Instructions to use ishani29/gemma-summary-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ishani29/gemma-summary-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-3-1b-it") model = PeftModel.from_pretrained(base_model, "ishani29/gemma-summary-lora") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files- adapter_config.json +2 -2
- adapter_model.safetensors +1 -1
adapter_config.json
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"rank_pattern": {},
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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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"task_type": "CAUSAL_LM",
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"use_dora": false,
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:9b455005b3f85a5deb599c68538714b0aad2f574b59d0dcd0f15246ea0890c7e
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size 2995512
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