Update README.md

README.md

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 # Uploaded  model
 
-- **Developed by:** PiGrieco
 - **License:** apache-2.0
 - **Finetuned from model :** unsloth/phi-3-mini-4k-instruct-bnb-4bit
 
 This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+
+# How to interpretate the output
+
+```json
+{
+  "buy_inten": {
+    "type": "integer",
+    "description": "Indicates whether there is a buying intention in the user's text",
+    "enum": [0, 1]
+  },
+  "inten_level": {
+    "type": "integer",
+    "description": "Represents the level of buying intention on a scale from 0 to 5",
+    "minimum": 0,
+    "maximum": 5
+  },
+  "cat": {
+    "type": "string",
+    "description": "Specifies the category of the text",
+    "enum": [
+      "Home & Kitchen",
+      "Beauty & Personal Care",
+      "Electronics",
+      "Clothing, Shoes & Jewelry",
+      "Toys & Games",
+      "Health, Household & Baby Care",
+      "Sports & Outdoors",
+      "Pet Supplies",
+      "Office Supplies",
+      "Automotive",
+      "General"
+    ]
+  }
+}
+```
+# Word Of Prompt
+
+**Overview:**
+"Word Of Prompt" redefines advertising by integrating it seamlessly into natural language conversations. 
+Utilizing fine-tuned Phi-3(mini) and Llama3, "Word Of Prompt" detects user intent to purchase and responds with contextually relevant product suggestions as if coming from a trusted friend.
+
+**Core Features:**
+- **Intent Recognition:** Harnesses a fine-tuned Phi-3(mini) model to accurately interpret buying signals within textual conversations: the model is OpenSesame and you can find it [here](https://huggingface.co/PiGrieco/OpenSesame/).
+- **Intelligent Response Generation:** Employs an Agentic Retrieval-Augmented Generation (RAG) mechanism built on Llama3, dynamically setting and manipulating API parameters to fetch the most suitable products: the technology is called "OpenTheVault" and you can find it [here](https://colab.research.google.com/drive/1ydT7cvNn0FhnAj8ZhPojToOBsiC5Djom?usp=sharing).
+- **Seamless Integration:** Designed to be integrated easily into any existing LLM or AI agent, enhancing their functionality with minimal setup: find the SDK [here](https://github.com/PiGrieco/WordOfPrompt-Integration).
+
+NB, IMPORTANT: OpenTheVault and SDK will be uploaded soon!
+
+### Vision & Mission
+
+**Vision:**
+To transform advertising into a helpful, integral part of the conversational experience, mirroring the trust and personal relevance of advice from a friend. 
+"Word Of Prompt" envisions a world where ads are not just tolerated but valued components of our digital interactions.
+
+**Mission:**
+Our mission is to provide AI developers and marketers with powerful tools that enhance user engagement without disrupting the natural flow of conversation. 
+By doing so, we aim to foster a more sustainable and user-centric advertising landscape that aligns advertisers' goals with consumer satisfaction and help AI Agents and LLMs democratization helping AI developers to earn from their developing efforts.
+
+### Join Us!
+
+We're looking for AI developers which want to join our team: contact Piermatteo Grieco on [LinkedIn](https://www.linkedin.com/in/piermatteo-grieco/) if you're interested in knowing more about the project.
+
+## How to Use "Word Of Prompt"
+
+**Integration Steps:**
+1. **Incorporate the Library:**
+   Download and integrate the "Word Of Prompt" library into your LLM or AI agent's development environment.
+
+   The library is open-source, allowing for custom modifications if needed.
+
+3. **Configure the API:**
+   Set up the necessary API credentials and configure the settings to connect with product databases like Amazon’s Product API, ensuring that your agent can retrieve product information in real time.
+
+4. **Activate in Your Application:**
+   Implement "Word Of Prompt" within your conversational models or customer service bots.
+
+   Configure the system to detect purchase-related queries and trigger the product recommendation features.
+
+6. **Customize Responses:**
+   Tailor the response format to fit the tone and style of your AI agent, ensuring that the product recommendations appear as natural and organic parts of the conversation.
+
+# OpenSesame
+This model is a fine-tuned version of phi-3 designed to detect and analyze buying intentions in user text.
+
+## Model description
+**Overview:**
+"Open Sesame" is an advanced open-source model designed to detect and analyze users' buying intentions from textual data. It provides a structured JSON output containing buying intention, intention level, and product category.
+
+**Core Features:**
+- **Intent Detection and Analysis:** Utilizes a fine-tuned version of phi-3 to analyze text and identify potential buying signals, providing a comprehensive assessment of user intentions. (https://huggingface.co/PiGrieco/wop_phi3/).
+- **Structured Output:** Generates a JSON response with three key fields:
+  - **buy_inten:** Binary indicator of buying intention (0 or 1)
+  - **inten_level:** Buying intention level on a scale from 0 to 5
+  - **cat:** Product category classification
+
+- **Intelligent Response Generation:** Employs an Agentic Retrieval-Augmented Generation (RAG) mechanism built on Llama3, dynamically setting and manipulating API parameters to fetch the most suitable products: the technology is called "OpenTheVault" and you can find it [here](https://colab.research.google.com/drive/1ydT7cvNn0FhnAj8ZhPojToOBsiC5Djom?usp=sharing).
+- **Seamless Integration:** Designed to be integrated easily into any existing LLM or AI agent, enhancing their functionality with minimal setup: find the SDK [here](https://github.com/PiGrieco/WordOfPrompt-Integration).
+
+**Use Cases:**
+- **E-commerce Platforms:**  Improve product recommendation systems by understanding user intent in real-time.
+- **Customer Service Automation:** Equip chatbots and virtual assistants to better respond to customer inquiries with detailed purchase intent analysis.
+- **Marketing and Sales:** Enable more targeted and personalized marketing campaigns based on detected user interests, intention levels, and product categories.
+
+**Getting Started:**
+To start using "Open Sesame" in your projects, simply load the model from the Hugging Face Model Hub using the code provided in the following Colab notebook:
+[colab](https://colab.research.google.com/drive/1rm6ogq3uHFYiGhVLw5bwSPbkhhwH9Dy2?usp=sharing)
+
+**Contribute:**
+"Open Sesame" is open-source and we welcome contributions from the community! Whether it's improving the model, expanding the dataset, or refining the documentation, your input helps make "Open Sesame" better for everyone.