Image-Text-to-Text
PEFT
Safetensors
English
lora
qwen3.5
vision-language
document-understanding
document-parsing
structured-extraction
json
insurance
legal-documents
ms-swift
vllm
Instructions to use Ashish-Ranjan/Qwen3.5-9B-Policy-Structure-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Ashish-Ranjan/Qwen3.5-9B-Policy-Structure-LoRA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B") model = PeftModel.from_pretrained(base_model, "Ashish-Ranjan/Qwen3.5-9B-Policy-Structure-LoRA") - Notebooks
- Google Colab
- Kaggle
File size: 3,336 Bytes
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<text x="24" y="34" font-size="18" font-weight="600" fill="#1f1f1f">Learning-rate sweep: full-document node F1 by epoch</text>
<text x="24" y="56" font-size="13" fill="#6b6b6b">Mean of the 2 held-out documents, plain free decoding (before bracket repair); epoch 0 = original model</text>
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<text x="70.0" y="300" font-size="11" fill="#6b6b6b" text-anchor="middle">original</text>
<text x="250.0" y="300" font-size="11" fill="#6b6b6b" text-anchor="middle">epoch 1</text>
<text x="430.0" y="300" font-size="11" fill="#6b6b6b" text-anchor="middle">epoch 2</text>
<text x="610.0" y="300" font-size="11" fill="#6b6b6b" text-anchor="middle">epoch 3</text>
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<text x="622.0" y="124.0" font-size="12" fill="#1f1f1f">lr 2e-4 · 0.78 ← released</text>
<text x="622.0" y="137.4" font-size="12" fill="#1f1f1f">lr 1e-4 · 0.72</text>
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