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 3570, 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:fa49be33f5b9ddc9a70d3cf2e331246665fdcc6eaa1b6d35c88e278e335395d1
|
| 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 sha256:03ce0c4b697fd154bf9c17d8a3ff46d3ba6b113761b3516973e5030159031046
|
| 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 sha256:5fd91a7367595c7c2ceb2f2618855abefa9c538ea9447f9bb1aa7919f131488a
|
| 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 sha256:3ba1c8310baa6581a06ef3da7d324d8ec2679b844a28c71f2f755bff61dd6cb5
|
| 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,
|
|
@@ -3578,6 +3578,16 @@
|
|
| 3578 |
"mean_token_accuracy": 0.7682245895266533,
|
| 3579 |
"num_tokens": 16586157.0,
|
| 3580 |
"step": 3560
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 3581 |
}
|
| 3582 |
],
|
| 3583 |
"logging_steps": 10,
|
|
@@ -3597,7 +3607,7 @@
|
|
| 3597 |
"attributes": {}
|
| 3598 |
}
|
| 3599 |
},
|
| 3600 |
-
"total_flos": 7.
|
| 3601 |
"train_batch_size": 4,
|
| 3602 |
"trial_name": null,
|
| 3603 |
"trial_params": null
|
|
|
|
| 2 |
"best_global_step": null,
|
| 3 |
"best_metric": null,
|
| 4 |
"best_model_checkpoint": null,
|
| 5 |
+
"epoch": 0.7616,
|
| 6 |
"eval_steps": 500,
|
| 7 |
+
"global_step": 3570,
|
| 8 |
"is_hyper_param_search": false,
|
| 9 |
"is_local_process_zero": true,
|
| 10 |
"is_world_process_zero": true,
|
|
|
|
| 3578 |
"mean_token_accuracy": 0.7682245895266533,
|
| 3579 |
"num_tokens": 16586157.0,
|
| 3580 |
"step": 3560
|
| 3581 |
+
},
|
| 3582 |
+
{
|
| 3583 |
+
"entropy": 0.8198081150650978,
|
| 3584 |
+
"epoch": 0.7616,
|
| 3585 |
+
"grad_norm": 0.3035356104373932,
|
| 3586 |
+
"learning_rate": 7.126327879619807e-05,
|
| 3587 |
+
"loss": 0.8880753517150879,
|
| 3588 |
+
"mean_token_accuracy": 0.7954168729484081,
|
| 3589 |
+
"num_tokens": 16623992.0,
|
| 3590 |
+
"step": 3570
|
| 3591 |
}
|
| 3592 |
],
|
| 3593 |
"logging_steps": 10,
|
|
|
|
| 3607 |
"attributes": {}
|
| 3608 |
}
|
| 3609 |
},
|
| 3610 |
+
"total_flos": 7.879059657379123e+16,
|
| 3611 |
"train_batch_size": 4,
|
| 3612 |
"trial_name": null,
|
| 3613 |
"trial_params": null
|