XFinder Llama38it by IAAR-Shanghai

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  Arxiv:2405.11874   Autotrain compatible   Conversational   En   Instruct   Llama   Pytorch   Region:us   Sharded

XFinder Llama38it Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
XFinder Llama38it (IAAR-Shanghai/xFinder-llama38it)

XFinder Llama38it Parameters and Internals

Model Type 
key answer extraction
Use Cases 
Areas:
evaluation of LLMs
Applications:
LLM evaluation
Primary Use Cases:
key answer extraction from LLM outputs
Additional Notes 
The model enhances the reliability of LLM evaluations and surpasses traditional RegEx-based extraction methods.
Training Details 
Data Sources:
Key Answer Finder (KAF) dataset
Data Volume:
26.9K samples
Methodology:
Fine-tuning
LLM NameXFinder Llama38it
Repository ๐Ÿค—https://huggingface.co/IAAR-Shanghai/xFinder-llama38it 
Model Size8b
Required VRAM16.1 GB
Updated2025-01-22
MaintainerIAAR-Shanghai
Model Typellama
Instruction-BasedYes
Model Files  2.0 GB: 1-of-9   1.9 GB: 2-of-9   2.0 GB: 3-of-9   1.9 GB: 4-of-9   2.0 GB: 5-of-9   1.9 GB: 6-of-9   2.0 GB: 7-of-9   1.3 GB: 8-of-9   1.1 GB: 9-of-9
Supported Languagesen
Model ArchitectureLlamaForCausalLM
Context Length8192
Model Max Length8192
Transformers Version4.39.3
Tokenizer ClassPreTrainedTokenizerFast
Vocabulary Size128256
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than IAAR-Shanghai/xFinder-llama38it.

Rank the XFinder Llama38it 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