Instructions to use ericlmtn/Qwen3.8-27B-Steiner-Style-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ericlmtn/Qwen3.8-27B-Steiner-Style-LoRA 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, "ericlmtn/Qwen3.8-27B-Steiner-Style-LoRA") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Qwen3.8-27B Steiner Style LoRA
Private rank-16 LoRA adapter for prose-style generation with Qwen3.8-27B.
Base model
- Model:
Qwen/Qwen3.8-27B - Revision:
1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0
Use that exact base revision for reproducible loading and evaluation.
Adapter
- Format: PEFT LoRA
- Rank: 16
- Alpha: 32
- Dropout: 0
- Task: causal language modeling
- Maximum training sequence length: 8,192 tokens
- Training library: PEFT 0.20.0 with Unsloth
The adapter targets the model's full-attention projections, Gated DeltaNet projections, and language-model MLP projections.
Privacy and limitations
The training corpus is private and is not included in this repository. This adapter is intended for private evaluation and inference. It may reproduce phrases or stylistic patterns from its training material, and detector scores are not evidence of authorship.
Files
adapter_model.safetensors: LoRA weightsadapter_config.json: PEFT configuration and pinned base revisionSHA256SUMS: integrity checksums
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Qwen/Qwen3.8-27B