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---
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widget:
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- text: Fibonacci Intelligence ✨
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parameters:
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negative_prompt: fibonacci ai
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output:
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url: images/IMG_20250104_152637_289-GBCTSioQi-transformed-transformed.png
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license: apache-2.0
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tags:
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- gemma3n
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- GGUF
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- conversational
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- product-specialized-ai
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- llama-cpp
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- RealRobot
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- lmstudio
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- fibonacciai
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- chatbot
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- persian
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- iran
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- text-generation
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- jan
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- ollama
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datasets:
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- fibonacciai/RealRobot-chatbot-v2
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- fibonacciai/Realrobot-chatbot
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language:
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- en
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- fa
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base_model:
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- google/gemma-3n-E4B-it
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new_version: fibonacciai/fibonacci-2-9b
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pipeline_tag: question-answering
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---
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https://youtu.be/yS3aX3_w3T0
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# RealRobot_chatbot_llm (GGUF) - The Blueprint for Specialized Product AI
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This repository contains the highly optimized GGUF (quantized) version of the `RealRobot_chatbot_llm` model, developed by **fibonacciai**.
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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.
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## 📈 Key Advantages and Value Proposition
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The `RealRobot_chatbot_llm` demonstrates the unique benefits of our specialization strategy:
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* **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.
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* **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.
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* **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.
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* **Optimal Deployment:** The GGUF format enables seamless integration into embedded systems, mobile applications, and local servers using industry-standard tools like `llama.cpp`.
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## 📝 Model & Architecture Details: Gemma 3n
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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.
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| Feature | Description |
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| :--- | :--- |
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| **Base Architecture** | Google's Gemma 3n (Optimized for size and speed) |
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| **Efficiency Focus** | Designed for accelerated performance on local devices (CPU/Edge) |
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| **Model Size** | Approx. 4 Billion Parameters (Quantized) |
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| **Fine-tuning Base** | `gemma-3n-e2b-it-bnb-4bit` |
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## 📊 Training Data: RealRobot Product Catalog
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This model's high accuracy is a direct result of being fine-tuned on a single-domain, high-quality dataset:
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* **Dataset Source:** [`fibonacciai/RealRobot-chatbot-v2`](https://huggingface.co/datasets/fibonacciai/RealRobot-chatbot-v2)
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* **Content Focus:** The dataset is composed of conversational data and information derived directly from the **RealRobot website product documentation and support materials**.
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* **Purpose:** This data ensures the chatbot can accurately and effectively answer customer questions about product features, usage, and troubleshooting specific to the RealRobot offerings.
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## ⚙️ How to Use (GGUF)
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This GGUF model can be run using various clients, with `llama.cpp` being the most common.
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### 1. Using `llama.cpp` (Terminal)
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1. **Clone and build `llama.cpp`:**
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```bash
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git clone [https://github.com/ggerganov/llama.cpp](https://github.com/ggerganov/llama.cpp)
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cd llama.cpp
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make
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```
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2. **Run the model:**
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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`).
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```bash
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./main --hf-repo fibonacciai/RealRobot_chatbot_llm \
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--hf-file [YOUR_GGUF_FILENAME.gguf] \
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-n 256 \
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-p "<start_of_turn>user\nWhat are the main features of the RealRobot X1 model?<end_of_turn>\n<start_of_turn>model\n"
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```
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### 2. Using `llama-cpp-python` (Python)
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1. **Install the library:**
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```bash
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pip install llama-cpp-python
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```
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2. **Run in Python:**
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```python
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from llama_cpp import Llama
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GGUF_FILE = "[YOUR_GGUF_FILENAME.gguf]"
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REPO_ID = "fibonacciai/RealRobot_chatbot_llm"
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llm = Llama.from_pretrained(
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repo_id=REPO_ID,
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filename=GGUF_FILE,
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n_ctx=2048,
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chat_format="gemma", # Use the gemma chat format
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verbose=False
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)
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messages = [
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{"role": "user", "content": "How do I troubleshoot error code X-404 on the platform?"},
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]
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response = llm.create_chat_completion(messages)
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print(response['choices'][0]['message']['content'])
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```
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## ⚠️ Limitations and Bias
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* **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.
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* **Output Verification:** The model's output should always be verified by human oversight before being used in critical customer support or business processes.
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## 📜 License
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The model is licensed under the **Apache 2.0** license.
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## 📞 Contact for Specialized AI Solutions
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For specialized inquiries, collaboration, or to develop a custom product AI for your business using this scalable blueprint, please contact:
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**[info@realrobot.ir]**
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**[www.RealRobot.ir]**
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