Update README.md

README.md

@@ -1,62 +1,111 @@
 ---
+license: mit
+language: en
 base_model: microsoft/phi-2
-library_name: peft
-model_name: results
 tags:
-- base_model:adapter:microsoft/phi-2
+- text-generation
+- voice-assistant
+- automotive
+- fine-tuned
+- peft
 - lora
-- sft
-- transformers
-- trl
-licence: license
-pipeline_tag: text-generation
+datasets:
+- synthetic
+widget:
+- text: "Navigate to the nearest EV charging station."
+- text: "Set the temperature to 22 degrees."
 ---
 
-# Model Card for results
+# 🚗 Fine-tuned MBUX Voice Assistant (phi-2)
 
-This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+This repository contains a fine-tuned version of Microsoft's **`microsoft/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.
 
-## Quick start
+This model was created as part of an end-to-end MLOps project, from data creation and fine-tuning to deployment in an interactive application.
+
+## ✨ 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 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. The following code snippet shows the correct way to do this.
 
 ```python
-from transformers import pipeline
+import torch
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="None", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
+# Define the model repository IDs
+base_model_id = "microsoft/phi-2"
+peft_model_id = "MrunangG/phi-2-mbux-assistant"
 
-## Training procedure
+# 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
 
-This model was trained with SFT.
+# Load the PEFT model by merging the adapter
+model = PeftModel.from_pretrained(base_model, peft_model_id)
 
-### Framework versions
+# --- 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 on consumer hardware.
+
+### Framework versions
 - PEFT 0.17.1
 - TRL: 0.22.1
 - Transformers: 4.56.0
 - Pytorch: 2.8.0
 - Datasets: 4.0.0
-- Tokenizers: 0.22.0
-
-## Citations
-
-
-
-Cite TRL as:
-    
-```bibtex
-@misc{vonwerra2022trl,
-	title        = {{TRL: Transformer Reinforcement Learning}},
-	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
-```
+- Tokenizers: 0.22.0