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 20, 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:b64336964bc4db0ec301681ba18f9e670e5c49901da3e7649561585811773a28
|
| 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 sha256:9377de11befdd479505b15c4b72424f103a13d434b5966a26370f1bbd95f4edd
|
| 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 sha256:4827ed25954d4dc04f5898f4147ad6ed2ac6a723dc4502ee69d75766dc127a9c
|
| 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 sha256:560de70bab917d96290b18fce132deecf69387371fbf756275f24d8fe0b28ff7
|
| 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,
|
|
@@ -28,6 +28,16 @@
|
|
| 28 |
"mean_token_accuracy": 0.65218452612559,
|
| 29 |
"num_tokens": 39906.0,
|
| 30 |
"step": 10
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 31 |
}
|
| 32 |
],
|
| 33 |
"logging_steps": 10,
|
|
@@ -47,7 +57,7 @@
|
|
| 47 |
"attributes": {}
|
| 48 |
}
|
| 49 |
},
|
| 50 |
-
"total_flos":
|
| 51 |
"train_batch_size": 4,
|
| 52 |
"trial_name": null,
|
| 53 |
"trial_params": null
|
|
|
|
| 2 |
"best_global_step": null,
|
| 3 |
"best_metric": null,
|
| 4 |
"best_model_checkpoint": null,
|
| 5 |
+
"epoch": 0.004266666666666667,
|
| 6 |
"eval_steps": 500,
|
| 7 |
+
"global_step": 20,
|
| 8 |
"is_hyper_param_search": false,
|
| 9 |
"is_local_process_zero": true,
|
| 10 |
"is_world_process_zero": true,
|
|
|
|
| 28 |
"mean_token_accuracy": 0.65218452612559,
|
| 29 |
"num_tokens": 39906.0,
|
| 30 |
"step": 10
|
| 31 |
+
},
|
| 32 |
+
{
|
| 33 |
+
"entropy": 1.1284118384122848,
|
| 34 |
+
"epoch": 0.004266666666666667,
|
| 35 |
+
"grad_norm": 0.49733078479766846,
|
| 36 |
+
"learning_rate": 6.333333333333334e-06,
|
| 37 |
+
"loss": 1.9721708297729492,
|
| 38 |
+
"mean_token_accuracy": 0.6748957321047783,
|
| 39 |
+
"num_tokens": 90428.0,
|
| 40 |
+
"step": 20
|
| 41 |
}
|
| 42 |
],
|
| 43 |
"logging_steps": 10,
|
|
|
|
| 57 |
"attributes": {}
|
| 58 |
}
|
| 59 |
},
|
| 60 |
+
"total_flos": 443951686388736.0,
|
| 61 |
"train_batch_size": 4,
|
| 62 |
"trial_name": null,
|
| 63 |
"trial_params": null
|