| --- |
| 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 |
|
|
