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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))
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