Instructions to use emorywang/opsd-qwen3-1.7b-singlegpu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use emorywang/opsd-qwen3-1.7b-singlegpu with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B") model = PeftModel.from_pretrained(base_model, "emorywang/opsd-qwen3-1.7b-singlegpu") - Notebooks
- Google Colab
- Kaggle
OPSD-Qwen3-1.7B (Single-GPU H20 Reproduction)
On-Policy Self-Distillation (OPSD) LoRA adapter for Qwen3-1.7B, reproduced on a single NVIDIA H20 96GB.
Training Config
- Base model: Qwen/Qwen3-1.7B
- Method: OPSD (fixed teacher, LoRA student)
- LoRA: r=64, alpha=128, target=[q,k,v,o,gate,up,down]_proj
- Trainer: TRL 0.26 GOLD trainer
- Steps: 125 (effective batch=8)
- Hardware: 1×H20 96GB, SDPA attention
- Dataset: siyanzhao/Openthoughts_math_30k_opsd (29,434 examples)
Files
adapter_*.json/safetensors— Final LoRA adapter (step 125)checkpoint-100/— Full training state at step 100 (for paper comparison)
Usage
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B", torch_dtype="bfloat16")
model = PeftModel.from_pretrained(base, "emorywang/opsd-qwen3-1.7b-singlegpu")
tokenizer = AutoTokenizer.from_pretrained("emorywang/opsd-qwen3-1.7b-singlegpu")
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