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 330, 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 sha256:080e280080559f2791d55f3e9b530866ef137afce957188c20fa52869278185a
|
| 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 sha256:9817785732336e82575cd690ac86eee871b24cb8b95746ad574d47c0f1ba7156
|
| 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 sha256:872fe76418f47a93b19f7178149bfab3a4c567f9bf8f19fe875e06de31f34354
|
| 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:f133a959bfddbf7e52765c340dc6f5b0914229e18f54079bc6ff8b34af89bb5f
|
| 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,
|
|
@@ -338,6 +338,16 @@
|
|
| 338 |
"mean_token_accuracy": 0.7420963421463966,
|
| 339 |
"num_tokens": 1472539.0,
|
| 340 |
"step": 320
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 341 |
}
|
| 342 |
],
|
| 343 |
"logging_steps": 10,
|
|
@@ -357,7 +367,7 @@
|
|
| 357 |
"attributes": {}
|
| 358 |
}
|
| 359 |
},
|
| 360 |
-
"total_flos":
|
| 361 |
"train_batch_size": 4,
|
| 362 |
"trial_name": null,
|
| 363 |
"trial_params": null
|
|
|
|
| 2 |
"best_global_step": null,
|
| 3 |
"best_metric": null,
|
| 4 |
"best_model_checkpoint": null,
|
| 5 |
+
"epoch": 0.0704,
|
| 6 |
"eval_steps": 500,
|
| 7 |
+
"global_step": 330,
|
| 8 |
"is_hyper_param_search": false,
|
| 9 |
"is_local_process_zero": true,
|
| 10 |
"is_world_process_zero": true,
|
|
|
|
| 338 |
"mean_token_accuracy": 0.7420963421463966,
|
| 339 |
"num_tokens": 1472539.0,
|
| 340 |
"step": 320
|
| 341 |
+
},
|
| 342 |
+
{
|
| 343 |
+
"entropy": 0.9518789499998093,
|
| 344 |
+
"epoch": 0.0704,
|
| 345 |
+
"grad_norm": 0.25352534651756287,
|
| 346 |
+
"learning_rate": 9.999748091322068e-05,
|
| 347 |
+
"loss": 0.9646738052368165,
|
| 348 |
+
"mean_token_accuracy": 0.7610545977950096,
|
| 349 |
+
"num_tokens": 1518725.0,
|
| 350 |
+
"step": 330
|
| 351 |
}
|
| 352 |
],
|
| 353 |
"logging_steps": 10,
|
|
|
|
| 367 |
"attributes": {}
|
| 368 |
}
|
| 369 |
},
|
| 370 |
+
"total_flos": 7212522925473792.0,
|
| 371 |
"train_batch_size": 4,
|
| 372 |
"trial_name": null,
|
| 373 |
"trial_params": null
|