Instructions to use ZeroUniqueness/qlora-llama-2-13b-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ZeroUniqueness/qlora-llama-2-13b-code with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/workspace/webui/models/TheBloke_Llama-2-13B-fp16") model = PeftModel.from_pretrained(base_model, "ZeroUniqueness/qlora-llama-2-13b-code") - Notebooks
- Google Colab
- Kaggle
Commit ·
7f3dee6
1
Parent(s): 07a6394
Training in progress, step 2300
Browse files- adapter_model.bin +1 -1
- {checkpoint-1900 → checkpoint-2200/adapter_model}/README.md +0 -0
- {checkpoint-1900 → checkpoint-2200/adapter_model}/adapter_config.json +0 -0
- {checkpoint-1900 → checkpoint-2200/adapter_model}/adapter_model.bin +1 -1
- {checkpoint-1900/adapter_model → checkpoint-2300}/README.md +0 -0
- {checkpoint-1900/adapter_model → checkpoint-2300}/adapter_config.json +0 -0
- {checkpoint-1900/adapter_model → checkpoint-2300}/adapter_model.bin +1 -1
- {checkpoint-1900 → checkpoint-2300}/optimizer.pt +1 -1
- {checkpoint-1900 → checkpoint-2300}/rng_state_0.pth +1 -1
- {checkpoint-1900 → checkpoint-2300}/rng_state_1.pth +1 -1
- {checkpoint-1900 → checkpoint-2300}/rng_state_10.pth +1 -1
- {checkpoint-1900 → checkpoint-2300}/rng_state_11.pth +1 -1
- {checkpoint-1900 → checkpoint-2300}/rng_state_12.pth +1 -1
- {checkpoint-1900 → checkpoint-2300}/rng_state_13.pth +1 -1
- {checkpoint-1900 → checkpoint-2300}/rng_state_2.pth +1 -1
- {checkpoint-1900 → checkpoint-2300}/rng_state_3.pth +1 -1
- {checkpoint-1900 → checkpoint-2300}/rng_state_4.pth +1 -1
- {checkpoint-1900 → checkpoint-2300}/rng_state_5.pth +1 -1
- {checkpoint-1900 → checkpoint-2300}/rng_state_6.pth +1 -1
- {checkpoint-1900 → checkpoint-2300}/rng_state_7.pth +1 -1
- {checkpoint-1900 → checkpoint-2300}/rng_state_8.pth +1 -1
- {checkpoint-1900 → checkpoint-2300}/rng_state_9.pth +1 -1
- {checkpoint-1900 → checkpoint-2300}/scheduler.pt +1 -1
- {checkpoint-1900 → checkpoint-2300}/trainer_state.json +107 -3
- {checkpoint-1900 → checkpoint-2300}/training_args.bin +0 -0
adapter_model.bin
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| 536 |
+
{
|
| 537 |
+
"epoch": 0.87,
|
| 538 |
+
"learning_rate": 0.0001613173661237589,
|
| 539 |
+
"loss": 0.8313,
|
| 540 |
+
"step": 2250
|
| 541 |
+
},
|
| 542 |
+
{
|
| 543 |
+
"epoch": 0.88,
|
| 544 |
+
"learning_rate": 0.0001605112827655069,
|
| 545 |
+
"loss": 0.8292,
|
| 546 |
+
"step": 2275
|
| 547 |
+
},
|
| 548 |
+
{
|
| 549 |
+
"epoch": 0.89,
|
| 550 |
+
"learning_rate": 0.0001596989478162339,
|
| 551 |
+
"loss": 0.8334,
|
| 552 |
+
"step": 2300
|
| 553 |
}
|
| 554 |
],
|
| 555 |
"max_steps": 7737,
|
| 556 |
"num_train_epochs": 3,
|
| 557 |
+
"total_flos": 9.906168731933868e+18,
|
| 558 |
"trial_name": null,
|
| 559 |
"trial_params": null
|
| 560 |
}
|
{checkpoint-1900 → checkpoint-2300}/training_args.bin
RENAMED
|
File without changes
|