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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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- GaetanMichelet/chat-120_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_120-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_120-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 and the GaetanMichelet/chat-120_ft_task-1 datasets. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9042 |
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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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| 2.4687 | 0.9091 | 5 | 2.4589 | |
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| 2.5083 | 2.0 | 11 | 2.4440 | |
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| 2.4676 | 2.9091 | 16 | 2.4218 | |
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| 2.4562 | 4.0 | 22 | 2.3870 | |
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| 2.377 | 4.9091 | 27 | 2.3475 | |
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| 2.3303 | 6.0 | 33 | 2.2793 | |
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| 2.2553 | 6.9091 | 38 | 2.2254 | |
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| 2.174 | 8.0 | 44 | 2.1392 | |
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| 2.131 | 8.9091 | 49 | 2.0661 | |
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| 2.0142 | 10.0 | 55 | 1.9626 | |
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| 1.8873 | 10.9091 | 60 | 1.8746 | |
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| 1.7633 | 12.0 | 66 | 1.7650 | |
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| 1.726 | 12.9091 | 71 | 1.6563 | |
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| 1.5711 | 14.0 | 77 | 1.5123 | |
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| 1.4344 | 14.9091 | 82 | 1.3950 | |
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| 1.3201 | 16.0 | 88 | 1.2661 | |
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| 1.1787 | 16.9091 | 93 | 1.1831 | |
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| 1.1444 | 18.0 | 99 | 1.1188 | |
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| 1.0591 | 18.9091 | 104 | 1.0836 | |
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| 1.0151 | 20.0 | 110 | 1.0540 | |
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| 1.0277 | 20.9091 | 115 | 1.0388 | |
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| 1.0025 | 22.0 | 121 | 1.0250 | |
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| 1.0161 | 22.9091 | 126 | 1.0154 | |
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| 0.9946 | 24.0 | 132 | 1.0047 | |
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| 0.9773 | 24.9091 | 137 | 0.9970 | |
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| 0.9708 | 26.0 | 143 | 0.9890 | |
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| 0.9374 | 26.9091 | 148 | 0.9822 | |
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| 0.9403 | 28.0 | 154 | 0.9751 | |
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| 0.94 | 28.9091 | 159 | 0.9703 | |
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| 0.902 | 30.0 | 165 | 0.9633 | |
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| 0.9215 | 30.9091 | 170 | 0.9604 | |
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| 0.8854 | 32.0 | 176 | 0.9548 | |
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| 0.96 | 32.9091 | 181 | 0.9503 | |
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| 0.9162 | 34.0 | 187 | 0.9453 | |
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| 0.8686 | 34.9091 | 192 | 0.9429 | |
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| 0.906 | 36.0 | 198 | 0.9385 | |
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| 0.8762 | 36.9091 | 203 | 0.9354 | |
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| 0.8929 | 38.0 | 209 | 0.9332 | |
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| 0.8687 | 38.9091 | 214 | 0.9301 | |
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| 0.8933 | 40.0 | 220 | 0.9279 | |
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| 0.858 | 40.9091 | 225 | 0.9241 | |
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| 0.8481 | 42.0 | 231 | 0.9223 | |
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| 0.8228 | 42.9091 | 236 | 0.9217 | |
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| 0.8593 | 44.0 | 242 | 0.9186 | |
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| 0.8238 | 44.9091 | 247 | 0.9156 | |
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| 0.8081 | 46.0 | 253 | 0.9161 | |
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| 0.8327 | 46.9091 | 258 | 0.9129 | |
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| 0.8029 | 48.0 | 264 | 0.9110 | |
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| 0.7909 | 48.9091 | 269 | 0.9094 | |
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| 0.7826 | 50.0 | 275 | 0.9079 | |
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| 0.773 | 50.9091 | 280 | 0.9122 | |
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| 0.7377 | 52.0 | 286 | 0.9078 | |
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| 0.7491 | 52.9091 | 291 | 0.9050 | |
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| 0.7414 | 54.0 | 297 | 0.9093 | |
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| 0.7275 | 54.9091 | 302 | 0.9053 | |
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| 0.7198 | 56.0 | 308 | 0.9046 | |
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| 0.7203 | 56.9091 | 313 | 0.9093 | |
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| 0.6903 | 58.0 | 319 | 0.9042 | |
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| 0.6987 | 58.9091 | 324 | 0.9107 | |
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| 0.7141 | 60.0 | 330 | 0.9079 | |
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| 0.7023 | 60.9091 | 335 | 0.9120 | |
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| 0.6945 | 62.0 | 341 | 0.9087 | |
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| 0.6897 | 62.9091 | 346 | 0.9130 | |
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| 0.6597 | 64.0 | 352 | 0.9134 | |
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| 0.6954 | 64.9091 | 357 | 0.9120 | |
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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 |
