What are the hardware requirements for Qwen3.5 9B Policy Structure LoRA?

Qwen3.5 9B Policy Structure LoRA requires approximately 0.2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen3.5 9B Policy Structure LoRA and how large is it?

Qwen3.5 9B Policy Structure LoRA is developed by Ashish-Ranjan, a model with 9b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Qwen3.5 9B Policy Structure LoRA?

Qwen3.5 9B Policy Structure LoRA is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen3.5 9B Policy Structure LoRA by Ashish-Ranjan — VRAM 0.2GB

Name: Qwen3.5 9B Policy Structure LoRA
Author: Ashish-Ranjan

Qwen3.5 9B Policy Structure LoRA is an open-source language model by Ashish-Ranjan. Features: 9b LLM, VRAM: 0.2GB, License: apache-2.0, LLM Explorer Score: 0.3.

Adapter Base model:adapter:qwen/qwen3.... Base model:qwen/qwen3.5-9b Dataset:ashish-ranjan/policy-s... Dataset:huggingfacem4/the caul... Dataset:openassistant/oasst2 Document-parsing Document-understanding En Finetuned Image-text-to-text Insurance Json Legal-documents Lora Ms-swift Peft Qwen3.5 Region:us Safetensors Structured-extraction Vision Vision-language Vllm

Model Card on HF 🤗: https://huggingface.co/Ashish-Ranjan/Qwen3.5-9B-Policy-Structure-LoRA

Qwen3.5 9B Policy Structure LoRA Parameters and Internals

LLM Name	Qwen3.5 9B Policy Structure LoRA
Repository 🤗	https://huggingface.co/Ashish-Ranjan/Qwen3.5-9B-Policy-Structure-LoRA
Base Model(s)	Qwen/Qwen3.5-9B Qwen/Qwen3.5-9B
Model Size	9b
Required VRAM	0.2 GB
Updated	2026-10-22
Maintainer	Ashish-Ranjan
Model Files	0.2 GB
Supported Languages	en
Model Architecture	Adapter
License	apache-2.0
Is Biased	none
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	^\|(\|m\|o\|d\|e\|l\|\\|.\|l\|a\|n\|g\|u\|a\|g\|e\|_\|m\|o\|d\|e\|l\|(\|?\|=\|\\|.\|)\|.\|*\|\\|.\|(\|i\|n\|_\|p\|r\|o\|j\|_\|b\|\|\|i\|n\|_\|p\|r\|o\|j\|_\|q\|k\|v\|\|\|g\|a\|t\|e\|_\|p\|r\|o\|j\|\|\|v\|_\|p\|r\|o\|j\|\|\|k\|_\|p\|r\|o\|j\|\|\|o\|_\|p\|r\|o\|j\|\|\|u\|p\|_\|p\|r\|o\|j\|\|\|i\|n\|_\|p\|r\|o\|j\|_\|z\|\|\|i\|n\|_\|p\|r\|o\|j\|_\|a\|\|\|o\|u\|t\|_\|p\|r\|o\|j\|\|\|d\|o\|w\|n\|_\|p\|r\|o\|j\|\|\|q\|_\|p\|r\|o\|j\|)\|)\|$
LoRA Alpha	32
LoRA Dropout	0.05
R Param	16

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Instruction Following and Task Automation
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Text Generation
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Multi-Language Support and Translation

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Note: green Score (e.g. "73.2") means that the model is better than Ashish-Ranjan/Qwen3.5-9B-Policy-Structure-LoRA.