Instructions to use Prome4e7e/LLM_MainCompetition2026_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Prome4e7e/LLM_MainCompetition2026_v3 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, "Prome4e7e/LLM_MainCompetition2026_v3") - Notebooks
- Google Colab
- Kaggle
Upload LoRA adapter (README written by author)
Browse files- README.md +1 -1
- adapter_config.json +5 -5
- adapter_model.safetensors +1 -1
README.md
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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: 1.
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- LoRA: r=64, alpha=128
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## Usage
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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: 1.99e-05
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- LoRA: r=64, alpha=128
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## Usage
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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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"v_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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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"k_proj",
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"v_proj",
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"gate_proj",
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"q_proj",
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"up_proj",
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"o_proj"
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],
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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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version https://git-lfs.github.com/spec/v1
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size 528550256
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