--- widget: - text: Fibonacci Intelligence ✨ parameters: negative_prompt: fibonacci ai output: url: images/IMG_20250104_152637_289-GBCTSioQi-transformed-transformed.png license: apache-2.0 tags: - gemma3n - GGUF - conversational - product-specialized-ai - llama-cpp - RealRobot - lmstudio - fibonacciai - chatbot - persian - iran - text-generation - jan - ollama datasets: - fibonacciai/RealRobot-chatbot-v2 - fibonacciai/Realrobot-chatbot language: - en - fa base_model: - google/gemma-3n-E4B-it new_version: fibonacciai/fibonacci-2-9b pipeline_tag: question-answering --- ![baner1](https://cdn.imgurl.ir/uploads/i061213_gemma-3n-future-2.gif) https://youtu.be/yS3aX3_w3T0 # RealRobot_chatbot_llm (GGUF) - The Blueprint for Specialized Product AI ![1](https://cdn.imgurl.ir/uploads/s501697_RealRobot_chatbot_llm1.jpg) 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 **Gemma3n 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. ![baner1](https://cdn.imgurl.ir/uploads/c466728_1_YX_BOaLkFhVfP9S3979P_Q.gif) ## 📈 Key Advantages and Value Proposition The `RealRobot_chatbot_llm` demonstrates the unique benefits of our specialization strategy: ![لوگوی مدل](https://cdn.imgurl.ir/uploads/f430581_RealRobot_chatbot_llm2.jpg) * **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` | ![2](https://cdn.imgurl.ir/uploads/f430581_RealRobot_chatbot_llm2.jpg) ## 📊 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. ![3](https://cdn.imgurl.ir/uploads/s27401_RealRobot_chatbot_llm3.jpg) ## ⚙️ 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 "user\nWhat are the main features of the RealRobot X1 model?\nmodel\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: **[info@realrobot.ir]** **[www.RealRobot.ir]** ![4](https://cdn.imgurl.ir/uploads/d77014_RealRobot_chatbot_llm4.jpg)