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Opt 125M Squad by anas-awadalla

 »  All LLMs  »  anas-awadalla  »  Opt 125M Squad   URL Share it on

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  Autotrain compatible   Endpoints compatible   Opt   Pytorch   Region:us
Model Card on HF 🤗: https://huggingface.co/anas-awadalla/opt-125m-squad 

Opt 125M Squad Benchmarks

LLME Score: 0.01516
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Opt 125M Squad (anas-awadalla/opt-125m-squad)
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Opt 125M Squad Parameters and Internals

Model Type 
extractive question answering
Use Cases 
Areas:
research, applications in extractive question answering
Primary Use Cases:
Answer extraction from given text contexts
Training Details 
Data Sources:
SQUAD dataset
Methodology:
Trained specifically for extractive question answering
Input Output 
Input Format:
(Context Text)\nQuestion:(Question Text)\nAnswer:
Accepted Modalities:
text
Output Format:
Extracted answer from context
LLM NameOpt 125M Squad
Repository 🤗https://huggingface.co/anas-awadalla/opt-125m-squad 
Model Size125m
Required VRAM0.5 GB
Updated2025-09-15
Maintaineranas-awadalla
Model Typeopt
Model Files  0.5 GB
Model ArchitectureOPTForCausalLM
Context Length2048
Model Max Length2048
Transformers Version4.19.2
Beginning of Sentence Token</s>
End of Sentence Token</s>
Unk Token</s>
Vocabulary Size50265
Torch Data Typefloat32
Activation Functionrelu
Errorsreplace

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Note: green Score (e.g. "73.2") means that the model is better than anas-awadalla/opt-125m-squad.

Rank the Opt 125M Squad Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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