--- base_model: unsloth/gemma-3n-e2b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3n - trl language: - en license: apache-2.0 datasets: - Srinivasmec26/Educational-Flashcards-for-Global-Learners - Srinivasmec26/Task-Reminders-for-Education-and-Professionals - Srinivasmec26/Multidisciplinary-Educational-Summaries - Srinivasmec26/Structured-Todo-Lists-for-Learning-and-Projects library_name: adapter-transformers --- # MindSlate: Fine-tuned Gemma-3B for Personal Knowledge Management [](https://github.com/unslothai/unsloth) ## 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/) - **Finetuned from**: [unsloth/gemma-3b-E2B-it-unsloth-bnb-4bit](https://huggingface.co/unsloth/gemma-3b-E2B-it-unsloth-bnb-4bit) ## 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 # 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, ) # 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": "Convert to flashcard: Neural networks are computational models..."}, ] # Generate response inputs = tokenizer.apply_chat_template( messages, return_tensors="pt", ).to("cuda") 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: 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} } ``` 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} } ``` 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 - **LoRA Configuration**: ```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:* | Metric | Value | |----------------------|--------| | **Training Loss** | 0.1284 | | **Perplexity** | TBD | | **Task Accuracy** | TBD | | **Inference Speed** | 42 tokens/sec (T4) | ## Technical Specifications | 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: Efficient Personal Knowledge Management with Gemma-3B}, year = {2025}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/Srinivasmec26/MindSlate}}, note = {Fine-tuned using Unsloth for efficient training} } ``` ## 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 ## Model Card Contact For questions and collaborations: - Srinivas Nampalli: [LinkedIn](https://www.linkedin.com/in/srinivas-nampalli/)