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--- |
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base_model: google/gemma-2-2b-it |
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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: gemma |
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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: Gemma-2-2B_task-1_60-samples_config-1_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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# Gemma-2-2B_task-1_60-samples_config-1_full |
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This model is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it) 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: 1.0263 |
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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: 0.0001 |
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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: 8 |
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- total_train_batch_size: 8 |
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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: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-------:|:----:|:---------------:| |
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| 2.6097 | 0.8696 | 5 | 2.4178 | |
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| 2.3915 | 1.9130 | 11 | 2.0981 | |
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| 1.8784 | 2.9565 | 17 | 1.6329 | |
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| 1.4387 | 4.0 | 23 | 1.2761 | |
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| 1.107 | 4.8696 | 28 | 1.1339 | |
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| 0.972 | 5.9130 | 34 | 1.0639 | |
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| 0.8841 | 6.9565 | 40 | 1.0263 | |
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| 0.7834 | 8.0 | 46 | 1.0309 | |
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| 0.7282 | 8.8696 | 51 | 1.0628 | |
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| 0.5877 | 9.9130 | 57 | 1.1318 | |
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| 0.4988 | 10.9565 | 63 | 1.2335 | |
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| 0.3489 | 12.0 | 69 | 1.5287 | |
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| 0.2429 | 12.8696 | 74 | 1.6523 | |
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| 0.1687 | 13.9130 | 80 | 1.9331 | |
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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 |
