Posttrain Lab Weights

《大语言模型后训练实践》课程实验的 LoRA adapter 权重。

目录	说明
`lab1-sft-r8/`	Lab1: LoRA r=8 SFT on Alpaca-zh (Qwen3-1.7B-Base)
`lab1-sft-r32/`	Lab1: LoRA r=32 SFT on Alpaca-zh (Qwen3-1.7B-Base)
`lab2-sft-best/`	Lab2: LoRA r=32 SFT on COIG-CQIA/zhihu (Qwen3-1.7B-Base)
`lab2-ablation-raw/`	Lab2 ablation: raw data (no QC)
`lab2-ablation-dedup/`	Lab2 ablation: dedup only
`lab2-ablation-clean/`	Lab2 ablation: full QC (dedup + filter)
`lab3-dpo/`	Lab3: DPO aligned on UltraFeedback (Qwen3-1.7B, beta=0.1)
`lab3-simpo/`	Lab3: SimPO aligned on UltraFeedback (Qwen3-1.7B, beta=2.0, gamma=0.5)
`lab3-dpo-beta005/`	Lab3 ablation: DPO beta=0.05
`lab3-dpo-beta01/`	Lab3 ablation: DPO beta=0.1
`lab3-dpo-beta05/`	Lab3 ablation: DPO beta=0.5

使用方法

from peft import PeftModel
from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B-Base")
model = PeftModel.from_pretrained(model, "leixinlin/posttrain-lab-weights", subfolder="lab2-sft-best")

Downloads last month: -; Downloads are not tracked for this model. How to track

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support