Instructions to use vuhaian/qlora_27b_53k_phase1_adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use vuhaian/qlora_27b_53k_phase1_adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.8-27B") model = PeftModel.from_pretrained(base_model, "vuhaian/qlora_27b_53k_phase1_adapter") - Notebooks
- Google Colab
- Kaggle
vuhaian/qlora_27b_53k_phase1_adapter
QLoRA adapter for Qwen/Qwen3.8-27B — phase1 of a two-phase curriculum.
- phase 1:
vuhaian/53k_lastdance, 80 steps, lr 5e-5 cosine - phase 2:
vuhaian/top3_lastdance, 80 steps, lr 3e-5, continuing phase 1's adapter - r=32, alpha=64, 400 modules (48 linear-attn x3, 16 full-attn x4, 64 MLP x3), 217.6M params
- base quantised NF4 for training (4-bit reaches 95.3% of this dense model)
- packed to 16,384 tokens, global batch 16, loss on the last assistant turn only
Held-out eval (both splits removed before phase 1):
| heldout | rest | |
|---|---|---|
| phase 1 end | 0.2691 | 0.2805 |
| phase 2 end | 0.2525 | 0.2729 |
Scale note: 80 steps is ~4% of a phase-1 epoch and ~24% of a phase-2 epoch.
Load with peft.PeftModel.from_pretrained on top of the base.
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Qwen/Qwen3.8-27B