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 2330, 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:e3082fb894dbd61eebf0ce8e1e0178955754520e516c02e6d4de3d6929e7e021
|
| 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 sha256:9a766541713bb5cb997e2d6a26f04a7fb9f56a20c1d508ecbbc0e6180d6addcb
|
| 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:bcdbf33be2e859933ffe1b4d6cf7b3a3cebb6e75037e3ceecd09032fe2f44d39
|
| 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 sha256:5defde01bc6738c098494a3315f8e057dccd48f0861e19102cb32c194b34b83c
|
| 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,
|
|
@@ -2338,6 +2338,16 @@
|
|
| 2338 |
"mean_token_accuracy": 0.7597260788083077,
|
| 2339 |
"num_tokens": 10785226.0,
|
| 2340 |
"step": 2320
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 2341 |
}
|
| 2342 |
],
|
| 2343 |
"logging_steps": 10,
|
|
@@ -2357,7 +2367,7 @@
|
|
| 2357 |
"attributes": {}
|
| 2358 |
}
|
| 2359 |
},
|
| 2360 |
-
"total_flos": 5.
|
| 2361 |
"train_batch_size": 4,
|
| 2362 |
"trial_name": null,
|
| 2363 |
"trial_params": null
|
|
|
|
| 2 |
"best_global_step": null,
|
| 3 |
"best_metric": null,
|
| 4 |
"best_model_checkpoint": null,
|
| 5 |
+
"epoch": 0.49706666666666666,
|
| 6 |
"eval_steps": 500,
|
| 7 |
+
"global_step": 2330,
|
| 8 |
"is_hyper_param_search": false,
|
| 9 |
"is_local_process_zero": true,
|
| 10 |
"is_world_process_zero": true,
|
|
|
|
| 2338 |
"mean_token_accuracy": 0.7597260788083077,
|
| 2339 |
"num_tokens": 10785226.0,
|
| 2340 |
"step": 2320
|
| 2341 |
+
},
|
| 2342 |
+
{
|
| 2343 |
+
"entropy": 0.8475333206355572,
|
| 2344 |
+
"epoch": 0.49706666666666666,
|
| 2345 |
+
"grad_norm": 0.30574989318847656,
|
| 2346 |
+
"learning_rate": 8.816715208500922e-05,
|
| 2347 |
+
"loss": 0.9416275978088379,
|
| 2348 |
+
"mean_token_accuracy": 0.7838068321347237,
|
| 2349 |
+
"num_tokens": 10830340.0,
|
| 2350 |
+
"step": 2330
|
| 2351 |
}
|
| 2352 |
],
|
| 2353 |
"logging_steps": 10,
|
|
|
|
| 2367 |
"attributes": {}
|
| 2368 |
}
|
| 2369 |
},
|
| 2370 |
+
"total_flos": 5.134250831680512e+16,
|
| 2371 |
"train_batch_size": 4,
|
| 2372 |
"trial_name": null,
|
| 2373 |
"trial_params": null
|