Instructions to use TheCoderScientist/GarudaCoder-27B-ID-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use TheCoderScientist/GarudaCoder-27B-ID-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/kaggle/temp/qwen38-27b-4bit") model = PeftModel.from_pretrained(base_model, "TheCoderScientist/GarudaCoder-27B-ID-lora") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Commit History
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Add training manifest
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Publish trained GarudaCoder Qwen3.8 27B LoRA
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Upload training/manifest_qwen38.json with huggingface_hub
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Qwen3.8-27B v1 Kaggle T4x2: audited SFT, pinned base, holdout manifest (Trained with Unsloth)
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Tambah section Adaptive thinking (v2: hemat ~60% inference untuk pertanyaan mudah)
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Tambah section Identity reinforcement (v2: 30 contoh identity, 2 epoch, lr 1e-5)
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Manifest bersih (tanpa nama file sumber training)
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Model card (bersih — tanpa detail sumber training)
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Model card lengkap (angka dari manifest_qwen38.json)
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Qwen3.8-27B v1 Kaggle T4x2: audited SFT, pinned base, holdout manifest (Trained with Unsloth)
Verified signatureUpload model trained with Unsloth 2x faster2086c46
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Upload training/manifest_qwen38.json with huggingface_hub
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Upload model trained with Unsloth
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Qwen3.8-27B v1 Kaggle T4x2: audited SFT, pinned base, holdout manifest (Trained with Unsloth)
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Upload README.md with huggingface_hub
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initial commit
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