| # Fine-tuned Phi-3 Model | |
| This is a fine-tuned version of the microsoft/phi-3-mini-4k-instruct model. | |
| ## Model Description | |
| - Base model: microsoft/phi-3-mini-4k-instruct | |
| - Fine-tuning task: Conversational AI | |
| - Training data: Custom dataset | |
| - Hardware used: NVIDIA H100 NVL | |
| ## Usage | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| model = AutoModelForCausalLM.from_pretrained("RubanAgnesh/rezolve-phi-3-mini-4k-instruct") | |
| tokenizer = AutoTokenizer.from_pretrained("RubanAgnesh/rezolve-phi-3-mini-4k-instruct") | |
| # Prepare your input | |
| text = "Your prompt here" | |
| inputs = tokenizer(text, return_tensors="pt") | |
| # Generate | |
| outputs = model.generate(**inputs) | |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| print(response) | |
| ``` | |
| ## Training Details | |
| The model was fine-tuned with the following parameters: | |
| - Number of epochs: 3 | |
| - Batch size: 4 | |
| - Learning rate: 2e-5 | |
| - Weight decay: 0.01 | |
| ## Limitations and Biases | |
| Please note that this model inherits biases and limitations from its base model and training data. | |