Instructions to use SammySpace/Qwen3.6-27B_SecAlign with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SammySpace/Qwen3.6-27B_SecAlign with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.6-27B") model = PeftModel.from_pretrained(base_model, "SammySpace/Qwen3.6-27B_SecAlign") - Transformers
How to use SammySpace/Qwen3.6-27B_SecAlign with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SammySpace/Qwen3.6-27B_SecAlign", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Qwen3.6-27B SecAlign
This repository contains a LoRA adapter for Qwen/Qwen3.6-27B. It does not include the base model weights. The supplied training metadata identifies direct preference optimization (DPO) with TRL.
To use the adapter, load the base model and apply this repository's adapter with PEFT. The adapter_config.json file records the base model and LoRA configuration; adapter_model.safetensors contains the adapter weights.
Training framework versions
- PEFT 0.19.1
- TRL 1.3.0
- Transformers 5.7.0.dev0
- PyTorch 2.8.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2
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Qwen/Qwen3.6-27B