What are the hardware requirements for Vilaw Llm DPO?

Vilaw Llm DPO requires approximately 0.2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Vilaw Llm DPO and how large is it?

Vilaw Llm DPO is developed by breakdown881, a model with 7b 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 Vilaw Llm DPO?

Vilaw Llm DPO is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Vilaw Llm DPO by breakdown881 — VRAM 0.2GB

Name: Vilaw Llm DPO
Author: breakdown881

Vilaw Llm DPO is an open-source language model by breakdown881. Features: 7b LLM, VRAM: 0.2GB, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.29.

Adapter Base model:adapter:qwen/qwen2.... Base model:qwen/qwen2.5-7b-ins... Conversational Dataset:ntphuc149/vibidlqa v1 Dataset:thangvip/vietnamese-le... Dpo Finetuned Instruct Legal Legal-reasoning Lora Peft Qlora Region:us Safetensors Synthetic-data Trl Unsloth Vi Vietnamese

Model Card on HF 🤗: https://huggingface.co/breakdown881/vilaw-llm-dpo

Vilaw Llm DPO Parameters and Internals

LLM Name	Vilaw Llm DPO
Repository 🤗	https://huggingface.co/breakdown881/vilaw-llm-dpo
Base Model(s)	Qwen/Qwen2.5-7B-Instruct Qwen/Qwen2.5-7B-Instruct
Model Size	7b
Required VRAM	0.2 GB
Updated	2026-09-14
Maintainer	breakdown881
Instruction-Based	Yes
Model Files	0.2 GB
Supported Languages	vi
Model Architecture	Adapter
License	apache-2.0
Is Biased	none
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|vision_pad\|>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	k_proj\|up_proj\|q_proj\|down_proj\|v_proj\|gate_proj\|o_proj
LoRA Alpha	32
LoRA Dropout	0
R Param	16

Rank the Vilaw Llm DPO capabilities

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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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Zduhac Cyber 7B V2	0K / 0 GB	8	1
Llama2 7B Qlora Guanaco	0K / 0.1 GB	11	1
Qwen Megumin	0K / 0.1 GB	11	1
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Vietnamese Amr Baseline 7B	0K / 0 GB	13	0
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Deepthink Reasoning Adapter	0K / 0.2 GB	16	3
Qwen2.5 7b NotesCorrector	0K / 0.6 GB	10	0
Mistral 7B Selfplay V0	0K / 0.2 GB	5	0
Mistral 7B V2 Selfplay	0K / 0.2 GB	7	0