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 2550, 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": 0.7522218823432922,
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"num_tokens": 11801605.0,
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"step": 2540
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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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"train_batch_size": 4,
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"trial_name": null,
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"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": 0.544,
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"eval_steps": 500,
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"global_step": 2550,
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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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| 2558 |
"mean_token_accuracy": 0.7522218823432922,
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| 2559 |
"num_tokens": 11801605.0,
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| 2560 |
"step": 2540
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| 2561 |
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| 2562 |
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{
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| 2563 |
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"entropy": 1.0214832991361618,
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| 2564 |
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"epoch": 0.544,
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| 2565 |
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"grad_norm": 0.265127569437027,
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| 2566 |
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"learning_rate": 8.559924985643436e-05,
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| 2567 |
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"loss": 1.1042506217956543,
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| 2568 |
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"mean_token_accuracy": 0.7507105216383934,
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| 2569 |
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"num_tokens": 11847802.0,
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| 2570 |
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"step": 2550
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| 2571 |
}
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| 2572 |
],
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"logging_steps": 10,
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"attributes": {}
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}
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| 2590 |
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"total_flos": 5.619448038607258e+16,
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| 2591 |
"train_batch_size": 4,
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"trial_name": null,
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| 2593 |
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