Instructions to use Chandher/lorabloom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Chandher/lorabloom with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-1b7") model = PeftModel.from_pretrained(base_model, "Chandher/lorabloom") - Notebooks
- Google Colab
- Kaggle
Upload model
Browse files- adapter_config.json +2 -2
- adapter_model.bin +2 -2
adapter_config.json
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"init_lora_weights": true,
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"layers_pattern": null,
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"layers_to_transform": null,
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"lora_alpha":
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"lora_dropout": 0.05,
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"modules_to_save": null,
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"peft_type": "LORA",
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"r":
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"revision": null,
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"target_modules": [
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"query_key_value"
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"init_lora_weights": true,
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"layers_pattern": null,
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"layers_to_transform": null,
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"lora_alpha": 8,
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"lora_dropout": 0.05,
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 4,
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"revision": null,
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"target_modules": [
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"query_key_value"
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adapter_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:1a2bc749fd87149700e1ac0042d44e163bc858566e671665a3c381b12d4867f7
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size 3163457
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