README.md

---
base_model: unsloth/phi-3-mini-4k-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---

# Uploaded  model

- **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.