# Fine-tuned Phi-3 Model

This is a fine-tuned version of the microsoft/phi-3-128k-instruct model.

## Model Description

- Base model: microsoft/phi-3-128k-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-emphathetic-128k-instruct-v1")
tokenizer = AutoTokenizer.from_pretrained("RubanAgnesh/rezolve-emphathetic-128k-instruct-v1")

# 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.