| --- |
| license: llama2 |
| library_name: peft |
| tags: |
| - generated_from_trainer |
| base_model: codellama/CodeLlama-7b-Instruct-hf |
| model-index: |
| - name: vendata-train |
| 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. --> |
|
|
| # vendata-train |
|
|
| This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.9052 |
|
|
| ## 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: 42 |
| - distributed_type: multi-GPU |
| - num_devices: 2 |
| - gradient_accumulation_steps: 8 |
| - total_train_batch_size: 64 |
| - total_eval_batch_size: 8 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_ratio: 0.1 |
| - training_steps: 100 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | |
| |:-------------:|:-----:|:----:|:---------------:| |
| | 1.0633 | 0.1 | 10 | 1.0362 | |
| | 1.2685 | 0.2 | 20 | 0.9920 | |
| | 1.2542 | 0.3 | 30 | 0.9562 | |
| | 1.1031 | 0.4 | 40 | 0.9356 | |
| | 1.0196 | 0.5 | 50 | 0.9224 | |
| | 0.9397 | 0.6 | 60 | 0.9140 | |
| | 0.9485 | 0.7 | 70 | 0.9091 | |
| | 0.9506 | 0.8 | 80 | 0.9064 | |
| | 0.978 | 0.9 | 90 | 0.9054 | |
| | 1.0167 | 1.0 | 100 | 0.9052 | |
| |
| |
| ### Framework versions |
| |
| - PEFT 0.8.2 |
| - Transformers 4.36.2 |
| - Pytorch 2.1.2 |
| - Datasets 2.16.1 |
| - Tokenizers 0.15.0 |
