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 1860, 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 sha256:6425eeda19ba056ee2b342cda29d5c0bd62bb338add5362226aed1306b74a862
|
| 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 sha256:622066bcb88b8bee80fb586cbff8d63c12b45207f4bd8a9555fd879998bc2c7a
|
| 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 sha256:6900bdcd2a41c2c94e4c353bba1be97fd1f96ffaf529e7eb7f2b15dc3f32bb65
|
| 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 sha256:3389e098396e0e90ec77ded90427ea77132ff66ec75ee7e8cc8afe1416926945
|
| 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,
|
|
@@ -1868,6 +1868,16 @@
|
|
| 1868 |
"mean_token_accuracy": 0.7662550717592239,
|
| 1869 |
"num_tokens": 8579411.0,
|
| 1870 |
"step": 1850
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1871 |
}
|
| 1872 |
],
|
| 1873 |
"logging_steps": 10,
|
|
@@ -1887,7 +1897,7 @@
|
|
| 1887 |
"attributes": {}
|
| 1888 |
}
|
| 1889 |
},
|
| 1890 |
-
"total_flos": 4.
|
| 1891 |
"train_batch_size": 4,
|
| 1892 |
"trial_name": null,
|
| 1893 |
"trial_params": null
|
|
|
|
| 2 |
"best_global_step": null,
|
| 3 |
"best_metric": null,
|
| 4 |
"best_model_checkpoint": null,
|
| 5 |
+
"epoch": 0.3968,
|
| 6 |
"eval_steps": 500,
|
| 7 |
+
"global_step": 1860,
|
| 8 |
"is_hyper_param_search": false,
|
| 9 |
"is_local_process_zero": true,
|
| 10 |
"is_world_process_zero": true,
|
|
|
|
| 1868 |
"mean_token_accuracy": 0.7662550717592239,
|
| 1869 |
"num_tokens": 8579411.0,
|
| 1870 |
"step": 1850
|
| 1871 |
+
},
|
| 1872 |
+
{
|
| 1873 |
+
"entropy": 0.820501434057951,
|
| 1874 |
+
"epoch": 0.3968,
|
| 1875 |
+
"grad_norm": 0.26821690797805786,
|
| 1876 |
+
"learning_rate": 9.289476978851976e-05,
|
| 1877 |
+
"loss": 0.8598980903625488,
|
| 1878 |
+
"mean_token_accuracy": 0.7888635769486427,
|
| 1879 |
+
"num_tokens": 8623046.0,
|
| 1880 |
+
"step": 1860
|
| 1881 |
}
|
| 1882 |
],
|
| 1883 |
"logging_steps": 10,
|
|
|
|
| 1897 |
"attributes": {}
|
| 1898 |
}
|
| 1899 |
},
|
| 1900 |
+
"total_flos": 4.095164350061568e+16,
|
| 1901 |
"train_batch_size": 4,
|
| 1902 |
"trial_name": null,
|
| 1903 |
"trial_params": null
|