Instructions to use shibajustfor/97e6c917-e820-40ce-af2e-b33a1b676b69 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/97e6c917-e820-40ce-af2e-b33a1b676b69 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1_5") model = PeftModel.from_pretrained(base_model, "shibajustfor/97e6c917-e820-40ce-af2e-b33a1b676b69") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 50, 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 28348936
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6848e5c3ea4f35c9df12864e594f1f5b936b65625e5ef52dee5b5ce5b62f602d
|
| 3 |
size 28348936
|
last-checkpoint/optimizer.pt
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 14714068
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:70ee5ef0f5768aa9bd93c29d10722f458c38da44a31ec8f1974bfeb4913905dd
|
| 3 |
size 14714068
|
last-checkpoint/rng_state.pth
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 14244
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:04bedbe5267f09e4e5d77875713d66b9ec32274917ea21af9ab2f163db830207
|
| 3 |
size 14244
|
last-checkpoint/scheduler.pt
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 1064
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9b80fcc7599efca0c6313d990c467c2eb3001742b23ddaadc22e3499c12cea79
|
| 3 |
size 1064
|
last-checkpoint/trainer_state.json
CHANGED
|
@@ -1,9 +1,9 @@
|
|
| 1 |
{
|
| 2 |
"best_metric": null,
|
| 3 |
"best_model_checkpoint": null,
|
| 4 |
-
"epoch": 0.
|
| 5 |
"eval_steps": 13,
|
| 6 |
-
"global_step":
|
| 7 |
"is_hyper_param_search": false,
|
| 8 |
"is_local_process_zero": true,
|
| 9 |
"is_world_process_zero": true,
|
|
@@ -60,6 +60,20 @@
|
|
| 60 |
"eval_samples_per_second": 62.753,
|
| 61 |
"eval_steps_per_second": 31.377,
|
| 62 |
"step": 39
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 63 |
}
|
| 64 |
],
|
| 65 |
"logging_steps": 10,
|
|
@@ -74,12 +88,12 @@
|
|
| 74 |
"should_evaluate": false,
|
| 75 |
"should_log": false,
|
| 76 |
"should_save": true,
|
| 77 |
-
"should_training_stop":
|
| 78 |
},
|
| 79 |
"attributes": {}
|
| 80 |
}
|
| 81 |
},
|
| 82 |
-
"total_flos":
|
| 83 |
"train_batch_size": 2,
|
| 84 |
"trial_name": null,
|
| 85 |
"trial_params": null
|
|
|
|
| 1 |
{
|
| 2 |
"best_metric": null,
|
| 3 |
"best_model_checkpoint": null,
|
| 4 |
+
"epoch": 0.0016209294409414358,
|
| 5 |
"eval_steps": 13,
|
| 6 |
+
"global_step": 50,
|
| 7 |
"is_hyper_param_search": false,
|
| 8 |
"is_local_process_zero": true,
|
| 9 |
"is_world_process_zero": true,
|
|
|
|
| 60 |
"eval_samples_per_second": 62.753,
|
| 61 |
"eval_steps_per_second": 31.377,
|
| 62 |
"step": 39
|
| 63 |
+
},
|
| 64 |
+
{
|
| 65 |
+
"epoch": 0.0012967435527531487,
|
| 66 |
+
"grad_norm": 0.8452262878417969,
|
| 67 |
+
"learning_rate": 0.0002,
|
| 68 |
+
"loss": 1.5871,
|
| 69 |
+
"step": 40
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"epoch": 0.0016209294409414358,
|
| 73 |
+
"grad_norm": 0.884437620639801,
|
| 74 |
+
"learning_rate": 0.0002,
|
| 75 |
+
"loss": 1.7649,
|
| 76 |
+
"step": 50
|
| 77 |
}
|
| 78 |
],
|
| 79 |
"logging_steps": 10,
|
|
|
|
| 88 |
"should_evaluate": false,
|
| 89 |
"should_log": false,
|
| 90 |
"should_save": true,
|
| 91 |
+
"should_training_stop": true
|
| 92 |
},
|
| 93 |
"attributes": {}
|
| 94 |
}
|
| 95 |
},
|
| 96 |
+
"total_flos": 1906577736990720.0,
|
| 97 |
"train_batch_size": 2,
|
| 98 |
"trial_name": null,
|
| 99 |
"trial_params": null
|