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--- |
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base_model: meta-llama/Llama-2-7b-chat-hf |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: llama-le-out |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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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) |
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# llama-le-out |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6239 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- total_eval_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 10 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.9364 | 0.06 | 100 | 0.8000 | |
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| 0.809 | 0.12 | 200 | 0.7724 | |
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| 0.8695 | 0.18 | 300 | 0.7571 | |
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| 0.7512 | 0.24 | 400 | 0.7406 | |
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| 0.8266 | 0.3 | 500 | 0.7327 | |
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| 0.7898 | 0.35 | 600 | 0.7238 | |
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| 0.9163 | 0.41 | 700 | 0.7135 | |
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| 0.6955 | 0.47 | 800 | 0.7025 | |
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| 0.7887 | 0.53 | 900 | 0.7009 | |
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| 0.7361 | 0.59 | 1000 | 0.6911 | |
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| 0.7736 | 0.65 | 1100 | 0.6897 | |
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| 0.7135 | 0.71 | 1200 | 0.6859 | |
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| 0.8138 | 0.77 | 1300 | 0.6788 | |
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| 0.7172 | 0.83 | 1400 | 0.6720 | |
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| 0.7387 | 0.89 | 1500 | 0.6695 | |
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| 0.7042 | 0.95 | 1600 | 0.6688 | |
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| 0.7231 | 1.0 | 1700 | 0.6652 | |
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| 0.7136 | 1.06 | 1800 | 0.6626 | |
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| 0.694 | 1.12 | 1900 | 0.6583 | |
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| 0.7401 | 1.18 | 2000 | 0.6551 | |
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| 0.63 | 1.24 | 2100 | 0.6519 | |
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| 0.6506 | 1.3 | 2200 | 0.6478 | |
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| 0.7436 | 1.36 | 2300 | 0.6457 | |
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| 0.5903 | 1.42 | 2400 | 0.6452 | |
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| 0.6861 | 1.48 | 2500 | 0.6399 | |
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| 0.6576 | 1.54 | 2600 | 0.6412 | |
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| 0.6327 | 1.59 | 2700 | 0.6357 | |
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| 0.6634 | 1.65 | 2800 | 0.6378 | |
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| 0.6419 | 1.71 | 2900 | 0.6349 | |
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| 0.6573 | 1.77 | 3000 | 0.6344 | |
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| 0.7052 | 1.83 | 3100 | 0.6327 | |
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| 0.6438 | 1.89 | 3200 | 0.6292 | |
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| 0.713 | 1.95 | 3300 | 0.6283 | |
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| 0.6357 | 2.01 | 3400 | 0.6293 | |
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| 0.5736 | 2.07 | 3500 | 0.6302 | |
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| 0.591 | 2.13 | 3600 | 0.6307 | |
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| 0.6995 | 2.19 | 3700 | 0.6295 | |
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| 0.6708 | 2.24 | 3800 | 0.6277 | |
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| 0.6329 | 2.3 | 3900 | 0.6262 | |
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| 0.6138 | 2.36 | 4000 | 0.6271 | |
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| 0.6316 | 2.42 | 4100 | 0.6266 | |
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| 0.6022 | 2.48 | 4200 | 0.6260 | |
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| 0.7221 | 2.54 | 4300 | 0.6252 | |
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| 0.6943 | 2.6 | 4400 | 0.6256 | |
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| 0.6616 | 2.66 | 4500 | 0.6246 | |
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| 0.6185 | 2.72 | 4600 | 0.6247 | |
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| 0.6417 | 2.78 | 4700 | 0.6239 | |
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| 0.6238 | 2.84 | 4800 | 0.6237 | |
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| 0.6024 | 2.89 | 4900 | 0.6236 | |
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| 0.6059 | 2.95 | 5000 | 0.6239 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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