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 3900, 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 sha256:3ea9e6fd508e45d1b0ae522a9810346fe23625e219516b549ad54254c64dbd6e
|
| 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 sha256:78f606c519b852382af670295682d3d8d2c90a3ce42a4d99f71bb1cccf542329
|
| 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:fe533b221cc6b1eaa1d9441627c260ce867c6c45607cc6abb24180b84588829b
|
| 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:c56064c1735ee527f04c4d25c090a26dce7f3ac690032151cffbf5e97e8d4c1f
|
| 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,
|
|
@@ -3908,6 +3908,16 @@
|
|
| 3908 |
"mean_token_accuracy": 0.7845224231481552,
|
| 3909 |
"num_tokens": 18135064.0,
|
| 3910 |
"step": 3890
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 3911 |
}
|
| 3912 |
],
|
| 3913 |
"logging_steps": 10,
|
|
@@ -3927,7 +3937,7 @@
|
|
| 3927 |
"attributes": {}
|
| 3928 |
}
|
| 3929 |
},
|
| 3930 |
-
"total_flos": 8.
|
| 3931 |
"train_batch_size": 4,
|
| 3932 |
"trial_name": null,
|
| 3933 |
"trial_params": null
|
|
|
|
| 2 |
"best_global_step": null,
|
| 3 |
"best_metric": null,
|
| 4 |
"best_model_checkpoint": null,
|
| 5 |
+
"epoch": 0.832,
|
| 6 |
"eval_steps": 500,
|
| 7 |
+
"global_step": 3900,
|
| 8 |
"is_hyper_param_search": false,
|
| 9 |
"is_local_process_zero": true,
|
| 10 |
"is_world_process_zero": true,
|
|
|
|
| 3908 |
"mean_token_accuracy": 0.7845224231481552,
|
| 3909 |
"num_tokens": 18135064.0,
|
| 3910 |
"step": 3890
|
| 3911 |
+
},
|
| 3912 |
+
{
|
| 3913 |
+
"entropy": 1.0117795512080192,
|
| 3914 |
+
"epoch": 0.832,
|
| 3915 |
+
"grad_norm": 0.26254504919052124,
|
| 3916 |
+
"learning_rate": 6.596677316924355e-05,
|
| 3917 |
+
"loss": 1.1285503387451172,
|
| 3918 |
+
"mean_token_accuracy": 0.7520590081810952,
|
| 3919 |
+
"num_tokens": 18184374.0,
|
| 3920 |
+
"step": 3900
|
| 3921 |
}
|
| 3922 |
],
|
| 3923 |
"logging_steps": 10,
|
|
|
|
| 3937 |
"attributes": {}
|
| 3938 |
}
|
| 3939 |
},
|
| 3940 |
+
"total_flos": 8.607022187339674e+16,
|
| 3941 |
"train_batch_size": 4,
|
| 3942 |
"trial_name": null,
|
| 3943 |
"trial_params": null
|