SESAME by tsunghanwu

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  Autotrain compatible   Endpoints compatible   Llava   Pytorch   Region:us   Sharded
Model Card on HF ๐Ÿค—: https://huggingface.co/tsunghanwu/SESAME 

SESAME Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
SESAME (tsunghanwu/SESAME)

SESAME Parameters and Internals

Model Type 
auto-regressive language model, segmentation model
Use Cases 
Areas:
research on large multimodal models and chatbots
Primary Use Cases:
research on large multimodal models and chatbots
Training Details 
Data Sources:
(FP-/R-)RefCOCO(+/g), LLaVA 150K VQA data
Methodology:
fine-tuning
Model Architecture:
multimodal model with instruction-based image grounding (segmentation)
LLM NameSESAME
Repository ๐Ÿค—https://huggingface.co/tsunghanwu/SESAME 
Model Size7b
Required VRAM16.2 GB
Updated2025-02-22
Maintainertsunghanwu
Model Typellava
Model Files  10.0 GB: 1-of-2   6.2 GB: 2-of-2
Model ArchitectureSESAMEForCausalLM
Licensemit
Context Length4096
Model Max Length4096
Transformers Version4.31.0
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32003
Torch Data Typebfloat16

Rank the SESAME 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