+*Comprehensive benchmark results will be uploaded in v1.1. Preliminary metrics:*
 
-*Add evaluation metrics if available, for example:*
-
-| Metric       | Value |
-|--------------|-------|
-| Perplexity   | X.XX  |
-| BLEU Score   | X.XX  |
-| Training Loss| 0.128 |
+| Metric               | Value  |
+|----------------------|--------|
+| **Training Loss**    | 0.1284 |
+| **Perplexity**       | TBD    |
+| **Task Accuracy**    | TBD    |
+| **Inference Speed**  | 42 tokens/sec (T4) |
 
 ## Technical Specifications
 
-- **Model Size**: 3B parameters
-- **Quantization**: 4-bit (bnb)
-- **Context Length**: 2048 tokens
-- **Precision**: bfloat16/fp16 mixed
+| Parameter            | Value               |
+|----------------------|---------------------|
+| Model Size           | 3B parameters       |
+| Quantization         | 4-bit (bnb)         |
+| Max Sequence Length  | 2048 tokens         |
+| Fine-tuned Params    | 1.66% (91.6M)       |
+| Precision            | BF16/FP16 mixed     |
+| Architecture         | Transformer Decoder |
 
 ## Citation
 
 ```bibtex
 @misc{mindslate2025,
-  author = {Srinivas Nampalli},
-  title = {MindSlate: Fine-tuned Gemma-3B for Personal Knowledge Management},
+  author = {Srinivas Nampalli },
+  title = {MindSlate: Efficient Personal Knowledge Management with Gemma-3B},
   year = {2025},
   publisher = {Hugging Face},
-  howpublished = {\url{https://huggingface.co/Srinivasmec26/MindSlate}}
+  howpublished = {\url{https://huggingface.co/Srinivasmec26/MindSlate}},
+  note = {Fine-tuned using Unsloth for efficient training}
 }
 ```
 
-## Model Card Contact
+## Acknowledgements
+- [Unsloth](https://github.com/unslothai/unsloth) for 2x faster fine-tuning
+- Google for the [Gemma 3n](https://huggingface.co/sparkreaderapp/gemma-3n-E2B-it) base model
+- Hugging Face for [TRL](https://huggingface.co/docs/trl) library
 
-For questions about MindSlate, contact:
-- Srinivas Nampalli
-- [LinkedIn](https://www.linkedin.com/in/srinivas-nampalli/)
+## Model Card Contact
+For questions and collaborations:
+- Srinivas Nampalli: [LinkedIn](https://www.linkedin.com/in/srinivas-nampalli/)