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--- |
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base_model: meta-llama/Meta-Llama-3.1-8B-Instruct |
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datasets: |
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- GaetanMichelet/chat-60_ft_task-1 |
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library_name: peft |
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license: llama3.1 |
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tags: |
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- alignment-handbook |
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- trl |
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- sft |
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- generated_from_trainer |
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model-index: |
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- name: Llama-31-8B_task-1_60-samples_config-4_full |
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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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# Llama-31-8B_task-1_60-samples_config-4_full |
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co./meta-llama/Meta-Llama-3.1-8B-Instruct) on the GaetanMichelet/chat-60_ft_task-1 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9355 |
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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: 1e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 16 |
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- total_train_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_ratio: 0.1 |
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- num_epochs: 150 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:--------:|:----:|:---------------:| |
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| 2.5391 | 0.6957 | 2 | 2.4168 | |
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| 2.5182 | 1.7391 | 5 | 2.4065 | |
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| 2.4879 | 2.7826 | 8 | 2.3913 | |
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| 2.4947 | 3.8261 | 11 | 2.3720 | |
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| 2.4335 | 4.8696 | 14 | 2.3479 | |
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| 2.4424 | 5.9130 | 17 | 2.3109 | |
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| 2.3698 | 6.9565 | 20 | 2.2672 | |
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| 2.3512 | 8.0 | 23 | 2.2129 | |
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| 2.32 | 8.6957 | 25 | 2.1830 | |
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| 2.2555 | 9.7391 | 28 | 2.1266 | |
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| 2.1681 | 10.7826 | 31 | 2.0537 | |
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| 2.0737 | 11.8261 | 34 | 1.9880 | |
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| 2.0403 | 12.8696 | 37 | 1.9277 | |
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| 1.9476 | 13.9130 | 40 | 1.8711 | |
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| 1.9204 | 14.9565 | 43 | 1.8155 | |
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| 1.8461 | 16.0 | 46 | 1.7615 | |
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| 1.8095 | 16.6957 | 48 | 1.7236 | |
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| 1.7597 | 17.7391 | 51 | 1.6580 | |
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| 1.6484 | 18.7826 | 54 | 1.5919 | |
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| 1.6443 | 19.8261 | 57 | 1.5262 | |
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| 1.5204 | 20.8696 | 60 | 1.4561 | |
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| 1.463 | 21.9130 | 63 | 1.3960 | |
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| 1.3833 | 22.9565 | 66 | 1.3404 | |
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| 1.3385 | 24.0 | 69 | 1.2875 | |
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| 1.3094 | 24.6957 | 71 | 1.2504 | |
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| 1.2303 | 25.7391 | 74 | 1.2007 | |
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| 1.1677 | 26.7826 | 77 | 1.1600 | |
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| 1.1674 | 27.8261 | 80 | 1.1332 | |
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| 1.1068 | 28.8696 | 83 | 1.1100 | |
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| 1.104 | 29.9130 | 86 | 1.0884 | |
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| 1.0617 | 30.9565 | 89 | 1.0717 | |
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| 1.0354 | 32.0 | 92 | 1.0577 | |
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| 1.0195 | 32.6957 | 94 | 1.0499 | |
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| 1.0659 | 33.7391 | 97 | 1.0396 | |
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| 1.0118 | 34.7826 | 100 | 1.0310 | |
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| 1.0009 | 35.8261 | 103 | 1.0247 | |
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| 0.9938 | 36.8696 | 106 | 1.0181 | |
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| 0.9736 | 37.9130 | 109 | 1.0124 | |
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| 0.9888 | 38.9565 | 112 | 1.0076 | |
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| 0.9637 | 40.0 | 115 | 1.0019 | |
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| 0.9769 | 40.6957 | 117 | 0.9987 | |
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| 0.936 | 41.7391 | 120 | 0.9939 | |
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| 0.9863 | 42.7826 | 123 | 0.9906 | |
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| 0.9626 | 43.8261 | 126 | 0.9863 | |
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| 0.9438 | 44.8696 | 129 | 0.9825 | |
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| 0.9034 | 45.9130 | 132 | 0.9804 | |
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| 0.9398 | 46.9565 | 135 | 0.9763 | |
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| 0.9206 | 48.0 | 138 | 0.9740 | |
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| 0.9251 | 48.6957 | 140 | 0.9728 | |
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| 0.9245 | 49.7391 | 143 | 0.9704 | |
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| 0.9332 | 50.7826 | 146 | 0.9671 | |
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| 0.9012 | 51.8261 | 149 | 0.9651 | |
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| 0.9075 | 52.8696 | 152 | 0.9627 | |
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| 0.9031 | 53.9130 | 155 | 0.9614 | |
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| 0.8969 | 54.9565 | 158 | 0.9592 | |
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| 0.9102 | 56.0 | 161 | 0.9583 | |
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| 0.8955 | 56.6957 | 163 | 0.9563 | |
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| 0.8775 | 57.7391 | 166 | 0.9547 | |
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| 0.8879 | 58.7826 | 169 | 0.9540 | |
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| 0.8805 | 59.8261 | 172 | 0.9510 | |
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| 0.8982 | 60.8696 | 175 | 0.9505 | |
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| 0.8897 | 61.9130 | 178 | 0.9494 | |
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| 0.8515 | 62.9565 | 181 | 0.9479 | |
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| 0.8637 | 64.0 | 184 | 0.9469 | |
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| 0.8719 | 64.6957 | 186 | 0.9471 | |
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| 0.8635 | 65.7391 | 189 | 0.9452 | |
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| 0.8579 | 66.7826 | 192 | 0.9445 | |
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| 0.8465 | 67.8261 | 195 | 0.9434 | |
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| 0.8588 | 68.8696 | 198 | 0.9436 | |
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| 0.868 | 69.9130 | 201 | 0.9421 | |
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| 0.8523 | 70.9565 | 204 | 0.9418 | |
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| 0.8654 | 72.0 | 207 | 0.9404 | |
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| 0.8525 | 72.6957 | 209 | 0.9405 | |
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| 0.8565 | 73.7391 | 212 | 0.9400 | |
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| 0.8424 | 74.7826 | 215 | 0.9407 | |
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| 0.8342 | 75.8261 | 218 | 0.9395 | |
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| 0.8539 | 76.8696 | 221 | 0.9393 | |
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| 0.8413 | 77.9130 | 224 | 0.9383 | |
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| 0.8488 | 78.9565 | 227 | 0.9382 | |
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| 0.8319 | 80.0 | 230 | 0.9395 | |
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| 0.8402 | 80.6957 | 232 | 0.9382 | |
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| 0.8604 | 81.7391 | 235 | 0.9376 | |
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| 0.8516 | 82.7826 | 238 | 0.9374 | |
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| 0.8195 | 83.8261 | 241 | 0.9378 | |
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| 0.8456 | 84.8696 | 244 | 0.9381 | |
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| 0.8313 | 85.9130 | 247 | 0.9374 | |
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| 0.8415 | 86.9565 | 250 | 0.9369 | |
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| 0.8318 | 88.0 | 253 | 0.9365 | |
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| 0.8271 | 88.6957 | 255 | 0.9370 | |
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| 0.8361 | 89.7391 | 258 | 0.9364 | |
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| 0.8216 | 90.7826 | 261 | 0.9365 | |
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| 0.8387 | 91.8261 | 264 | 0.9366 | |
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| 0.8457 | 92.8696 | 267 | 0.9366 | |
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| 0.8491 | 93.9130 | 270 | 0.9367 | |
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| 0.8171 | 94.9565 | 273 | 0.9357 | |
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| 0.8168 | 96.0 | 276 | 0.9367 | |
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| 0.8161 | 96.6957 | 278 | 0.9364 | |
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| 0.8442 | 97.7391 | 281 | 0.9356 | |
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| 0.8388 | 98.7826 | 284 | 0.9363 | |
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| 0.8365 | 99.8261 | 287 | 0.9355 | |
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| 0.8493 | 100.8696 | 290 | 0.9360 | |
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| 0.8267 | 101.9130 | 293 | 0.9355 | |
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| 0.8304 | 102.9565 | 296 | 0.9361 | |
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| 0.8216 | 104.0 | 299 | 0.9361 | |
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| 0.8436 | 104.3478 | 300 | 0.9358 | |
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### Framework versions |
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- PEFT 0.12.0 |
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- Transformers 4.44.0 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
