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 1590, 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:a15a606af16096574d55da5bfa0c97bd61ac1873e1a2c616ae0869bbee98ef94
|
| 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:a5fff7f0ebe315f406de49ed6476679f89574d84ece5cd848bbcbf5b1865f732
|
| 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:bb3fa2d957c53aaffc3cde109a07f2f86fccb5f9a7691be7af2f77587f268701
|
| 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 sha256:032046bbcdfa1f36d1626b0ebf00f2c99f764144f3ab3f5ab38455677cb9010b
|
| 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,
|
|
@@ -1598,6 +1598,16 @@
|
|
| 1598 |
"mean_token_accuracy": 0.7790701374411583,
|
| 1599 |
"num_tokens": 7352371.0,
|
| 1600 |
"step": 1580
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1601 |
}
|
| 1602 |
],
|
| 1603 |
"logging_steps": 10,
|
|
@@ -1617,7 +1627,7 @@
|
|
| 1617 |
"attributes": {}
|
| 1618 |
}
|
| 1619 |
},
|
| 1620 |
-
"total_flos": 3.
|
| 1621 |
"train_batch_size": 4,
|
| 1622 |
"trial_name": null,
|
| 1623 |
"trial_params": null
|
|
|
|
| 2 |
"best_global_step": null,
|
| 3 |
"best_metric": null,
|
| 4 |
"best_model_checkpoint": null,
|
| 5 |
+
"epoch": 0.3392,
|
| 6 |
"eval_steps": 500,
|
| 7 |
+
"global_step": 1590,
|
| 8 |
"is_hyper_param_search": false,
|
| 9 |
"is_local_process_zero": true,
|
| 10 |
"is_world_process_zero": true,
|
|
|
|
| 1598 |
"mean_token_accuracy": 0.7790701374411583,
|
| 1599 |
"num_tokens": 7352371.0,
|
| 1600 |
"step": 1580
|
| 1601 |
+
},
|
| 1602 |
+
{
|
| 1603 |
+
"entropy": 0.8671779796481133,
|
| 1604 |
+
"epoch": 0.3392,
|
| 1605 |
+
"grad_norm": 0.32672831416130066,
|
| 1606 |
+
"learning_rate": 9.510514465900653e-05,
|
| 1607 |
+
"loss": 0.9251022338867188,
|
| 1608 |
+
"mean_token_accuracy": 0.7759940758347511,
|
| 1609 |
+
"num_tokens": 7393383.0,
|
| 1610 |
+
"step": 1590
|
| 1611 |
}
|
| 1612 |
],
|
| 1613 |
"logging_steps": 10,
|
|
|
|
| 1627 |
"attributes": {}
|
| 1628 |
}
|
| 1629 |
},
|
| 1630 |
+
"total_flos": 3.503919077350195e+16,
|
| 1631 |
"train_batch_size": 4,
|
| 1632 |
"trial_name": null,
|
| 1633 |
"trial_params": null
|