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 1210, 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 sha256:43fcad317f9dc0279c2bdfe2776581fe1e9ded6c6b456e1df675c7d5f9b7ab4c
|
| 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:f8a0fdb8bb12f98e024e4e681c21868d90f54ee8cb87fd85bedfcae773d8ae33
|
| 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 sha256:118e54e4238fbac6d96a3f5db54dfaa2da72293ef9253831a39b22d03a7082d9
|
| 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 sha256:26f70b2baed2af61db86c660a4d5947a9d3c73e1e277ed144c6224b03af39ae7
|
| 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,
|
|
@@ -1218,6 +1218,16 @@
|
|
| 1218 |
"mean_token_accuracy": 0.7419089064002037,
|
| 1219 |
"num_tokens": 5592981.0,
|
| 1220 |
"step": 1200
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1221 |
}
|
| 1222 |
],
|
| 1223 |
"logging_steps": 10,
|
|
@@ -1237,7 +1247,7 @@
|
|
| 1237 |
"attributes": {}
|
| 1238 |
}
|
| 1239 |
},
|
| 1240 |
-
"total_flos": 2.
|
| 1241 |
"train_batch_size": 4,
|
| 1242 |
"trial_name": null,
|
| 1243 |
"trial_params": null
|
|
|
|
| 2 |
"best_global_step": null,
|
| 3 |
"best_metric": null,
|
| 4 |
"best_model_checkpoint": null,
|
| 5 |
+
"epoch": 0.2581333333333333,
|
| 6 |
"eval_steps": 500,
|
| 7 |
+
"global_step": 1210,
|
| 8 |
"is_hyper_param_search": false,
|
| 9 |
"is_local_process_zero": true,
|
| 10 |
"is_world_process_zero": true,
|
|
|
|
| 1218 |
"mean_token_accuracy": 0.7419089064002037,
|
| 1219 |
"num_tokens": 5592981.0,
|
| 1220 |
"step": 1200
|
| 1221 |
+
},
|
| 1222 |
+
{
|
| 1223 |
+
"entropy": 1.115493653714657,
|
| 1224 |
+
"epoch": 0.2581333333333333,
|
| 1225 |
+
"grad_norm": 0.2895198464393616,
|
| 1226 |
+
"learning_rate": 9.754533274949575e-05,
|
| 1227 |
+
"loss": 1.2449783325195312,
|
| 1228 |
+
"mean_token_accuracy": 0.7372826255857945,
|
| 1229 |
+
"num_tokens": 5637868.0,
|
| 1230 |
+
"step": 1210
|
| 1231 |
}
|
| 1232 |
],
|
| 1233 |
"logging_steps": 10,
|
|
|
|
| 1247 |
"attributes": {}
|
| 1248 |
}
|
| 1249 |
},
|
| 1250 |
+
"total_flos": 2.670758300820787e+16,
|
| 1251 |
"train_batch_size": 4,
|
| 1252 |
"trial_name": null,
|
| 1253 |
"trial_params": null
|