File size: 6,240 Bytes
815d405 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 |
---
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
# 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]**
 |