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 3370, 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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"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": 0.7655998513102531,
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"step": 3360
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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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| 3401 |
"train_batch_size": 4,
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"trial_name": null,
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"is_world_process_zero": true,
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| 3378 |
"mean_token_accuracy": 0.7655998513102531,
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| 3379 |
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"step": 3360
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| 3381 |
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{
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| 3383 |
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"entropy": 0.8534669198095799,
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| 3384 |
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| 3385 |
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"grad_norm": 0.20464476943016052,
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| 3386 |
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"learning_rate": 7.434267462852496e-05,
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| 3387 |
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"loss": 0.9573710441589356,
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| 3388 |
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"mean_token_accuracy": 0.7839296951889991,
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| 3389 |
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"num_tokens": 15691801.0,
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| 3390 |
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"step": 3370
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| 3391 |
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| 3392 |
],
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| 3393 |
"logging_steps": 10,
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"attributes": {}
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"total_flos": 7.432897359605146e+16,
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| 3411 |
"train_batch_size": 4,
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| 3412 |
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| 3413 |
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