Andyrasika/fine-tuning-llama

experiments

This model is a fine-tuned version of TinyPixel/Llama-2-7B-bf16-sharded on the dialogstudio dataset. It achieves the following results on the evaluation set:

• Loss: 1.8522

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 4
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.05
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
1.9048	0.4	22	1.9220
1.824	0.8	44	1.8809
1.6784	1.2	66	1.8619
1.77	1.6	88	1.8537
1.6501	2.0	110	1.8522

from peft import AutoPeftModelForCausalLM

trained_model = AutoPeftModelForCausalLM.from_pretrained(
    "Andyrasika/fine-tuning-llama",
    low_cpu_mem_usage=True,
)

merged_model = model.merge_and_unload()
merged_model.save_pretrained("merged_model", safe_serialization=True)
tokenizer.save_pretrained("merged_model")

Framework versions

• Transformers 4.32.1
• Pytorch 2.0.1+cu118
• Datasets 2.14.4
• Tokenizers 0.13.3