---
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