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Qwen2 VL 2B Instruct GPTQ Int4 by Qwen

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Qwen2 VL 2B Instruct GPTQ Int4 is an open-source language model by Qwen. Features: 2b LLM, VRAM: 2.5GB, Context: 32K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.19.

  Arxiv:2308.12966   Arxiv:2409.12191   4-bit Base model:quantized:qwen/qwen... Base model:qwen/qwen2-vl-2b-in...   Conversational   En   Gptq   Image-text-to-text   Instruct   Multimodal   Quantized   Qwen2 vl   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4 

Qwen2 VL 2B Instruct GPTQ Int4 Benchmarks

LLME Score: 0.19465
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Qwen2 VL 2B Instruct GPTQ Int4 (Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4)
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Qwen2 VL 2B Instruct GPTQ Int4 Parameters and Internals

Model Type 
text generation, multimodal
Use Cases 
Areas:
research, commercial applications, agent integration
Applications:
visual understanding, video-based QA, mobile and robotic integrations
Primary Use Cases:
visual question answering, dialog, content creation, multilingual support
Limitations:
Lack of audio support, Updates up to June 2023, Limited individual/IP recognition, Limited complex instruction handling, Low counting accuracy, Weak spatial reasoning
Additional Notes 
Reports quantized model performance across various tasks, highlighting strengths in multimodal integration and weaknesses, such as limitations in audio and complex reasoning.
Supported Languages 
English (native), Chinese (native), European languages (high), Japanese (high), Korean (high), Arabic (high), Vietnamese (high)
Training Details 
Data Volume:
up to June 2023
Model Architecture:
Naive Dynamic Resolution, Multimodal Rotary Position Embedding (M-ROPE)
Input Output 
Input Format:
Images, Videos (local files, base64, URLs)
Accepted Modalities:
text, image
Output Format:
Textual descriptions
Performance Tips:
Enabling flash_attention_2 recommended for better acceleration and memory saving.
Release Notes 
Version:
Qwen2-VL-2B-Instruct-GPTQ-Int4
Date:
2023
Notes:
Quantized model version with multi-language support and enhanced image and video processing capabilities.
LLM NameQwen2 VL 2B Instruct GPTQ Int4
Repository 🤗https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4 
Base Model(s)  Qwen2 VL 2B Instruct   Qwen/Qwen2-VL-2B-Instruct
Model Size2b
Required VRAM2.5 GB
Updated2026-04-11
MaintainerQwen
Model Typeqwen2_vl
Instruction-BasedYes
Model Files  2.5 GB
Supported Languagesen
GPTQ QuantizationYes
Quantization Typegptq
Model ArchitectureQwen2VLForConditionalGeneration
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.45.0.dev0
Tokenizer ClassQwen2Tokenizer
Padding Token<|endoftext|>
Vocabulary Size151936
Torch Data Typefloat16
Errorsreplace

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Note: green Score (e.g. "73.2") means that the model is better than Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4.

Rank the Qwen2 VL 2B Instruct GPTQ Int4 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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