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GoLLIE 7B by HiTZ

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  Arxiv:2310.03668   Autotrain compatible   Code   Codegen   Custom code   Dataset:ace05   Dataset:bc5cdr   Dataset:conll2003   Dataset:conll2012 ontonotesv5   Dataset:ncbi disease   Dataset:rams   Dataset:tacred   Dataset:wnut 17   En   Endpoints compatible   Event extraction   Ie   Information extraction   Llama   Named entity recogniton   Pytorch   Region:us   Relation extraction   Sharded
Model Card on HF 🤗: https://huggingface.co/HiTZ/GoLLIE-7B 

GoLLIE 7B Benchmarks

ARC: 36.09 vs 96.7 (so35)-62.7%
HellaSwag: 57.93 vs 95.3 (gpt4)-39.2%
MMLU: 29.38 vs 88.3 (so35)-66.7%
TruthfulQA: 39.27 vs 59 (gpt4)-33.4%
WinoGrande: 58.96 vs 87.5 (gpt4)-32.6%
GSM8K: 3.26 vs 96.4 (so35)-96.6%
LLME Score: 0.1739
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
GoLLIE 7B (HiTZ/GoLLIE-7B)
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GoLLIE 7B Parameters and Internals

Model Type 
Text Generation
Use Cases 
Areas:
Information Extraction, Named Entity Recognition, Event Extraction, Relation Extraction
Primary Use Cases:
zero-shot Information Extraction
Additional Notes 
Flash attention implementation must be enabled with `trust_remote_code=True` for optimal performance.
Supported Languages 
english (proficient)
Training Details 
Data Sources:
ACE05, bc5cdr, conll2003, ncbi_disease, conll2012_ontonotesv5, rams, tacred, wnut_17
LLM NameGoLLIE 7B
Repository 🤗https://huggingface.co/HiTZ/GoLLIE-7B 
Model Size7b
Required VRAM40.5 GB
Updated2025-09-08
MaintainerHiTZ
Model Typellama
Model Files  10.0 GB: 1-of-2   9.9 GB: 1-of-3   3.5 GB: 2-of-2   9.9 GB: 2-of-3   7.2 GB: 3-of-3
Supported Languagesen
Generates CodeYes
Model ArchitectureLlamaForCausalLM
Licensellama2
Context Length16384
Model Max Length16384
Transformers Version4.33.1
Tokenizer ClassCodeLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32016
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than HiTZ/GoLLIE-7B.

Rank the GoLLIE 7B 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124
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