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 3450, checkpoint
Browse files
last-checkpoint/adapter_model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 70430032
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c07b78eeecb81ff2ff3db73ffd3bd439147272dbc61cf6254569bd9159395ba8
|
| 3 |
size 70430032
|
last-checkpoint/optimizer.pt
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 141058579
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d73e452e1ac35f87aeeb399a8625ee3f27dd7ebd2bd25f53d0ed99c0597496da
|
| 3 |
size 141058579
|
last-checkpoint/rng_state.pth
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 14645
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b6191bf4b81ba084172fa2bdfcbf72f3b9527022fdd20bd4494e733bafb423a9
|
| 3 |
size 14645
|
last-checkpoint/scheduler.pt
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 1465
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:a1eb5a2d64117b6f060427c98f9e7c895a461779409d0a875ab55445d57100aa
|
| 3 |
size 1465
|
last-checkpoint/trainer_state.json
CHANGED
|
@@ -2,9 +2,9 @@
|
|
| 2 |
"best_global_step": null,
|
| 3 |
"best_metric": null,
|
| 4 |
"best_model_checkpoint": null,
|
| 5 |
-
"epoch": 0.
|
| 6 |
"eval_steps": 500,
|
| 7 |
-
"global_step":
|
| 8 |
"is_hyper_param_search": false,
|
| 9 |
"is_local_process_zero": true,
|
| 10 |
"is_world_process_zero": true,
|
|
@@ -3458,6 +3458,16 @@
|
|
| 3458 |
"mean_token_accuracy": 0.7715103484690189,
|
| 3459 |
"num_tokens": 16029965.0,
|
| 3460 |
"step": 3440
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 3461 |
}
|
| 3462 |
],
|
| 3463 |
"logging_steps": 10,
|
|
@@ -3477,7 +3487,7 @@
|
|
| 3477 |
"attributes": {}
|
| 3478 |
}
|
| 3479 |
},
|
| 3480 |
-
"total_flos": 7.
|
| 3481 |
"train_batch_size": 4,
|
| 3482 |
"trial_name": null,
|
| 3483 |
"trial_params": null
|
|
|
|
| 2 |
"best_global_step": null,
|
| 3 |
"best_metric": null,
|
| 4 |
"best_model_checkpoint": null,
|
| 5 |
+
"epoch": 0.736,
|
| 6 |
"eval_steps": 500,
|
| 7 |
+
"global_step": 3450,
|
| 8 |
"is_hyper_param_search": false,
|
| 9 |
"is_local_process_zero": true,
|
| 10 |
"is_world_process_zero": true,
|
|
|
|
| 3458 |
"mean_token_accuracy": 0.7715103484690189,
|
| 3459 |
"num_tokens": 16029965.0,
|
| 3460 |
"step": 3440
|
| 3461 |
+
},
|
| 3462 |
+
{
|
| 3463 |
+
"entropy": 0.9526532724499702,
|
| 3464 |
+
"epoch": 0.736,
|
| 3465 |
+
"grad_norm": 0.24097904562950134,
|
| 3466 |
+
"learning_rate": 7.312409608752208e-05,
|
| 3467 |
+
"loss": 1.0411754608154298,
|
| 3468 |
+
"mean_token_accuracy": 0.7602859303355217,
|
| 3469 |
+
"num_tokens": 16078997.0,
|
| 3470 |
+
"step": 3450
|
| 3471 |
}
|
| 3472 |
],
|
| 3473 |
"logging_steps": 10,
|
|
|
|
| 3487 |
"attributes": {}
|
| 3488 |
}
|
| 3489 |
},
|
| 3490 |
+
"total_flos": 7.615748174960026e+16,
|
| 3491 |
"train_batch_size": 4,
|
| 3492 |
"trial_name": null,
|
| 3493 |
"trial_params": null
|