Instructions to use blackbeard334/CodeLlama-7b-Instruct-hf__reverse_engineer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use blackbeard334/CodeLlama-7b-Instruct-hf__reverse_engineer with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("blackbeard334/CodeLlama-7b-Instruct-hf__reverse_engineer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 9790, checkpoint
Browse files
last-checkpoint/optimizer.pt
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last-checkpoint/rng_state.pth
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last-checkpoint/scheduler.pt
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last-checkpoint/trainer_state.json
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"best_global_step": null,
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"best_metric": null,
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"best_model_checkpoint": null,
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"epoch": 75.
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"eval_steps": 500,
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"global_step":
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"is_hyper_param_search": false,
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"is_local_process_zero": true,
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"is_world_process_zero": true,
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"mean_token_accuracy": 0.6435405910015106,
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"num_tokens": 35666156.0,
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"step": 9780
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"logging_steps": 5,
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"attributes": {}
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"total_flos": 1.
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"train_batch_size": 4,
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"best_global_step": null,
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"best_metric": null,
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"best_model_checkpoint": null,
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"epoch": 75.3076923076923,
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"eval_steps": 500,
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"global_step": 9790,
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"is_hyper_param_search": false,
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"is_local_process_zero": true,
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"is_world_process_zero": true,
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| 17612 |
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"learning_rate": 1.08e-06,
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"step": 9785
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{
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"epoch": 75.3076923076923,
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"grad_norm": 0.0,
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"learning_rate": 1.055e-06,
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"loss": 1.7454,
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"mean_token_accuracy": 0.6543306648731232,
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"num_tokens": 35703139.0,
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"step": 9790
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"logging_steps": 5,
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"attributes": {}
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"total_flos": 1.8105827390049485e+18,
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"train_batch_size": 4,
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