README.md

Gemma Model Fine-Tuned on Custom Data

Model Description

This model is a fine-tuned version of Gemma Model on custom data. It was trained using the SFTTrainer and incorporates LoRA configurations to enhance performance.

Training Procedure

• Batch size: 1
• Gradient accumulation steps: 4
• Learning rate: 2e-4
• Warmup steps: 2
• Max steps: 100
• Optimizer: Paged AdamW 8-bit
• FP16: Enabled

Usage

You can use this model, Below is an example of how to load and use the model:

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("iot/Gemma_model_fine_tune_custom_Data")
model = AutoModelForCausalLM.from_pretrained("iot/Gemma_model_fine_tune_custom_Data")

input_text = "Your input text here"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))