Instructions to use blackbeard334/CodeLlama-7b-Instruct-hf__reverse_engineer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use blackbeard334/CodeLlama-7b-Instruct-hf__reverse_engineer with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("blackbeard334/CodeLlama-7b-Instruct-hf__reverse_engineer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 2990, checkpoint
Browse files
last-checkpoint/optimizer.pt
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 1657
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:65e787b18a45e3fe53d0164a6ba171dce969357f4b20b9cc87a290d0316f7477
|
| 3 |
size 1657
|
last-checkpoint/rng_state.pth
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 14709
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9c0ddeaf0737cef5ec7cd8e12e7460b57df54b3816e592dc6aab5af01bd0c4ef
|
| 3 |
size 14709
|
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:b02ec551fa98d7fbe4e782eccd6f5d9966ff01816171d9296bba211a882ffc45
|
| 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":
|
| 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,
|
|
@@ -5372,6 +5372,24 @@
|
|
| 5372 |
"mean_token_accuracy": 0.6427533149719238,
|
| 5373 |
"num_tokens": 10867773.0,
|
| 5374 |
"step": 2980
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 5375 |
}
|
| 5376 |
],
|
| 5377 |
"logging_steps": 5,
|
|
@@ -5391,7 +5409,7 @@
|
|
| 5391 |
"attributes": {}
|
| 5392 |
}
|
| 5393 |
},
|
| 5394 |
-
"total_flos": 5.
|
| 5395 |
"train_batch_size": 4,
|
| 5396 |
"trial_name": null,
|
| 5397 |
"trial_params": null
|
|
|
|
| 2 |
"best_global_step": null,
|
| 3 |
"best_metric": null,
|
| 4 |
"best_model_checkpoint": null,
|
| 5 |
+
"epoch": 23.0,
|
| 6 |
"eval_steps": 500,
|
| 7 |
+
"global_step": 2990,
|
| 8 |
"is_hyper_param_search": false,
|
| 9 |
"is_local_process_zero": true,
|
| 10 |
"is_world_process_zero": true,
|
|
|
|
| 5372 |
"mean_token_accuracy": 0.6427533149719238,
|
| 5373 |
"num_tokens": 10867773.0,
|
| 5374 |
"step": 2980
|
| 5375 |
+
},
|
| 5376 |
+
{
|
| 5377 |
+
"epoch": 22.96153846153846,
|
| 5378 |
+
"grad_norm": 0.0,
|
| 5379 |
+
"learning_rate": 3.508e-05,
|
| 5380 |
+
"loss": 1.7629,
|
| 5381 |
+
"mean_token_accuracy": 0.6471167922019958,
|
| 5382 |
+
"num_tokens": 10885829.0,
|
| 5383 |
+
"step": 2985
|
| 5384 |
+
},
|
| 5385 |
+
{
|
| 5386 |
+
"epoch": 23.0,
|
| 5387 |
+
"grad_norm": 0.0,
|
| 5388 |
+
"learning_rate": 3.5055e-05,
|
| 5389 |
+
"loss": 1.7349,
|
| 5390 |
+
"mean_token_accuracy": 0.65148167014122,
|
| 5391 |
+
"num_tokens": 10904415.0,
|
| 5392 |
+
"step": 2990
|
| 5393 |
}
|
| 5394 |
],
|
| 5395 |
"logging_steps": 5,
|
|
|
|
| 5409 |
"attributes": {}
|
| 5410 |
}
|
| 5411 |
},
|
| 5412 |
+
"total_flos": 5.525887420145664e+17,
|
| 5413 |
"train_batch_size": 4,
|
| 5414 |
"trial_name": null,
|
| 5415 |
"trial_params": null
|