Instructions to use nadejdatarabukina/091b0083-b042-4c61-ad58-be0042fefdbf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nadejdatarabukina/091b0083-b042-4c61-ad58-be0042fefdbf with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Mistral-Nemo-Instruct-2407") model = PeftModel.from_pretrained(base_model, "nadejdatarabukina/091b0083-b042-4c61-ad58-be0042fefdbf") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 30, 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 456603682
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4c6e0a255824653706628b9c668bd617f0387d3fbb3db134b83af615c372526a
|
| 3 |
size 456603682
|
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 sha256:27dda49e3c662d8ae35bbed5c7910e852710db95858fa8dba4b98a5d1c00f698
|
| 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 sha256:2def2cd24154d8cecbaa07c36ae27e5ebb9b7273a78abfea27aa67c480e4ae2b
|
| 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": 8,
|
| 6 |
-
"global_step":
|
| 7 |
"is_hyper_param_search": false,
|
| 8 |
"is_local_process_zero": true,
|
| 9 |
"is_world_process_zero": true,
|
|
@@ -95,6 +95,20 @@
|
|
| 95 |
"eval_samples_per_second": 5.84,
|
| 96 |
"eval_steps_per_second": 2.943,
|
| 97 |
"step": 24
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 98 |
}
|
| 99 |
],
|
| 100 |
"logging_steps": 3,
|
|
@@ -109,12 +123,12 @@
|
|
| 109 |
"should_evaluate": false,
|
| 110 |
"should_log": false,
|
| 111 |
"should_save": true,
|
| 112 |
-
"should_training_stop":
|
| 113 |
},
|
| 114 |
"attributes": {}
|
| 115 |
}
|
| 116 |
},
|
| 117 |
-
"total_flos": 1.
|
| 118 |
"train_batch_size": 2,
|
| 119 |
"trial_name": null,
|
| 120 |
"trial_params": null
|
|
|
|
| 1 |
{
|
| 2 |
"best_metric": null,
|
| 3 |
"best_model_checkpoint": null,
|
| 4 |
+
"epoch": 0.10101010101010101,
|
| 5 |
"eval_steps": 8,
|
| 6 |
+
"global_step": 30,
|
| 7 |
"is_hyper_param_search": false,
|
| 8 |
"is_local_process_zero": true,
|
| 9 |
"is_world_process_zero": true,
|
|
|
|
| 95 |
"eval_samples_per_second": 5.84,
|
| 96 |
"eval_steps_per_second": 2.943,
|
| 97 |
"step": 24
|
| 98 |
+
},
|
| 99 |
+
{
|
| 100 |
+
"epoch": 0.09090909090909091,
|
| 101 |
+
"grad_norm": NaN,
|
| 102 |
+
"learning_rate": 7.022351411174866e-06,
|
| 103 |
+
"loss": 0.0,
|
| 104 |
+
"step": 27
|
| 105 |
+
},
|
| 106 |
+
{
|
| 107 |
+
"epoch": 0.10101010101010101,
|
| 108 |
+
"grad_norm": NaN,
|
| 109 |
+
"learning_rate": 0.0,
|
| 110 |
+
"loss": 0.0,
|
| 111 |
+
"step": 30
|
| 112 |
}
|
| 113 |
],
|
| 114 |
"logging_steps": 3,
|
|
|
|
| 123 |
"should_evaluate": false,
|
| 124 |
"should_log": false,
|
| 125 |
"should_save": true,
|
| 126 |
+
"should_training_stop": true
|
| 127 |
},
|
| 128 |
"attributes": {}
|
| 129 |
}
|
| 130 |
},
|
| 131 |
+
"total_flos": 1.71546035355648e+16,
|
| 132 |
"train_batch_size": 2,
|
| 133 |
"trial_name": null,
|
| 134 |
"trial_params": null
|