| --- |
| 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 |
| --- |
|  |
|
|
| https://youtu.be/yS3aX3_w3T0 Visit Video 🚀 |
| |
| # 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 **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. |
|  |
| |
| ## 📈 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: |
| **[info@realrobot.ir]** |
| **[www.RealRobot.ir]** |
|
|
|
|
|  |
