What are the hardware requirements for Qwen Legal Lora Rag Ft?

Qwen Legal Lora Rag Ft requires approximately 0.2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen Legal Lora Rag Ft and how large is it?

Qwen Legal Lora Rag Ft is developed by TinPhan2007, a model with 3b 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 Qwen Legal Lora Rag Ft?

Qwen Legal Lora Rag Ft is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen Legal Lora Rag Ft by TinPhan2007 — VRAM 0.2GB

Name: Qwen Legal Lora Rag Ft
Author: TinPhan2007

Qwen Legal Lora Rag Ft is an open-source language model by TinPhan2007. Features: 3b LLM, VRAM: 0.2GB, Instruction-Based, LLM Explorer Score: 0.24.

Arxiv:1910.09700 Base model:adapter:qwen/qwen2.... Base model:qwen/qwen2.5-3b-ins... Conversational Instruct Lora Peft Region:us Safetensors Sft Trl

Model Card on HF 🤗: https://huggingface.co/TinPhan2007/qwen-legal-lora-rag-ft

Qwen Legal Lora Rag Ft Parameters and Internals

LLM Name	Qwen Legal Lora Rag Ft
Repository 🤗	https://huggingface.co/TinPhan2007/qwen-legal-lora-rag-ft
Base Model(s)	Qwen/Qwen2.5-3B-Instruct Qwen/Qwen2.5-3B-Instruct
Model Size	3b
Required VRAM	0.2 GB
Updated	2026-11-10
Maintainer	TinPhan2007
Instruction-Based	Yes
Model Files	0.2 GB
Model Architecture	AutoModel
Model Max Length	131072
Is Biased	none
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	v_proj\|k_proj\|o_proj\|up_proj\|q_proj\|down_proj\|gate_proj
LoRA Alpha	128
LoRA Dropout	0.05
R Param	64
Errors	replace

Rank the Qwen Legal Lora Rag Ft capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

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Note: green Score (e.g. "73.2") means that the model is better than TinPhan2007/qwen-legal-lora-rag-ft.