ViLegalQwen3-1.7B-Base

ViLegalQwen3-1.7B-Base is a decoder-only language model for Vietnamese legal text understanding, part of the ViLegalLM suite. It is continually pretrained from Qwen3-1.7B-Base on a newly curated 16GB Vietnamese legal corpus. ViLegalQwen3-1.7B-Base achieves state-of-the-art results among 1.7B-scale models across Vietnamese legal downstream tasks including Question Answering, Natural Language Inference, and Syllogism Reasoning.

Paper: ViLegalLM: Language Models for Vietnamese Legal Text — Read paper

Resources: GitHub | ViLegalBERT | ViLegalQwen2.5-1.5-Base

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("ntphuc149/ViLegalQwen3-1.7B-Base")
model = AutoModelForCausalLM.from_pretrained("ntphuc149/ViLegalQwen3-1.7B-Base")

Note: This is a base (pretrained) model, not an instruction-tuned model. We do not recommend using base language models for conversations. Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model.

Model Summary

Evaluation Results

ViLegalQwen3-1.7B-Base achieves state-of-the-art results among 1.7B-scale models across all evaluated Vietnamese legal benchmarks. Bold = best in the 1.7B parameter group. Italic = closed-source model scores.

True/False — ALQAC-TF

Multiple Choice — ALQAC-MCQ & VLSP-MCQ-LK

Abstractive QA — ViBidLQA-AQA

NLI — VLSP-NLI

Syllogism Reasoning — VLSP-Syllogism

Also in ViLegalLM

Limitations and Biases

• Domain scope: Trained exclusively on Vietnamese legal texts; may not generalize to other legal systems or jurisdictions.
• Base model only: Not instruction-tuned; outputs may be incomplete or incoherent without task-specific fine-tuning.
• Temporal bias: Legal corpus reflects Vietnamese law as of the collection date; model outputs may not reflect recent legislative changes.
• Inherited biases: May reflect biases present in the source legal corpora, including regional variations in legal practice and domain coverage imbalances.
• Not a legal authority: Model outputs should never be used as definitive legal interpretations without expert validation.

Intended Use

Intended for:

• Further fine-tuning on Vietnamese legal downstream tasks (QA, NLI, reasoning)
• Vietnamese legal text generation and completion
• Research on Vietnamese legal NLP and continual pretraining

Not intended for:

• Direct conversational or instruction-following use without fine-tuning
• Replacing professional legal counsel or human judgment in legal decision-making
• Providing legal advice without expert validation
• Legal systems outside Vietnam without careful domain adaptation

Citation

If you use ViLegalQwen3-1.7B-Base, please cite our paper:

@inproceedings{nguyen-etal-2026-vilegallm,
    title = "{V}i{L}egal{LM}: Language Models for {V}ietnamese Legal Text",
    author = "Nguyen, Truong-Phuc  and
      Nguyen, Quy-Nhan  and
      Nguyen, Minh-Tien",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-acl.1801/",
    pages = "36136--36150",
    ISBN = "979-8-89176-395-1",
    abstract = "We present **ViLegalLM**, comprising **ViLegalBERT** and **ViLegalQwen**, the first suite of Vietnamese pretrained language models for legal text understanding and generation. It includes one encoder-only model (ViLegalBERT, 135M parameters) and two decoder-only models (ViLegalQwen2.5-1.5B-Base and ViLegalQwen3-1.7B-Base), all continually pretrained on a newly curated 16GB Vietnamese legal corpus, significantly larger than previous work. To mitigate data scarcity, we construct three synthetic datasets using LLM-based generation and hard negative mining for True/False QA, Multiple Choice QA, and Natural Language Inference. We establish state-of-the-art results among open-source models on four main Vietnamese legal downstream tasks spanning ten benchmarks, demonstrating that continual pretraining from base models consistently outperforms instruction-tuned adaptation. Source codes, corpus, datasets, and model checkpoints are publicly available at https://github.com/ntphuc149/ViLegalLM."
}

License

Apache-2.0

This model is derived from Qwen3-1.7B-Base which is licensed under Apache-2.0. ViLegalQwen3-1.7B-Base is released under the same Apache-2.0 license.