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 2700, 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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| 2708 |
"mean_token_accuracy": 0.7823046505451202,
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"num_tokens": 12496420.0,
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| 2710 |
"step": 2690
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}
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],
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| 2713 |
"logging_steps": 10,
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"attributes": {}
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}
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},
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"total_flos": 5.
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| 2731 |
"train_batch_size": 4,
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| 2732 |
"trial_name": null,
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| 2733 |
"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": 0.576,
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"eval_steps": 500,
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"global_step": 2700,
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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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| 2708 |
"mean_token_accuracy": 0.7823046505451202,
|
| 2709 |
"num_tokens": 12496420.0,
|
| 2710 |
"step": 2690
|
| 2711 |
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},
|
| 2712 |
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{
|
| 2713 |
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"entropy": 0.9863057106733322,
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| 2714 |
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"epoch": 0.576,
|
| 2715 |
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"grad_norm": 0.2574012279510498,
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| 2716 |
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"learning_rate": 8.372914715119269e-05,
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| 2717 |
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"loss": 1.0647315979003906,
|
| 2718 |
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"mean_token_accuracy": 0.7581366948783398,
|
| 2719 |
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"num_tokens": 12541298.0,
|
| 2720 |
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"step": 2700
|
| 2721 |
}
|
| 2722 |
],
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| 2723 |
"logging_steps": 10,
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| 2737 |
"attributes": {}
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| 2738 |
}
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| 2739 |
},
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| 2740 |
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"total_flos": 5.946095835240038e+16,
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| 2741 |
"train_batch_size": 4,
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| 2742 |
"trial_name": null,
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| 2743 |
"trial_params": null
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