Imp V1.5 2B Qwen1.5 by MILVLG

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  Arxiv:2405.12107   Autotrain compatible   Conversational   Custom code Dataset:liuhaotian/llava-instr... Dataset:liuhaotian/llava-pretr...   Imp qwen2   Instruct   Region:us   Safetensors   Sharded   Tensorflow

Imp V1.5 2B Qwen1.5 Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Imp V1.5 2B Qwen1.5 (MILVLG/Imp-v1.5-2B-Qwen1.5)

Imp V1.5 2B Qwen1.5 Parameters and Internals

Model Type 
text-generation, multimodal
Use Cases 
Areas:
academic, commercial
Additional Notes 
The example for using the model can only be run on GPUs currently.
Training Details 
Data Sources:
liuhaotian/LLaVA-Pretrain, liuhaotian/LLaVA-Instruct-150K
Methodology:
Built upon Qwen1.5-1.8B-Chat and SigLIP visual encoder trained on 1M mixed dataset
Model Architecture:
Qwen1.5-1.8B-Chat with SigLIP visual encoder
Input Output 
Input Format:
Similar to LLaVA format
Accepted Modalities:
text, image
LLM NameImp V1.5 2B Qwen1.5
Repository ๐Ÿค—https://huggingface.co/MILVLG/Imp-v1.5-2B-Qwen1.5 
Model Size2b
Required VRAM4.4 GB
Updated2025-02-22
MaintainerMILVLG
Model Typeimp_qwen2
Instruction-BasedYes
Model Files  1.0 GB: 1-of-5   1.0 GB: 2-of-5   1.0 GB: 3-of-5   0.8 GB: 4-of-5   0.6 GB: 5-of-5
Model ArchitectureImpQwen2ForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.36.0
Tokenizer ClassQwen2Tokenizer
Padding Token<|endoftext|>
Vocabulary Size151936
Torch Data Typefloat16
Errorsreplace

Rank the Imp V1.5 2B Qwen1.5 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241227