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 4020, 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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| 4028 |
"mean_token_accuracy": 0.752861674129963,
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"num_tokens": 18701666.0,
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"step": 4010
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}
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],
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| 4033 |
"logging_steps": 10,
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"attributes": {}
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}
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},
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"total_flos": 8.
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| 4051 |
"train_batch_size": 4,
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| 4052 |
"trial_name": null,
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| 4053 |
"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": 0.8576,
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"eval_steps": 500,
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| 7 |
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"global_step": 4020,
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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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| 4028 |
"mean_token_accuracy": 0.752861674129963,
|
| 4029 |
"num_tokens": 18701666.0,
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| 4030 |
"step": 4010
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| 4031 |
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},
|
| 4032 |
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{
|
| 4033 |
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"entropy": 0.9462185628712177,
|
| 4034 |
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"epoch": 0.8576,
|
| 4035 |
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"grad_norm": 0.23720327019691467,
|
| 4036 |
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"learning_rate": 6.398545047100321e-05,
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| 4037 |
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"loss": 1.023193359375,
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| 4038 |
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"mean_token_accuracy": 0.7677365422248841,
|
| 4039 |
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"num_tokens": 18749563.0,
|
| 4040 |
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"step": 4020
|
| 4041 |
}
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| 4042 |
],
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| 4043 |
"logging_steps": 10,
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| 4057 |
"attributes": {}
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| 4058 |
}
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| 4059 |
},
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| 4060 |
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"total_flos": 8.877190800631296e+16,
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| 4061 |
"train_batch_size": 4,
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| 4062 |
"trial_name": null,
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| 4063 |
"trial_params": null
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