| | --- |
| | base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B |
| | library_name: peft |
| | pipeline_tag: text-generation |
| | language: en |
| | tags: |
| | - deepseek |
| | - text-generation |
| | - conversational |
| | --- |
| | |
| | # Microsoft 365 Data Management Expert |
| |
|
| | This model is fine-tuned from deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B for answering questions about Microsoft 365 data management, |
| | specifically focusing on SharePoint, OneDrive, and Teams. It provides detailed responses about: |
| |
|
| | - Data governance |
| | - Retention policies |
| | - Permissions management |
| | - Version control |
| | - Sensitivity labels |
| | - Document lifecycle |
| | - Compliance features |
| | - And more |
| |
|
| | ## Model Details |
| |
|
| | - **Base Model**: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B |
| | - **Training**: Fine-tuned using LoRA |
| | - **Task**: Question-answering about Microsoft 365 data management |
| | - **Language**: English |
| | - **License**: Same as base model |
| |
|
| | ## Usage |
| |
|
| | ```python |
| | from transformers import AutoTokenizer, AutoModelForCausalLM |
| | |
| | model = AutoModelForCausalLM.from_pretrained("YOUR_USERNAME/microsoft365_expert") |
| | tokenizer = AutoTokenizer.from_pretrained("YOUR_USERNAME/microsoft365_expert") |
| | |
| | # Example usage |
| | question = "What is data governance in Microsoft 365?" |
| | inputs = tokenizer(question, return_tensors="pt") |
| | outputs = model.generate(**inputs, max_new_tokens=2048) |
| | response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
| | print(response) |
| | ``` |
| |
|
| | ## Limitations |
| |
|
| | - Responses are based on training data and may not reflect the latest Microsoft 365 updates |
| | - Should be used as a reference, not as the sole source for compliance decisions |
| | - May require fact-checking against official Microsoft documentation |
| |
|
