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 2800, 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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| 2808 |
"mean_token_accuracy": 0.7538750320672989,
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"num_tokens": 12969810.0,
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| 2810 |
"step": 2790
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| 2811 |
}
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],
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| 2813 |
"logging_steps": 10,
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"attributes": {}
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}
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},
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-
"total_flos": 6.
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| 2831 |
"train_batch_size": 4,
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| 2832 |
"trial_name": null,
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| 2833 |
"trial_params": null
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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": 0.5973333333333334,
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"eval_steps": 500,
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| 7 |
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"global_step": 2800,
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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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| 2808 |
"mean_token_accuracy": 0.7538750320672989,
|
| 2809 |
"num_tokens": 12969810.0,
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| 2810 |
"step": 2790
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| 2811 |
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},
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| 2812 |
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{
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| 2813 |
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"entropy": 0.9516462504863739,
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| 2814 |
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"epoch": 0.5973333333333334,
|
| 2815 |
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"grad_norm": 0.27765893936157227,
|
| 2816 |
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"learning_rate": 8.243158631907382e-05,
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| 2817 |
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"loss": 1.0368030548095704,
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| 2818 |
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"mean_token_accuracy": 0.7694585740566253,
|
| 2819 |
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"num_tokens": 13015439.0,
|
| 2820 |
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"step": 2800
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| 2821 |
}
|
| 2822 |
],
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| 2823 |
"logging_steps": 10,
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| 2837 |
"attributes": {}
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| 2838 |
}
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| 2839 |
},
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| 2840 |
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"total_flos": 6.168558319342694e+16,
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| 2841 |
"train_batch_size": 4,
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| 2842 |
"trial_name": null,
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| 2843 |
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