|
|
--- |
|
|
library_name: peft |
|
|
base_model: mtzig/prm800k_llama_debug_full |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
metrics: |
|
|
- accuracy |
|
|
- precision |
|
|
- recall |
|
|
- f1 |
|
|
model-index: |
|
|
- name: v3c_llama_lora |
|
|
results: [] |
|
|
--- |
|
|
|
|
|
<!-- 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. --> |
|
|
|
|
|
# v3c_llama_lora |
|
|
|
|
|
This model is a fine-tuned version of [mtzig/prm800k_llama_debug_full](https://huggingface.co/mtzig/prm800k_llama_debug_full) on an unknown dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 0.4195 |
|
|
- Accuracy: 0.8128 |
|
|
- Precision: 0.7778 |
|
|
- Recall: 0.42 |
|
|
- F1: 0.5455 |
|
|
|
|
|
## 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: 4 |
|
|
- eval_batch_size: 4 |
|
|
- seed: 765837 |
|
|
- distributed_type: multi-GPU |
|
|
- num_devices: 4 |
|
|
- gradient_accumulation_steps: 4 |
|
|
- total_train_batch_size: 64 |
|
|
- total_eval_batch_size: 16 |
|
|
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
|
|
- lr_scheduler_type: cosine |
|
|
- lr_scheduler_warmup_ratio: 0.1 |
|
|
- num_epochs: 1 |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
|
|
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
|
|
| No log | 0 | 0 | 0.6173 | 0.7487 | 1.0 | 0.06 | 0.1132 | |
|
|
| 0.3808 | 0.0492 | 40 | 0.5695 | 0.7487 | 0.8 | 0.08 | 0.1455 | |
|
|
| 0.3036 | 0.0984 | 80 | 0.4816 | 0.7647 | 0.6364 | 0.28 | 0.3889 | |
|
|
| 0.305 | 0.1476 | 120 | 0.4852 | 0.8021 | 0.7241 | 0.42 | 0.5316 | |
|
|
| 0.256 | 0.1967 | 160 | 0.4328 | 0.8021 | 0.7826 | 0.36 | 0.4932 | |
|
|
| 0.2062 | 0.2459 | 200 | 0.4699 | 0.7861 | 0.75 | 0.3 | 0.4286 | |
|
|
| 0.2004 | 0.2951 | 240 | 0.4480 | 0.7807 | 0.7143 | 0.3 | 0.4225 | |
|
|
| 0.2241 | 0.3443 | 280 | 0.4449 | 0.7807 | 0.7143 | 0.3 | 0.4225 | |
|
|
| 0.1505 | 0.3935 | 320 | 0.4088 | 0.8182 | 0.75 | 0.48 | 0.5854 | |
|
|
| 0.1752 | 0.4427 | 360 | 0.4386 | 0.7861 | 0.75 | 0.3 | 0.4286 | |
|
|
| 0.2382 | 0.4919 | 400 | 0.4186 | 0.8128 | 0.7778 | 0.42 | 0.5455 | |
|
|
| 0.238 | 0.5410 | 440 | 0.4313 | 0.7914 | 0.7391 | 0.34 | 0.4658 | |
|
|
| 0.1448 | 0.5902 | 480 | 0.4161 | 0.8128 | 0.7778 | 0.42 | 0.5455 | |
|
|
| 0.2096 | 0.6394 | 520 | 0.4251 | 0.7968 | 0.75 | 0.36 | 0.4865 | |
|
|
| 0.204 | 0.6886 | 560 | 0.4413 | 0.7914 | 0.7391 | 0.34 | 0.4658 | |
|
|
| 0.1545 | 0.7378 | 600 | 0.4312 | 0.7968 | 0.75 | 0.36 | 0.4865 | |
|
|
| 0.1883 | 0.7870 | 640 | 0.4288 | 0.8021 | 0.76 | 0.38 | 0.5067 | |
|
|
| 0.2403 | 0.8362 | 680 | 0.4288 | 0.8021 | 0.76 | 0.38 | 0.5067 | |
|
|
| 0.1937 | 0.8853 | 720 | 0.4245 | 0.8021 | 0.76 | 0.38 | 0.5067 | |
|
|
| 0.164 | 0.9345 | 760 | 0.4182 | 0.8075 | 0.7692 | 0.4 | 0.5263 | |
|
|
| 0.2185 | 0.9837 | 800 | 0.4195 | 0.8128 | 0.7778 | 0.42 | 0.5455 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- PEFT 0.13.2 |
|
|
- Transformers 4.46.3 |
|
|
- Pytorch 2.5.1+cu124 |
|
|
- Datasets 3.1.0 |
|
|
- Tokenizers 0.20.3 |
