Instructions to use Taehyun34/output_gptreasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Taehyun34/output_gptreasoning with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceM4/idefics2-8b") model = PeftModel.from_pretrained(base_model, "Taehyun34/output_gptreasoning") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +1 -1
- adapter_config.json +3 -3
- adapter_model.safetensors +1 -1
- training_args.bin +1 -1
README.md
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This model is a fine-tuned version of [HuggingFaceM4/idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b) 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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This model is a fine-tuned version of [HuggingFaceM4/idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5066
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## Model description
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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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"k_proj",
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"q_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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"q_proj",
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"k_proj",
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"o_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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