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 270, 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:a83e334499e13010cbbf5cecb0bb22142b1face7b601f1296f3bc5ff257e68d7
|
| 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 sha256:029229960761235bb4caff3db2819104d0b659f6e15151da4eb01d39467ff942
|
| 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 sha256:0154f3c4d93ae23ed70888eed113babeac6a19733650f59df1defa4fafdf197c
|
| 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 sha256:6142b90a0b9151f21aa0bf0aec5bd23876596c79fceb1461e144f427fbe24d9b
|
| 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,
|
|
@@ -278,6 +278,16 @@
|
|
| 278 |
"mean_token_accuracy": 0.7475032344460487,
|
| 279 |
"num_tokens": 1203153.0,
|
| 280 |
"step": 260
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 281 |
}
|
| 282 |
],
|
| 283 |
"logging_steps": 10,
|
|
@@ -297,7 +307,7 @@
|
|
| 297 |
"attributes": {}
|
| 298 |
}
|
| 299 |
},
|
| 300 |
-
"total_flos":
|
| 301 |
"train_batch_size": 4,
|
| 302 |
"trial_name": null,
|
| 303 |
"trial_params": null
|
|
|
|
| 2 |
"best_global_step": null,
|
| 3 |
"best_metric": null,
|
| 4 |
"best_model_checkpoint": null,
|
| 5 |
+
"epoch": 0.0576,
|
| 6 |
"eval_steps": 500,
|
| 7 |
+
"global_step": 270,
|
| 8 |
"is_hyper_param_search": false,
|
| 9 |
"is_local_process_zero": true,
|
| 10 |
"is_world_process_zero": true,
|
|
|
|
| 278 |
"mean_token_accuracy": 0.7475032344460487,
|
| 279 |
"num_tokens": 1203153.0,
|
| 280 |
"step": 260
|
| 281 |
+
},
|
| 282 |
+
{
|
| 283 |
+
"entropy": 1.0000992961227895,
|
| 284 |
+
"epoch": 0.0576,
|
| 285 |
+
"grad_norm": 0.2648380696773529,
|
| 286 |
+
"learning_rate": 8.966666666666666e-05,
|
| 287 |
+
"loss": 1.0818438529968262,
|
| 288 |
+
"mean_token_accuracy": 0.7550511255860328,
|
| 289 |
+
"num_tokens": 1246554.0,
|
| 290 |
+
"step": 270
|
| 291 |
}
|
| 292 |
],
|
| 293 |
"logging_steps": 10,
|
|
|
|
| 307 |
"attributes": {}
|
| 308 |
}
|
| 309 |
},
|
| 310 |
+
"total_flos": 5944904466201600.0,
|
| 311 |
"train_batch_size": 4,
|
| 312 |
"trial_name": null,
|
| 313 |
"trial_params": null
|