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 170, 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:d74edd1550f3054e6c6139caf7d16e4fba4e83f275ff94e54f95511292c6eef0
|
| 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 sha256:80f329ce846ef5208cb4db50950c0159fb4339bbdfa9deca86421fcb3a3e22fe
|
| 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 sha256:05ed2df097c924d834b0115a6978c7176f079a672434526c0cd830d074b234e6
|
| 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 sha256:1e24a40a25924ca0c915ae0e90b167f67b5630f335c6534762576ecaa3d8bf48
|
| 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,
|
|
@@ -178,6 +178,16 @@
|
|
| 178 |
"mean_token_accuracy": 0.7412141926586628,
|
| 179 |
"num_tokens": 751635.0,
|
| 180 |
"step": 160
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 181 |
}
|
| 182 |
],
|
| 183 |
"logging_steps": 10,
|
|
@@ -197,7 +207,7 @@
|
|
| 197 |
"attributes": {}
|
| 198 |
}
|
| 199 |
},
|
| 200 |
-
"total_flos":
|
| 201 |
"train_batch_size": 4,
|
| 202 |
"trial_name": null,
|
| 203 |
"trial_params": null
|
|
|
|
| 2 |
"best_global_step": null,
|
| 3 |
"best_metric": null,
|
| 4 |
"best_model_checkpoint": null,
|
| 5 |
+
"epoch": 0.03626666666666667,
|
| 6 |
"eval_steps": 500,
|
| 7 |
+
"global_step": 170,
|
| 8 |
"is_hyper_param_search": false,
|
| 9 |
"is_local_process_zero": true,
|
| 10 |
"is_world_process_zero": true,
|
|
|
|
| 178 |
"mean_token_accuracy": 0.7412141926586628,
|
| 179 |
"num_tokens": 751635.0,
|
| 180 |
"step": 160
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"entropy": 1.0946420103311538,
|
| 184 |
+
"epoch": 0.03626666666666667,
|
| 185 |
+
"grad_norm": 0.2150825560092926,
|
| 186 |
+
"learning_rate": 5.633333333333334e-05,
|
| 187 |
+
"loss": 1.1224396705627442,
|
| 188 |
+
"mean_token_accuracy": 0.7382922798395157,
|
| 189 |
+
"num_tokens": 794488.0,
|
| 190 |
+
"step": 170
|
| 191 |
}
|
| 192 |
],
|
| 193 |
"logging_steps": 10,
|
|
|
|
| 207 |
"attributes": {}
|
| 208 |
}
|
| 209 |
},
|
| 210 |
+
"total_flos": 3810848905328640.0,
|
| 211 |
"train_batch_size": 4,
|
| 212 |
"trial_name": null,
|
| 213 |
"trial_params": null
|