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--- |
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license: mit |
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datasets: |
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- Replete-AI/code_bagel |
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language: |
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- en |
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tags: |
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- code |
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--- |
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### Base_model |
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microsoft/Phi-3-medium-128k-instruct(https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) |
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### Datasets |
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Replete-AI/code_bagel(https://huggingface.co/datasets/Replete-AI/code_bagel) |
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### Train Loss |
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### Train State |
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Trainable params: 27852800 || all params: 13988090880 || trainable%: 0.1991 |
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Total Training Duration：69h18m17s |
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{ |
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"epoch": 0.9999679800589659, |
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"total_flos": 1.446273483573748e+20, |
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"train_loss": 0.44412665014957775, |
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"train_runtime": 249497.725, |
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"train_samples_per_second": 13.018, |
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"train_steps_per_second": 0.102 |
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} |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 128 |
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- total_eval_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 1200 |
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- num_epochs: 1.0 |
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### I personally fine-tuned the largest dataset, which took the most time. |
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