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 1140, 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:e421356a08ab52e140026da65d5861223082140ba24ee78b3fe86949d0ea4236
|
| 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 sha256:7ebc9a1960eca13fadd941b073064cf7560b80d545e6159964841e0fad286a4c
|
| 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 sha256:2e85b5465fe52973a7bd51b7c785181b8b382ea41c86dc79168d0eca8972a904
|
| 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 sha256:9e58f741bd504af13f9a07922da1601be73c5c9e500ed2ea57a5d3024618328c
|
| 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,
|
|
@@ -1148,6 +1148,16 @@
|
|
| 1148 |
"mean_token_accuracy": 0.7670834749937058,
|
| 1149 |
"num_tokens": 5257491.0,
|
| 1150 |
"step": 1130
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1151 |
}
|
| 1152 |
],
|
| 1153 |
"logging_steps": 10,
|
|
@@ -1167,7 +1177,7 @@
|
|
| 1167 |
"attributes": {}
|
| 1168 |
}
|
| 1169 |
},
|
| 1170 |
-
"total_flos": 2.
|
| 1171 |
"train_batch_size": 4,
|
| 1172 |
"trial_name": null,
|
| 1173 |
"trial_params": null
|
|
|
|
| 2 |
"best_global_step": null,
|
| 3 |
"best_metric": null,
|
| 4 |
"best_model_checkpoint": null,
|
| 5 |
+
"epoch": 0.2432,
|
| 6 |
"eval_steps": 500,
|
| 7 |
+
"global_step": 1140,
|
| 8 |
"is_hyper_param_search": false,
|
| 9 |
"is_local_process_zero": true,
|
| 10 |
"is_world_process_zero": true,
|
|
|
|
| 1148 |
"mean_token_accuracy": 0.7670834749937058,
|
| 1149 |
"num_tokens": 5257491.0,
|
| 1150 |
"step": 1130
|
| 1151 |
+
},
|
| 1152 |
+
{
|
| 1153 |
+
"entropy": 1.0790555529296397,
|
| 1154 |
+
"epoch": 0.2432,
|
| 1155 |
+
"grad_norm": 0.2658112645149231,
|
| 1156 |
+
"learning_rate": 9.790627239537144e-05,
|
| 1157 |
+
"loss": 1.2411640167236329,
|
| 1158 |
+
"mean_token_accuracy": 0.7390920028090477,
|
| 1159 |
+
"num_tokens": 5302986.0,
|
| 1160 |
+
"step": 1140
|
| 1161 |
}
|
| 1162 |
],
|
| 1163 |
"logging_steps": 10,
|
|
|
|
| 1177 |
"attributes": {}
|
| 1178 |
}
|
| 1179 |
},
|
| 1180 |
+
"total_flos": 2.50703427553536e+16,
|
| 1181 |
"train_batch_size": 4,
|
| 1182 |
"trial_name": null,
|
| 1183 |
"trial_params": null
|