File size: 1,982 Bytes
784ba7e 9fb2633 784ba7e 9fb2633 784ba7e 9fb2633 784ba7e 9fb2633 784ba7e 9fb2633 784ba7e 9fb2633 784ba7e 9fb2633 784ba7e 9fb2633 784ba7e 9fb2633 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
---
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 |