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vicuna-robust-m5-sft-lora
justinwangx/vicuna-robust-m5-sft-lora
vicuna-robust-m5-sft-lora is a machine learning model from justinwangx. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
should probably proofread and complete it, then remove this comment. --
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Public
Updated Dec 21, 2023
Repo size
135 MB
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.safetensors134 MB · 98%
At a glance
- Access
- Public
- Created
- Dec 21, 2023
- Updated
- Dec 21, 2023
- SHA
- fac72054
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. -->
vicuna-robust-m5-sft-lora
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.2993
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: 3e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 128
- total_train_batch_size: 2048
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.3818 | 0.65 | 7 | 1.3745 |
| 1.3733 | 1.64 | 14 | 1.3538 |
| 1.3415 | 2.63 | 21 | 1.3307 |
| 1.3278 | 3.62 | 28 | 1.3120 |
| 1.3039 | 4.61 | 35 | 1.2992 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0a0+32f93b1
- Datasets 2.14.6
- Tokenizers 0.14.1
- Created
- Dec 21, 2023
- Updated
- Dec 21, 2023