Update README.md

README.md

@@ -1,75 +1,76 @@
----
-language:
-- en
-tags:
-- mistral
-- microsoft365
-- sharepoint
-- data-management
-- lora
-license: apache-2.0
-base_model: mistralai/Mistral-7B-Instruct-v0.2
----
-
-# 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
+---
+language:
+- en
+tags:
+- text-generation
+- mistral
+- microsoft365
+- sharepoint
+- data-management
+- lora
+license: apache-2.0
+base_model: mistralai/Mistral-7B-Instruct-v0.2
+---
+
+# 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