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VLM2Vec LoRA by TIGER-Lab

 »  All LLMs  »  TIGER-Lab  »  VLM2Vec LoRA   URL Share it on

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  Arxiv:2410.05160   Autotrain compatible Base model:finetune:microsoft/... Base model:microsoft/phi-3.5-v...   Conversational   Custom code   Dataset:tiger-lab/mmeb-train   Embedding   En   Instruct   Lora   Phi3 v   Region:us   Vision
Model Card on HF 🤗: https://huggingface.co/TIGER-Lab/VLM2Vec-LoRA 

VLM2Vec LoRA Benchmarks

LLME Score: 0.18157
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
VLM2Vec LoRA (TIGER-Lab/VLM2Vec-LoRA)
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VLM2Vec LoRA Parameters and Internals

Model Type 
multimodal embedding
Additional Notes 
The model uses an [EOS] token as the representation of the multimodal inputs.
Supported Languages 
en (English)
Training Details 
Data Sources:
TIGER-Lab/MMEB-train
Methodology:
Converting an existing well-trained VLM into an embedding model with contrastive learning.
LLM NameVLM2Vec LoRA
Repository 🤗https://huggingface.co/TIGER-Lab/VLM2Vec-LoRA 
Base Model(s)  microsoft/Phi-3.5-vision-instruct   microsoft/Phi-3.5-vision-instruct
Required VRAM0 GB
Updated2025-09-23
MaintainerTIGER-Lab
Instruction-BasedYes
Model Files  0.0 GB   0.0 GB
Supported Languagesen
Model ArchitectureAutoModelForCausalLM
Licenseapache-2.0
Model Max Length131072
Is Biasednone
Tokenizer ClassLlamaTokenizer
Padding Token<|endoftext|>
PEFT TypeLORA
LoRA ModelYes
PEFT Target Modulesk_proj|out_proj|gate_up_proj|down_proj|v_proj|qkv_proj|o_proj|q_proj
LoRA Alpha64
LoRA Dropout0.1
R Param8

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Note: green Score (e.g. "73.2") means that the model is better than TIGER-Lab/VLM2Vec-LoRA.

Rank the VLM2Vec LoRA 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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Release v20241124
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