What are the hardware requirements for Logics Parsing V2?

Logics Parsing V2 requires approximately 8.9 GB of VRAM and supports a context window of 256K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Logics Parsing V2 and how large is it?

Logics Parsing V2 is developed by Logics-MLLM, a model with 4.4b 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 Logics Parsing V2?

Logics Parsing V2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Logics Parsing V2 by Logics-MLLM — VRAM 8.9GB, 256K context

Name: Logics Parsing V2
Author: Logics-MLLM

Logics Parsing V2 is an open-source language model by Logics-MLLM. Features: 4.4b LLM, VRAM: 8.9GB, Context: 256K, LLM Explorer Score: 0.23.

Qwen3 vl Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Logics-MLLM/Logics-Parsing-v2

Logics Parsing V2 Parameters and Internals

LLM Name	Logics Parsing V2
Repository 🤗	https://huggingface.co/Logics-MLLM/Logics-Parsing-v2
Model Size	4.4b
Required VRAM	8.9 GB
Updated	2026-09-25
Maintainer	Logics-MLLM
Model Type	qwen3_vl
Model Files	5.0 GB: 1-of-2 3.9 GB: 2-of-2
Model Architecture	Qwen3VLForConditionalGeneration
Context Length	262144
Model Max Length	262144
Transformers Version	4.57.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Errors	replace

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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

Best Alternatives to Logics Parsing V2

Best Alternatives	Context / RAM	Downloads	Likes
IoU PD	0K / 9.7 GB	36	1

Note: green Score (e.g. "73.2") means that the model is better than Logics-MLLM/Logics-Parsing-v2.