Instructions to use agurung/lcb-med-hard-ft-qwen3-4b-dft-iid-24-highlr-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use agurung/lcb-med-hard-ft-qwen3-4b-dft-iid-24-highlr-v1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507") model = PeftModel.from_pretrained(base_model, "agurung/lcb-med-hard-ft-qwen3-4b-dft-iid-24-highlr-v1") - Notebooks
- Google Colab
- Kaggle
agurung/lcb-med-hard-ft-qwen3-4b-dft-iid-24-highlr-v1
PEFT LoRA adapter archive for the LCB med/hard Qwen3-4B FT experiment.
- Base model:
Qwen/Qwen3-4B-Instruct-2507 - Local run name:
dft_iid_24-lora-r128-a32-lr5e-4-const-lr5e-4-qps8-gpuauto-ep4-highlr_v1 - Arm:
dft_iid_24 - Variant:
highlr-v1 - Adapter checkpoints:
16 - Included bytes:
18164814344 - Excluded local optimizer/RNG bytes:
0
The archive intentionally keeps HF/PEFT model artifacts and training metadata, but excludes optimizer, scheduler, and RNG state files. These repos are meant for evaluation and RL seeding, not exact optimizer-state resume.
Checkpoint dirs:
checkpoint-114, checkpoint-133, checkpoint-152, checkpoint-171, checkpoint-19, checkpoint-190, checkpoint-209, checkpoint-228, checkpoint-247, checkpoint-266, checkpoint-285, checkpoint-300, checkpoint-38, checkpoint-57, checkpoint-76, checkpoint-95
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Base model
Qwen/Qwen3-4B-Instruct-2507