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The following models are available to use with our fine-tuning API. Get started with fine-tuning a model!
  • Training Precision Type indicates the precision type used during training for each model.
    • AMP (Automated Mixed Precision): AMP allows the training speed to be faster with less memory usage while preserving convergence behavior compared to using float32. Learn more about AMP in this PyTorch blog.
    • bf16 (bfloat 16): This uses bf16 for all weights. Some large models on our platform uses full bf16 training for better memory usage and training speed.
  • Long-context fine-tuning of Llama 3.1 (8B) Reference, Llama 3.1 (8B) Reference, Llama 3.1 (70B) Reference, Llama 3.1 Instruct (70B) Reference for context sizes of 32K-131K is only supported using the LoRA method.
  • For Llama 3.1 (405B) Fine-tuning, please contact us.

LoRA Fine-tuning

LoRA Long-context Fine-tuning

1* — Gradient accumulation 8 is used, so effectively you will get batch size 8 (iteration time is slower).

Full Fine-tuning

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