Instructions to use nikeda/sft-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nikeda/sft-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "nikeda/sft-lora") - Notebooks
- Google Colab
- Kaggle
Upload LoRA adapter (README written by author)
Browse files- README.md +1 -1
- adapter_config.json +4 -4
- adapter_model.safetensors +1 -1
README.md
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- Base model: Qwen/Qwen3-4B-Instruct-2507
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- Method: QLoRA (4-bit)
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- Max sequence length: 512
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- Epochs:
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- Learning rate: 2e-05
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- LoRA: r=32, alpha=64
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- Base model: Qwen/Qwen3-4B-Instruct-2507
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- Method: QLoRA (4-bit)
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- Max sequence length: 512
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- Epochs: 2
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- Learning rate: 2e-05
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- LoRA: r=32, alpha=64
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adapter_config.json
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"revision": null,
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"target_modules": [
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"target_parameters": null,
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"task_type": "CAUSAL_LM",
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"revision": null,
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"target_modules": [
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"gate_proj",
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"k_proj",
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"o_proj",
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"q_proj"
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"target_parameters": null,
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"task_type": "CAUSAL_LM",
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adapter_model.safetensors
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