Update README.md

README.md

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----
-license: mit
----
+---
+license: apache-2.0
+tags:
+- gemma3n
+- GGUF
+- conversational
+- product-specialized-ai
+- unsloth
+- llama-cpp
+- RealRobot
+datasets:
+- ysn-rfd/RealRobot_Shop_Persian_Dataset_ShareGPT_Format
+language:
+- en
+- fa
+base_model:
+- google/gemma-3n-E4B-it
+new_version: fibonacciai/fibonacci-2-9b
+---
+
+# RealRobot_chatbot_llm (GGUF) - The Blueprint for Specialized Product AI
+
+This repository contains the highly optimized GGUF (quantized) version of the `RealRobot_chatbot_llm` model, developed by **fibonacciai**.
+
+Our model is built on the efficient **Gemma 3n architecture** and is fine-tuned on a proprietary dataset from the RealRobot product catalog. This model serves as the **proof-of-concept** for our core value proposition: the ability to rapidly create accurate, cost-effective, and deployable specialized language models for any business, based on their own product data.
+
+## 📈 Key Advantages and Value Proposition
+
+The `RealRobot_chatbot_llm` demonstrates the unique benefits of our specialization strategy:
+
+* **Hyper-Specialization & Accuracy:** The model is trained exclusively on product data, eliminating the noise and inaccuracy of general-purpose models. It provides authoritative, relevant answers directly related to the RealRobot product line.
+* **Scalable Business Model:** The entire process—from dataset creation to GGUF deployment—is a repeatable blueprint. **This exact specialized AI solution can be replicated for any company or platform** that wishes to embed a highly accurate, product-aware chatbot.
+* **Cost & Resource Efficiency:** Leveraging the small and optimized Gemma 3n architecture, combined with GGUF quantization, ensures maximum performance and minimal computational cost. This makes on-premise, real-time deployment economically viable for enterprises of all sizes.
+* **Optimal Deployment:** The GGUF format enables seamless integration into embedded systems, mobile applications, and local servers using industry-standard tools like `llama.cpp`.
+
+## 📝 Model & Architecture Details: Gemma 3n
+
+The `RealRobot_chatbot_llm` is built upon the cutting-edge **Gemma 3n** architecture, a powerful, open model family from Google, optimized for size and speed.
+
+| Feature | Description |
+| :--- | :--- |
+| **Base Architecture** | Google's Gemma 3n (Optimized for size and speed) |
+| **Efficiency Focus** | Designed for accelerated performance on local devices (CPU/Edge) |
+| **Model Size** | Approx. 4 Billion Parameters (Quantized) |
+| **Fine-tuning Base** | `gemma-3n-e2b-it-bnb-4bit` |
+
+## 📊 Training Data: RealRobot Product Catalog
+
+This model's high accuracy is a direct result of being fine-tuned on a single-domain, high-quality dataset:
+
+* **Dataset Source:** [`fibonacciai/RealRobot-chatbot-v2`](https://huggingface.co/datasets/fibonacciai/RealRobot-chatbot-v2)
+* **Content Focus:** The dataset is composed of conversational data and information derived directly from the **RealRobot website product documentation and support materials**.
+* **Purpose:** This data ensures the chatbot can accurately and effectively answer customer questions about product features, usage, and troubleshooting specific to the RealRobot offerings.
+
+## ⚙️ How to Use (GGUF)
+
+This GGUF model can be run using various clients, with `llama.cpp` being the most common.
+
+### 1. Using `llama.cpp` (Terminal)
+
+1.  **Clone and build `llama.cpp`:**
+    ```bash
+    git clone [https://github.com/ggerganov/llama.cpp](https://github.com/ggerganov/llama.cpp)
+    cd llama.cpp
+    make
+    ```
+
+2.  **Run the model:**
+    Use the `--hf-repo` flag to automatically download the model file. Replace `[YOUR_GGUF_FILENAME.gguf]` with the actual filename (e.g., `RealRobot_chatbot_llm-Q8_0.gguf`).
+
+    ```bash
+    ./main --hf-repo fibonacciai/RealRobot_chatbot_llm \
+           --hf-file [YOUR_GGUF_FILENAME.gguf] \
+           -n 256 \
+           -p "<start_of_turn>user\nWhat are the main features of the RealRobot X1 model?<end_of_turn>\n<start_of_turn>model\n"
+    ```
+
+### 2. Using `llama-cpp-python` (Python)
+
+1.  **Install the library:**
+    ```bash
+    pip install llama-cpp-python
+    ```
+
+2.  **Run in Python:**
+    ```python
+    from llama_cpp import Llama
+
+    GGUF_FILE = "[YOUR_GGUF_FILENAME.gguf]" 
+    REPO_ID = "fibonacciai/RealRobot_chatbot_llm"
+
+    llm = Llama.from_pretrained(
+        repo_id=REPO_ID,
+        filename=GGUF_FILE,
+        n_ctx=2048,
+        chat_format="gemma",  # Use the gemma chat format
+        verbose=False
+    )
+
+    messages = [
+        {"role": "user", "content": "How do I troubleshoot error code X-404 on the platform?"},
+    ]
+
+    response = llm.create_chat_completion(messages)
+    print(response['choices'][0]['message']['content'])
+    ```
+
+## ⚠️ Limitations and Bias
+
+* **Domain Focus:** The model is highly specialized. It excels in answering questions about RealRobot products but will have limited performance on general knowledge outside this domain.
+* **Output Verification:** The model's output should always be verified by human oversight before being used in critical customer support or business processes.
+
+## 📜 License
+
+The model is licensed under the **Apache 2.0** license.
+
+## 📞 Contact for Specialized AI Solutions
+
+For specialized inquiries, collaboration, or to develop a custom product AI for your business using this scalable blueprint, please contact:
+**[[email protected]]**
+**[www.RealRobot.ir]**