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---
base_model: meta-llama/Llama-2-7b-chat-hf
tags:
- generated_from_trainer
model-index:
- name: llama-le-out
results: []
---
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# llama-le-out
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6239
## 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9364 | 0.06 | 100 | 0.8000 |
| 0.809 | 0.12 | 200 | 0.7724 |
| 0.8695 | 0.18 | 300 | 0.7571 |
| 0.7512 | 0.24 | 400 | 0.7406 |
| 0.8266 | 0.3 | 500 | 0.7327 |
| 0.7898 | 0.35 | 600 | 0.7238 |
| 0.9163 | 0.41 | 700 | 0.7135 |
| 0.6955 | 0.47 | 800 | 0.7025 |
| 0.7887 | 0.53 | 900 | 0.7009 |
| 0.7361 | 0.59 | 1000 | 0.6911 |
| 0.7736 | 0.65 | 1100 | 0.6897 |
| 0.7135 | 0.71 | 1200 | 0.6859 |
| 0.8138 | 0.77 | 1300 | 0.6788 |
| 0.7172 | 0.83 | 1400 | 0.6720 |
| 0.7387 | 0.89 | 1500 | 0.6695 |
| 0.7042 | 0.95 | 1600 | 0.6688 |
| 0.7231 | 1.0 | 1700 | 0.6652 |
| 0.7136 | 1.06 | 1800 | 0.6626 |
| 0.694 | 1.12 | 1900 | 0.6583 |
| 0.7401 | 1.18 | 2000 | 0.6551 |
| 0.63 | 1.24 | 2100 | 0.6519 |
| 0.6506 | 1.3 | 2200 | 0.6478 |
| 0.7436 | 1.36 | 2300 | 0.6457 |
| 0.5903 | 1.42 | 2400 | 0.6452 |
| 0.6861 | 1.48 | 2500 | 0.6399 |
| 0.6576 | 1.54 | 2600 | 0.6412 |
| 0.6327 | 1.59 | 2700 | 0.6357 |
| 0.6634 | 1.65 | 2800 | 0.6378 |
| 0.6419 | 1.71 | 2900 | 0.6349 |
| 0.6573 | 1.77 | 3000 | 0.6344 |
| 0.7052 | 1.83 | 3100 | 0.6327 |
| 0.6438 | 1.89 | 3200 | 0.6292 |
| 0.713 | 1.95 | 3300 | 0.6283 |
| 0.6357 | 2.01 | 3400 | 0.6293 |
| 0.5736 | 2.07 | 3500 | 0.6302 |
| 0.591 | 2.13 | 3600 | 0.6307 |
| 0.6995 | 2.19 | 3700 | 0.6295 |
| 0.6708 | 2.24 | 3800 | 0.6277 |
| 0.6329 | 2.3 | 3900 | 0.6262 |
| 0.6138 | 2.36 | 4000 | 0.6271 |
| 0.6316 | 2.42 | 4100 | 0.6266 |
| 0.6022 | 2.48 | 4200 | 0.6260 |
| 0.7221 | 2.54 | 4300 | 0.6252 |
| 0.6943 | 2.6 | 4400 | 0.6256 |
| 0.6616 | 2.66 | 4500 | 0.6246 |
| 0.6185 | 2.72 | 4600 | 0.6247 |
| 0.6417 | 2.78 | 4700 | 0.6239 |
| 0.6238 | 2.84 | 4800 | 0.6237 |
| 0.6024 | 2.89 | 4900 | 0.6236 |
| 0.6059 | 2.95 | 5000 | 0.6239 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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