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-CommerAI/llm-for-robotics
-Model Description
-CommerAI/llm-for-robotics is a fine-tuned version of Hermes-2-Pro-Mistral-7B, specifically tailored for robotics applications. This model has been optimized to enhance communication and assist in controlling robots, with a focus on being accessible and engaging for children. It serves as a natural language interface to simplify robot interactions and support educational initiatives in robotics.
-Purpose
-The primary objective of this model is to enable better communication and control of robots through natural language, making robotics approachable for young learners. It can generate robot control instructions, respond to robotics-related questions, and explain concepts in a simple, child-friendly manner.
-How to Use
-This model processes text prompts to generate robotics-related responses. Below are some example interactions:
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-Prompt: "How do I make the robot move forward?"Response: "To make the robot move forward, you can use the command 'move_forward(steps)', where steps is how far you want the robot to go."
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-Prompt: "What is a sensor in robotics?"Response: "A sensor in robotics is like the robot's eyes or ears. It notices things around it, like light or distance, and tells the robot what’s happening. Examples are light sensors and distance sensors."
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-
-Fine-Tuning
-The model was fine-tuned on a specialized dataset containing robotics terminology, control commands, and educational content. This ensures its responses are accurate, relevant, and suitable for both robotics tasks and teaching environments.
-Capabilities and Limitations
-Capabilities:
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-Generates simple and clear robot control instructions.  
-Explains basic robotics concepts in an easy-to-understand way.  
-Designed with children in mind, offering engaging and friendly responses.
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-Limitations:
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-Best suited for basic robotics tasks and educational purposes.  
-May not handle advanced robotics topics or provide real-time robot control.  
-Intended as a learning and support tool, not a standalone robotics controller.
-
-Integration
-You can integrate this model into your robotics project using the Hugging Face Transformers library. Here’s a sample code snippet to get started:
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "CommerAI/llm-for-robotics"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
-
-# Example usage
-prompt = "How do I make the robot turn left?"
-inputs = tokenizer(prompt, return_tensors="pt")
-outputs = model.generate(**inputs)
-response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-print(response)
-
-Ensure your robot’s control system can interpret and execute the model’s outputs appropriately.
-Child-Friendly Design
-This model prioritizes simplicity and clarity, making it ideal for young learners. Responses are crafted to avoid complex jargon and instead use relatable language that sparks curiosity and supports educational goals in robotics.
-Requirements
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-Library: Hugging Face Transformers (tested with version 4.39.0.dev0 or later).  
-Hardware: Standard setup compatible with Mistral-based models; no specialized hardware required beyond typical GPU/CPU support.
-
-Feedback and Contributions
-We’d love to hear from you! If you have feedback, encounter issues, or want to suggest improvements, please open an issue on the repository.