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 3210, 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:a4608fe69037e476aa864f7ed6f95bd2a736c2c081654228aba09dce283587c2
|
| 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 sha256:8191f78b55b3d9a0bb3fd6543fca84d2db9122c8a5636c8bf5a3b3013ec286b8
|
| 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:aa04534c1113a9f50eecadd2111ba75ec2bed9781b19da4285b5c1ab4d0e7c3e
|
| 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:be70e4db37452226ebb82c1877b1c54bc5ac13da8cc45dd7064c1e97d5607de6
|
| 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,
|
|
@@ -3218,6 +3218,16 @@
|
|
| 3218 |
"mean_token_accuracy": 0.8022488832473755,
|
| 3219 |
"num_tokens": 14891820.0,
|
| 3220 |
"step": 3200
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 3221 |
}
|
| 3222 |
],
|
| 3223 |
"logging_steps": 10,
|
|
@@ -3237,7 +3247,7 @@
|
|
| 3237 |
"attributes": {}
|
| 3238 |
}
|
| 3239 |
},
|
| 3240 |
-
"total_flos": 7.
|
| 3241 |
"train_batch_size": 4,
|
| 3242 |
"trial_name": null,
|
| 3243 |
"trial_params": null
|
|
|
|
| 2 |
"best_global_step": null,
|
| 3 |
"best_metric": null,
|
| 4 |
"best_model_checkpoint": null,
|
| 5 |
+
"epoch": 0.6848,
|
| 6 |
"eval_steps": 500,
|
| 7 |
+
"global_step": 3210,
|
| 8 |
"is_hyper_param_search": false,
|
| 9 |
"is_local_process_zero": true,
|
| 10 |
"is_world_process_zero": true,
|
|
|
|
| 3218 |
"mean_token_accuracy": 0.8022488832473755,
|
| 3219 |
"num_tokens": 14891820.0,
|
| 3220 |
"step": 3200
|
| 3221 |
+
},
|
| 3222 |
+
{
|
| 3223 |
+
"entropy": 1.0069321602582932,
|
| 3224 |
+
"epoch": 0.6848,
|
| 3225 |
+
"grad_norm": 0.27509671449661255,
|
| 3226 |
+
"learning_rate": 7.672291623287766e-05,
|
| 3227 |
+
"loss": 1.1010238647460937,
|
| 3228 |
+
"mean_token_accuracy": 0.754035946726799,
|
| 3229 |
+
"num_tokens": 14942310.0,
|
| 3230 |
+
"step": 3210
|
| 3231 |
}
|
| 3232 |
],
|
| 3233 |
"logging_steps": 10,
|
|
|
|
| 3247 |
"attributes": {}
|
| 3248 |
}
|
| 3249 |
},
|
| 3250 |
+
"total_flos": 7.076161637037158e+16,
|
| 3251 |
"train_batch_size": 4,
|
| 3252 |
"trial_name": null,
|
| 3253 |
"trial_params": null
|