experiments

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.4332

Model description

MODEL_NAME = "/content/blackhole33/llama-5000-sample-peft"
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16
)

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=True)

model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME, quantization_config=quantization_config, device_map="auto"
)

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: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: constant
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 1
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
1.3475	0.2	100	1.5142
1.4979	0.4	200	1.4703
1.4307	0.6	300	1.4510
1.3795	0.8	400	1.4434
1.3847	1.0	500	1.4332

Framework versions

• PEFT 0.12.0
• Transformers 4.44.1
• Pytorch 2.3.1+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1