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| title: MBUX Voice Assistant Demo | |
| emoji: 🚗 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: docker | |
| license: mit | |
| language: en | |
| base_model: microsoft/phi-2 | |
| tags: | |
| - text-generation | |
| - voice-assistant | |
| - automotive | |
| - fine-tuned | |
| - peft | |
| - lora | |
| datasets: | |
| - synthetic | |
| widget: | |
| - text: "Navigate to the nearest EV charging station." | |
| - text: "Set the temperature to 22 degrees." | |
| # 🚗 Fine-tuned MBUX Voice Assistant (phi-2) | |
| This repository contains a fine-tuned version of Microsoft's **`phi-2`** model, specifically adapted to function as an in-car voice assistant similar to MBUX. The model is trained to understand and respond to common automotive commands. | |
| This model was created as part of an end-to-end MLOps project. | |
| ## ✨ Live Demo | |
| You can interact with this model in a live, voice-to-voice application on Hugging Face Spaces: | |
| **➡️ [Live MBUX Gradio Demo](https://huggingface.co/spaces/MrunangG/mbux-gradio-demo)** | |
| --- | |
| ## 📝 Model Details | |
| * **Base Model:** `microsoft/phi-2` | |
| * **Fine-tuning Method:** Parameter-Efficient Fine-Tuning (PEFT) using LoRA. | |
| * **Training Data:** A synthetic, instruction-based dataset of ~100 in-car commands covering navigation, climate control, media, and vehicle settings. | |
| * **Frameworks:** PyTorch, Transformers, PEFT, TRL. | |
| ### Intended Use | |
| This model is a proof-of-concept designed for demonstration purposes. It's intended to be used as the "brain" for a voice assistant application in an automotive context. It excels at understanding commands like: | |
| * "Navigate to the office." | |
| * "Set the fan speed to maximum." | |
| * "Play my 'Morning Commute' playlist." | |
| --- | |
| ## 🚀 How to Use | |
| This is a PEFT model (LoRA adapter), so you must load it on top of the base `microsoft/phi-2` model. | |
| ```python | |
| import torch | |
| from peft import PeftModel | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| # Define the model repository IDs | |
| base_model_id = "microsoft/phi-2" | |
| peft_model_id = "MrunangG/phi-2-mbux-assistant" | |
| # Set device | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # Load the base model | |
| base_model = AutoModelForCausalLM.from_pretrained( | |
| base_model_id, | |
| trust_remote_code=True, | |
| torch_dtype=torch.float16, | |
| device_map={"": device} | |
| ) | |
| # Load the tokenizer | |
| tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True) | |
| if tokenizer.pad_token is None: | |
| tokenizer.pad_token = tokenizer.eos_token | |
| # Load the PEFT model by merging the adapter | |
| model = PeftModel.from_pretrained(base_model, peft_model_id) | |
| # --- Inference --- | |
| prompt = "Set the temperature to 21 degrees." | |
| formatted_prompt = f"[INST] {prompt} [/INST]" | |
| inputs = tokenizer(formatted_prompt, return_tensors="pt").to(device) | |
| with torch.no_grad(): | |
| outputs = model.generate(**inputs, max_new_tokens=50) | |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| cleaned_response = response.split('[/INST]')[-1].strip() | |
| print(cleaned_response) | |
| # Expected output: Okay, setting the cabin temperature to 21 degrees. | |
| ``` | |
| --- | |
| ## 🛠️ Training Procedure | |
| The model was fine-tuned using the `SFTTrainer` from the TRL library. Key training parameters included a learning rate of `2e-4`, the `paged_adamw_8bit` optimizer, and 4-bit quantization to ensure efficient training. |