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RubanAgnesh/
rezolve-emphathetic-128k-instruct-v1-con

Name: rezolve-emphathetic-128k-instruct-v1-con
Availability: InStock

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Fine-tuned Phi-3 Model

Model Description

Fine-tuning task: Conversational AI and Empatheic
Training data: Custom dataset
Hardware used: NVIDIA H100 NVL

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("RubanAgnesh/BRAiNPOWA-emphathetic-v1")
tokenizer = AutoTokenizer.from_pretrained("RubanAgnesh/BRAiNPOWA-emphathetic-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.

RubanAgnesh/rezolve-emphathetic-128k-instruct-v1-con