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---
library_name: peft
license: gemma
base_model: google/codegemma-7b
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: code-bench-CodeGemma-7B-cgv1-ds
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. -->
# code-bench-CodeGemma-7B-cgv1-ds
This model is a fine-tuned version of [google/codegemma-7b](https://huggingface.co/google/codegemma-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0947
## 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 3
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9203 | 0.0530 | 50 | 1.0306 |
| 0.551 | 0.1061 | 100 | 0.5383 |
| 0.4483 | 0.1591 | 150 | 0.4048 |
| 0.3469 | 0.2121 | 200 | 0.3013 |
| 0.2868 | 0.2652 | 250 | 0.2447 |
| 0.2307 | 0.3182 | 300 | 0.2061 |
| 0.1972 | 0.3713 | 350 | 0.1727 |
| 0.1716 | 0.4243 | 400 | 0.1525 |
| 0.1612 | 0.4773 | 450 | 0.1468 |
| 0.1631 | 0.5304 | 500 | 0.1400 |
| 0.1739 | 0.5834 | 550 | 0.1376 |
| 0.148 | 0.6364 | 600 | 0.1330 |
| 0.1413 | 0.6895 | 650 | 0.1274 |
| 0.1464 | 0.7425 | 700 | 0.1267 |
| 0.1376 | 0.7955 | 750 | 0.1240 |
| 0.1287 | 0.8486 | 800 | 0.1210 |
| 0.1402 | 0.9016 | 850 | 0.1198 |
| 0.1261 | 0.9547 | 900 | 0.1173 |
| 0.1195 | 1.0077 | 950 | 0.1145 |
| 0.1254 | 1.0607 | 1000 | 0.1133 |
| 0.1109 | 1.1138 | 1050 | 0.1119 |
| 0.1206 | 1.1668 | 1100 | 0.1093 |
| 0.1195 | 1.2198 | 1150 | 0.1084 |
| 0.1237 | 1.2729 | 1200 | 0.1073 |
| 0.1205 | 1.3259 | 1250 | 0.1064 |
| 0.1105 | 1.3789 | 1300 | 0.1048 |
| 0.1027 | 1.4320 | 1350 | 0.1038 |
| 0.1128 | 1.4850 | 1400 | 0.1035 |
| 0.1207 | 1.5381 | 1450 | 0.1030 |
| 0.1057 | 1.5911 | 1500 | 0.1013 |
| 0.1056 | 1.6441 | 1550 | 0.0996 |
| 0.1086 | 1.6972 | 1600 | 0.0985 |
| 0.1078 | 1.7502 | 1650 | 0.0982 |
| 0.0987 | 1.8032 | 1700 | 0.0968 |
| 0.1037 | 1.8563 | 1750 | 0.0960 |
| 0.1047 | 1.9093 | 1800 | 0.0957 |
| 0.1045 | 1.9623 | 1850 | 0.0947 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.5.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1 |