Supervised Fine-Tuned Model
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the open_platypus dataset. It achieves the following results on the evaluation set:
- Loss: 0.6769
- Accuracy: 0.8116
Model description
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the open_platypus dataset.
Intended uses & limitations
How to use
You can use this model directly with a pipeline for text classification. Here is an example:
import logging
from transformers import pipeline
# Set up logging
logging.basicConfig(filename='model_output.log', level=logging.INFO)
# Load the model using the pipeline API for text generation
model_name = "2nji/llama3-platypus"
generator = pipeline('text-generation', model=model_name)
# Example prompt
prompt = "Hello! How can AI help humans in daily life?"
# Generate response
try:
responses = generator(prompt, max_length=50) # Adjust max_length as needed
response_text = responses[0]['generated_text']
print("Model response:", response_text)
# Log the output
logging.info("Sent prompt: %s", prompt)
logging.info("Received response: %s", response_text)
except Exception as e:
logging.error("Error in generating response: %s", str(e))
Training and evaluation data
The model was fine-tuned on the open_platypus dataset.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
Training results
The model was trained on a single NVIDIA H100 GPU with the following results:
- Loss: 0.6769
- Accuracy: 0.8116
Framework versions
- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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