Downloads · 30 days
11
31% of all-time downloads
llama3-8b-instruct-qlora-large
nrishabh/llama3-8b-instruct-qlora-large
llama3-8b-instruct-qlora-large is a machine learning model from nrishabh. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
All-time downloads
36
Public
Repo size
436 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors218 MB · 96%
At a glance
- Library
- peft
- License
- mit
- Access
- Public
- Created
- May 13, 2024
- Updated
- May 13, 2024
- SHA
- de0f7bdf
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
llama3-8b-instruct-qlora-large
This model is a fine-tuned version of LoftQ/Meta-Llama-3-8B-Instruct-4bit-64rank on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8530
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.3454 | 1.0 | 158 | 1.2439 |
| 2.1288 | 2.0 | 316 | 1.0900 |
| 2.0335 | 3.0 | 474 | 1.0394 |
| 1.9315 | 4.0 | 632 | 0.9995 |
| 1.804 | 5.0 | 790 | 0.9605 |
| 1.6583 | 6.0 | 948 | 0.9411 |
| 1.4994 | 7.0 | 1106 | 0.9283 |
| 1.3388 | 8.0 | 1264 | 0.9158 |
| 1.1894 | 9.0 | 1422 | 0.9103 |
| 1.0616 | 10.0 | 1580 | 0.9027 |
| 0.9461 | 11.0 | 1738 | 0.8963 |
| 0.8447 | 12.0 | 1896 | 0.8922 |
| 0.7575 | 13.0 | 2054 | 0.8887 |
| 0.6817 | 14.0 | 2212 | 0.8803 |
| 0.6192 | 15.0 | 2370 | 0.8761 |
| 0.5669 | 16.0 | 2528 | 0.8715 |
| 0.5196 | 17.0 | 2686 | 0.8719 |
| 0.479 | 18.0 | 2844 | 0.8683 |
| 0.4473 | 19.0 | 3002 | 0.8662 |
| 0.4202 | 20.0 | 3160 | 0.8624 |
| 0.397 | 21.0 | 3318 | 0.8590 |
| 0.377 | 22.0 | 3476 | 0.8573 |
| 0.3622 | 23.0 | 3634 | 0.8558 |
| 0.3514 | 24.0 | 3792 | 0.8548 |
| 0.3434 | 25.0 | 3950 | 0.8543 |
| 0.3349 | 26.0 | 4108 | 0.8541 |
| 0.332 | 27.0 | 4266 | 0.8538 |
| 0.328 | 28.0 | 4424 | 0.8541 |
| 0.3286 | 29.0 | 4582 | 0.8532 |
| 0.3279 | 30.0 | 4740 | 0.8530 |
Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
- Library
- peft
- License
- mit
- Created
- May 13, 2024
- Updated
- May 13, 2024