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 4700, 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 sha256:3b2a68f7d9e0a543fe75b7163a399cf5b0bf1a0ef1750338f105db7351e36270
|
| 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 sha256:5f81b6d162fd96f7d11cb719f3fcf99dac6824efbac7feecd4fd27c5e87e2265
|
| 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 sha256:80c61da9b117d62f98bb1ace4af08410b60db8fefb001196c18c05e0b9940a8f
|
| 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:da625ffd446e8c6673e23bcb9af0578aec3b58f68e107c37a452176de1fa5e45
|
| 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": 1.
|
| 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,
|
|
@@ -4708,6 +4708,16 @@
|
|
| 4708 |
"mean_token_accuracy": 0.7673146160025346,
|
| 4709 |
"num_tokens": 21789348.0,
|
| 4710 |
"step": 4690
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 4711 |
}
|
| 4712 |
],
|
| 4713 |
"logging_steps": 10,
|
|
@@ -4727,7 +4737,7 @@
|
|
| 4727 |
"attributes": {}
|
| 4728 |
}
|
| 4729 |
},
|
| 4730 |
-
"total_flos": 1.
|
| 4731 |
"train_batch_size": 4,
|
| 4732 |
"trial_name": null,
|
| 4733 |
"trial_params": null
|
|
|
|
| 2 |
"best_global_step": null,
|
| 3 |
"best_metric": null,
|
| 4 |
"best_model_checkpoint": null,
|
| 5 |
+
"epoch": 1.00256,
|
| 6 |
"eval_steps": 500,
|
| 7 |
+
"global_step": 4700,
|
| 8 |
"is_hyper_param_search": false,
|
| 9 |
"is_local_process_zero": true,
|
| 10 |
"is_world_process_zero": true,
|
|
|
|
| 4708 |
"mean_token_accuracy": 0.7673146160025346,
|
| 4709 |
"num_tokens": 21789348.0,
|
| 4710 |
"step": 4690
|
| 4711 |
+
},
|
| 4712 |
+
{
|
| 4713 |
+
"entropy": 0.9121152207255363,
|
| 4714 |
+
"epoch": 1.00256,
|
| 4715 |
+
"grad_norm": 0.2546738386154175,
|
| 4716 |
+
"learning_rate": 5.240476485367317e-05,
|
| 4717 |
+
"loss": 0.9231260299682618,
|
| 4718 |
+
"mean_token_accuracy": 0.7732596561312676,
|
| 4719 |
+
"num_tokens": 21834781.0,
|
| 4720 |
+
"step": 4700
|
| 4721 |
}
|
| 4722 |
],
|
| 4723 |
"logging_steps": 10,
|
|
|
|
| 4737 |
"attributes": {}
|
| 4738 |
}
|
| 4739 |
},
|
| 4740 |
+
"total_flos": 1.0338854618522726e+17,
|
| 4741 |
"train_batch_size": 4,
|
| 4742 |
"trial_name": null,
|
| 4743 |
"trial_params": null
|