Instructions to use iamPi/qwen36-35b-a3b-top3-qlora_e2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use iamPi/qwen36-35b-a3b-top3-qlora_e2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("lenikonate/qwen36-35b-a3b-2108-3e") model = PeftModel.from_pretrained(base_model, "iamPi/qwen36-35b-a3b-top3-qlora_e2") - Notebooks
- Google Colab
- Kaggle
iamPi/qwen36-35b-a3b-top3-qlora_e2
QLoRA adapter for lenikonate/qwen36-35b-a3b-2108-3e, epoch 2 of 3.
- data:
vuhaian/top3_lastdance, loss on the last assistant turn only - adapter: r=32, alpha=64, on the attention/linear-attention mixers and the
shared expert. The 256 routed experts are 3D
nn.Parametertensors, which PEFT cannot target, so they stay frozen. - base quantised to NF4 on its
nn.Linearmodules only (2.36B of 35.1B params); the experts remain bf16. - lr 5e-5 cosine, warmup 0.03, packed to 16,384 tokens, global batch 16 packs.
Load with peft.PeftModel.from_pretrained on top of the base model.
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Base model
lenikonate/qwen36-35b-a3b-2108-test3