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 1970, 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:dd804cdd81770152b3d7177dff6d991c1421b876c0e1dd1f85de893bc31b3815
|
| 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 sha256:5dcbdfedc6733d5976b7dcfb074961ff1cf954506d4c206597b7fc67a84d75f2
|
| 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:cd865cb8212842e7dbc68c1c29d3d05cec68d0c1e280a37c46234002777acbe2
|
| 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 sha256:32bb471a796601b246ca30ed6a8dcc726b36b2b6891d11b7f34267b43df43a3c
|
| 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,
|
|
@@ -1978,6 +1978,16 @@
|
|
| 1978 |
"mean_token_accuracy": 0.7426734983921051,
|
| 1979 |
"num_tokens": 9088894.0,
|
| 1980 |
"step": 1960
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1981 |
}
|
| 1982 |
],
|
| 1983 |
"logging_steps": 10,
|
|
@@ -1997,7 +2007,7 @@
|
|
| 1997 |
"attributes": {}
|
| 1998 |
}
|
| 1999 |
},
|
| 2000 |
-
"total_flos": 4.
|
| 2001 |
"train_batch_size": 4,
|
| 2002 |
"trial_name": null,
|
| 2003 |
"trial_params": null
|
|
|
|
| 2 |
"best_global_step": null,
|
| 3 |
"best_metric": null,
|
| 4 |
"best_model_checkpoint": null,
|
| 5 |
+
"epoch": 0.4202666666666667,
|
| 6 |
"eval_steps": 500,
|
| 7 |
+
"global_step": 1970,
|
| 8 |
"is_hyper_param_search": false,
|
| 9 |
"is_local_process_zero": true,
|
| 10 |
"is_world_process_zero": true,
|
|
|
|
| 1978 |
"mean_token_accuracy": 0.7426734983921051,
|
| 1979 |
"num_tokens": 9088894.0,
|
| 1980 |
"step": 1960
|
| 1981 |
+
},
|
| 1982 |
+
{
|
| 1983 |
+
"entropy": 0.9542628638446331,
|
| 1984 |
+
"epoch": 0.4202666666666667,
|
| 1985 |
+
"grad_norm": 0.2736513614654541,
|
| 1986 |
+
"learning_rate": 9.188570479371387e-05,
|
| 1987 |
+
"loss": 1.14229097366333,
|
| 1988 |
+
"mean_token_accuracy": 0.7659218199551105,
|
| 1989 |
+
"num_tokens": 9133717.0,
|
| 1990 |
+
"step": 1970
|
| 1991 |
}
|
| 1992 |
],
|
| 1993 |
"logging_steps": 10,
|
|
|
|
| 2007 |
"attributes": {}
|
| 2008 |
}
|
| 2009 |
},
|
| 2010 |
+
"total_flos": 4.335691124917248e+16,
|
| 2011 |
"train_batch_size": 4,
|
| 2012 |
"trial_name": null,
|
| 2013 |
"trial_params": null
|