Instructions to use moos124/code-reasoning-0.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moos124/code-reasoning-0.5b with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("moos124/code-reasoning-0.5b", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 3310, checkpoint
Browse files
last-checkpoint/adapter_model.safetensors
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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": 0.
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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": 0.7683326050639152,
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"num_tokens": 15350117.0,
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"step": 3300
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}
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],
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"logging_steps": 10,
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"attributes": {}
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}
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},
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"total_flos": 7.
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"train_batch_size": 4,
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"trial_name": null,
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"best_global_step": null,
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"epoch": 0.7061333333333333,
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"global_step": 3310,
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"is_local_process_zero": true,
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"is_world_process_zero": true,
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| 3318 |
"mean_token_accuracy": 0.7683326050639152,
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| 3319 |
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| 3320 |
"step": 3300
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| 3322 |
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{
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| 3323 |
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"entropy": 1.0591608263552188,
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| 3324 |
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"epoch": 0.7061333333333333,
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| 3325 |
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"grad_norm": 0.28637412190437317,
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| 3326 |
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"learning_rate": 7.524441010181889e-05,
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| 3327 |
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"loss": 1.1826082229614259,
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| 3328 |
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"mean_token_accuracy": 0.7397180199623108,
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| 3329 |
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"num_tokens": 15404947.0,
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| 3330 |
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"step": 3310
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| 3331 |
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| 3332 |
],
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"logging_steps": 10,
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"attributes": {}
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}
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"total_flos": 7.296998380031386e+16,
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"train_batch_size": 4,
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"trial_name": null,
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