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 2710, 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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| 2718 |
"mean_token_accuracy": 0.7581366948783398,
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"num_tokens": 12541298.0,
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| 2720 |
"step": 2700
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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": 5.
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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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"best_global_step": null,
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"best_metric": null,
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"best_model_checkpoint": null,
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"epoch": 0.5781333333333334,
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"eval_steps": 500,
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"global_step": 2710,
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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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| 2718 |
"mean_token_accuracy": 0.7581366948783398,
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| 2719 |
"num_tokens": 12541298.0,
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| 2720 |
"step": 2700
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| 2721 |
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},
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| 2722 |
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{
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| 2723 |
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"entropy": 1.001559029519558,
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| 2724 |
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"epoch": 0.5781333333333334,
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| 2725 |
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"grad_norm": 0.3141133487224579,
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| 2726 |
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"learning_rate": 8.360118417451113e-05,
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| 2727 |
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"loss": 1.1159303665161133,
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| 2728 |
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"mean_token_accuracy": 0.7509155049920082,
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| 2729 |
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"num_tokens": 12586250.0,
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| 2730 |
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"step": 2710
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| 2731 |
}
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| 2732 |
],
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| 2733 |
"logging_steps": 10,
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| 2747 |
"attributes": {}
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| 2748 |
}
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| 2749 |
},
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| 2750 |
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"total_flos": 5.966754642983731e+16,
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| 2751 |
"train_batch_size": 4,
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| 2752 |
"trial_name": null,
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| 2753 |
"trial_params": null
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