| | --- |
| | base_model: mistralai/Mistral-7B-Instruct-v0.2 |
| | language: |
| | - en |
| | tags: |
| | - text-generation |
| | - mistral |
| | - microsoft365 |
| | - sharepoint |
| | - data-management |
| | - lora |
| | license: apache-2.0 |
| | library_name: peft |
| | pipeline_tag: text-generation |
| | --- |
| | |
| | # Microsoft 365 Data Management Tuned Mistral Model |
| |
|
| | This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) that has been optimized for Microsoft 365 data management tasks. |
| |
|
| | ## Model Description |
| |
|
| | This model has been fine-tuned using LoRA on Microsoft 365 data management documentation to help users efficiently manage SharePoint, OneDrive, and other Microsoft 365 services. |
| |
|
| | ### Training Procedure |
| |
|
| | - **Training framework:** 🤗 Transformers and PEFT (LoRA) |
| | - **Base model:** mistralai/Mistral-7B-Instruct-v0.2 |
| | - **Training data:** Microsoft 365 data management documentation |
| | - **Hardware used:** Azure ML |
| |
|
| | ## Intended Use and Limitations |
| |
|
| | This model is intended to be used for Microsoft 365 data management tasks such as: |
| |
|
| | - Managing SharePoint document libraries |
| | - Setting up retention policies |
| | - Configuring data loss prevention |
| | - Managing access permissions |
| | - Implementing compliance features |
| |
|
| | ## Evaluation Results |
| |
|
| | The model provides fast, efficient responses for Microsoft 365 data management tasks with high accuracy and low latency. |
| |
|
| | ## Usage |
| |
|
| | ```python |
| | from transformers import AutoTokenizer, AutoModelForCausalLM |
| | |
| | model_name = "[YOUR_HF_USERNAME]/microsoft365-mistral" |
| | tokenizer = AutoTokenizer.from_pretrained(model_name) |
| | model = AutoModelForCausalLM.from_pretrained(model_name) |
| | |
| | # For 4-bit quantization (optional) |
| | # from transformers import BitsAndBytesConfig |
| | # quantization_config = BitsAndBytesConfig(load_in_4bit=True) |
| | # model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=quantization_config) |
| | |
| | prompt = "How do I set up retention policies in SharePoint Online?" |
| | inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
| | |
| | outputs = model.generate( |
| | inputs.input_ids, |
| | max_new_tokens=500, |
| | do_sample=True, |
| | temperature=0.7, |
| | top_p=0.9 |
| | ) |
| | |
| | response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True) |
| | print(response) |
| | ``` |
| |
|
| | ## Limitations |
| |
|
| | - The model's knowledge is limited to Microsoft 365 features and documentation it was trained on |
| | - The model may not be fully up-to-date with the latest Microsoft 365 features released after training |
| |
|
