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 150, 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 sha256:26f47639f796592780f52b25e192afa68bf4acb6f07e8eeda9d5136f989802f5
|
| 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 sha256:425f572b7e15ce5d3e71c0a0bbc0f2f2b0060b165f55500fedbc4ef8781dfa73
|
| 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:aef73cb2ad9ce5708c43b11454569f2a6c3922ed2564781c9fdac7b7df1c18a5
|
| 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 sha256:2360b5c051850efe138a73d88cec5bae92a688e50d21d633b84e2bdbd726e985
|
| 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,
|
|
@@ -158,6 +158,16 @@
|
|
| 158 |
"mean_token_accuracy": 0.7148015096783638,
|
| 159 |
"num_tokens": 657261.0,
|
| 160 |
"step": 140
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 161 |
}
|
| 162 |
],
|
| 163 |
"logging_steps": 10,
|
|
@@ -177,7 +187,7 @@
|
|
| 177 |
"attributes": {}
|
| 178 |
}
|
| 179 |
},
|
| 180 |
-
"total_flos":
|
| 181 |
"train_batch_size": 4,
|
| 182 |
"trial_name": null,
|
| 183 |
"trial_params": null
|
|
|
|
| 2 |
"best_global_step": null,
|
| 3 |
"best_metric": null,
|
| 4 |
"best_model_checkpoint": null,
|
| 5 |
+
"epoch": 0.032,
|
| 6 |
"eval_steps": 500,
|
| 7 |
+
"global_step": 150,
|
| 8 |
"is_hyper_param_search": false,
|
| 9 |
"is_local_process_zero": true,
|
| 10 |
"is_world_process_zero": true,
|
|
|
|
| 158 |
"mean_token_accuracy": 0.7148015096783638,
|
| 159 |
"num_tokens": 657261.0,
|
| 160 |
"step": 140
|
| 161 |
+
},
|
| 162 |
+
{
|
| 163 |
+
"entropy": 1.103185237944126,
|
| 164 |
+
"epoch": 0.032,
|
| 165 |
+
"grad_norm": 0.25304746627807617,
|
| 166 |
+
"learning_rate": 4.966666666666667e-05,
|
| 167 |
+
"loss": 1.1829004287719727,
|
| 168 |
+
"mean_token_accuracy": 0.7423724889755249,
|
| 169 |
+
"num_tokens": 704749.0,
|
| 170 |
+
"step": 150
|
| 171 |
}
|
| 172 |
],
|
| 173 |
"logging_steps": 10,
|
|
|
|
| 187 |
"attributes": {}
|
| 188 |
}
|
| 189 |
},
|
| 190 |
+
"total_flos": 3367482990403584.0,
|
| 191 |
"train_batch_size": 4,
|
| 192 |
"trial_name": null,
|
| 193 |
"trial_params": null
|