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 ·
deec9a7
1
Parent(s): 6f15860
Training in progress, step 2000
Browse files- adapter_model.bin +1 -1
- {checkpoint-1600 → checkpoint-1900/adapter_model}/README.md +0 -0
- {checkpoint-1600 → checkpoint-1900/adapter_model}/adapter_config.json +0 -0
- {checkpoint-1600 → checkpoint-1900/adapter_model}/adapter_model.bin +1 -1
- {checkpoint-1600/adapter_model → checkpoint-2000}/README.md +0 -0
- {checkpoint-1600/adapter_model → checkpoint-2000}/adapter_config.json +0 -0
- {checkpoint-1600/adapter_model → checkpoint-2000}/adapter_model.bin +1 -1
- {checkpoint-1600 → checkpoint-2000}/optimizer.pt +1 -1
- {checkpoint-1600 → checkpoint-2000}/rng_state_0.pth +1 -1
- {checkpoint-1600 → checkpoint-2000}/rng_state_1.pth +1 -1
- {checkpoint-1600 → checkpoint-2000}/rng_state_10.pth +1 -1
- {checkpoint-1600 → checkpoint-2000}/rng_state_11.pth +1 -1
- {checkpoint-1600 → checkpoint-2000}/rng_state_12.pth +1 -1
- {checkpoint-1600 → checkpoint-2000}/rng_state_13.pth +1 -1
- {checkpoint-1600 → checkpoint-2000}/rng_state_2.pth +1 -1
- {checkpoint-1600 → checkpoint-2000}/rng_state_3.pth +1 -1
- {checkpoint-1600 → checkpoint-2000}/rng_state_4.pth +1 -1
- {checkpoint-1600 → checkpoint-2000}/rng_state_5.pth +1 -1
- {checkpoint-1600 → checkpoint-2000}/rng_state_6.pth +1 -1
- {checkpoint-1600 → checkpoint-2000}/rng_state_7.pth +1 -1
- {checkpoint-1600 → checkpoint-2000}/rng_state_8.pth +1 -1
- {checkpoint-1600 → checkpoint-2000}/rng_state_9.pth +1 -1
- {checkpoint-1600 → checkpoint-2000}/scheduler.pt +1 -1
- {checkpoint-1600 → checkpoint-2000}/trainer_state.json +107 -3
- {checkpoint-1600 → checkpoint-2000}/training_args.bin +0 -0
adapter_model.bin
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"loss": 0.8442,
|
| 466 |
+
"step": 1975
|
| 467 |
+
},
|
| 468 |
+
{
|
| 469 |
+
"epoch": 0.78,
|
| 470 |
+
"learning_rate": 0.00016901637153785885,
|
| 471 |
+
"loss": 0.8399,
|
| 472 |
+
"step": 2000
|
| 473 |
+
},
|
| 474 |
+
{
|
| 475 |
+
"epoch": 0.78,
|
| 476 |
+
"eval_loss": 0.8339959383010864,
|
| 477 |
+
"eval_runtime": 58.5829,
|
| 478 |
+
"eval_samples_per_second": 12.461,
|
| 479 |
+
"eval_steps_per_second": 0.905,
|
| 480 |
+
"step": 2000
|
| 481 |
}
|
| 482 |
],
|
| 483 |
"max_steps": 7737,
|
| 484 |
"num_train_epochs": 3,
|
| 485 |
+
"total_flos": 8.614008003953164e+18,
|
| 486 |
"trial_name": null,
|
| 487 |
"trial_params": null
|
| 488 |
}
|
{checkpoint-1600 → checkpoint-2000}/training_args.bin
RENAMED
|
File without changes
|