Instructions to use tsk-18/ft-google-gemma-2b-it-qlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use tsk-18/ft-google-gemma-2b-it-qlora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it") model = PeftModel.from_pretrained(base_model, "tsk-18/ft-google-gemma-2b-it-qlora") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +7 -5
- adapter_config.json +5 -5
- adapter_model.safetensors +1 -1
- training_args.bin +1 -1
README.md
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This model is a fine-tuned version of [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss:
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## Model description
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- total_train_batch_size: 128
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: constant
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- num_epochs:
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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### Framework versions
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This model is a fine-tuned version of [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.6079
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## Model description
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- total_train_batch_size: 128
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: constant
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- num_epochs: 5
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 0.2365 | 1.0 | 1 | 2.6422 |
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| 0.1717 | 2.0 | 2 | 2.2893 |
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| 0.1298 | 3.0 | 3 | 1.9988 |
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| 0.0971 | 4.0 | 4 | 1.7610 |
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| 0.0673 | 5.0 | 5 | 1.6079 |
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### Framework versions
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adapter_config.json
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"o_proj",
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"v_proj",
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"up_proj",
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"down_proj",
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"q_proj",
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"k_proj",
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"
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],
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"down_proj",
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"k_proj",
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"q_proj",
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"gate_proj",
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"o_proj",
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"up_proj",
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"v_proj"
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],
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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adapter_model.safetensors
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training_args.bin
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