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 1800, 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 sha256:83783d606cd824f2f6041919fa0d046573a81fa8c2147a263d33471b60bdf4f3
|
| 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 sha256:36c77f8336c1ed8c1c2baa9a0a421b359896bd3ce32961171b7eeb8fc3c09e37
|
| 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:f1a9978791bbf62f5f5d42f8b5d355d0fd47d1899bdded4ed3bea740f2601d46
|
| 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 sha256:180a1823af3df2638ec65911fe40ac441d8f1f67d7dbb8494c98c5503d586af5
|
| 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,
|
|
@@ -1808,6 +1808,16 @@
|
|
| 1808 |
"mean_token_accuracy": 0.746114706993103,
|
| 1809 |
"num_tokens": 8300355.0,
|
| 1810 |
"step": 1790
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1811 |
}
|
| 1812 |
],
|
| 1813 |
"logging_steps": 10,
|
|
@@ -1827,7 +1837,7 @@
|
|
| 1827 |
"attributes": {}
|
| 1828 |
}
|
| 1829 |
},
|
| 1830 |
-
"total_flos": 3.
|
| 1831 |
"train_batch_size": 4,
|
| 1832 |
"trial_name": null,
|
| 1833 |
"trial_params": null
|
|
|
|
| 2 |
"best_global_step": null,
|
| 3 |
"best_metric": null,
|
| 4 |
"best_model_checkpoint": null,
|
| 5 |
+
"epoch": 0.384,
|
| 6 |
"eval_steps": 500,
|
| 7 |
+
"global_step": 1800,
|
| 8 |
"is_hyper_param_search": false,
|
| 9 |
"is_local_process_zero": true,
|
| 10 |
"is_world_process_zero": true,
|
|
|
|
| 1808 |
"mean_token_accuracy": 0.746114706993103,
|
| 1809 |
"num_tokens": 8300355.0,
|
| 1810 |
"step": 1790
|
| 1811 |
+
},
|
| 1812 |
+
{
|
| 1813 |
+
"entropy": 0.943967767059803,
|
| 1814 |
+
"epoch": 0.384,
|
| 1815 |
+
"grad_norm": 0.2434380203485489,
|
| 1816 |
+
"learning_rate": 9.34190507139859e-05,
|
| 1817 |
+
"loss": 0.9968074798583985,
|
| 1818 |
+
"mean_token_accuracy": 0.7640544638037682,
|
| 1819 |
+
"num_tokens": 8346150.0,
|
| 1820 |
+
"step": 1800
|
| 1821 |
}
|
| 1822 |
],
|
| 1823 |
"logging_steps": 10,
|
|
|
|
| 1837 |
"attributes": {}
|
| 1838 |
}
|
| 1839 |
},
|
| 1840 |
+
"total_flos": 3.965886390401126e+16,
|
| 1841 |
"train_batch_size": 4,
|
| 1842 |
"trial_name": null,
|
| 1843 |
"trial_params": null
|