Instructions to use nblinh/fcd3320e-2511-4e7b-a81e-8b77b18ba2eb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh/fcd3320e-2511-4e7b-a81e-8b77b18ba2eb with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2") model = PeftModel.from_pretrained(base_model, "nblinh/fcd3320e-2511-4e7b-a81e-8b77b18ba2eb") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +2 -2
- adapter_config.json +4 -4
- adapter_model.bin +1 -1
- adapter_model.safetensors +1 -1
- training_args.bin +1 -1
README.md
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This model is a fine-tuned version of [UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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## Model description
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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 [UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7176
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| 0.528 | 0.0002 | 10 | 0.7176 |
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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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"down_proj",
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"up_proj",
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"k_proj",
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"v_proj",
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"
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"o_proj"
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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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"up_proj",
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"down_proj",
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"gate_proj",
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"k_proj",
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"o_proj",
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"v_proj",
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"q_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.bin
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adapter_model.safetensors
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training_args.bin
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