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Galactica 30B by facebook

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  Arxiv:1810.03993   Autotrain compatible   Galactica   Opt   Pytorch   Region:us   Sharded
Model Card on HF 🤗: https://huggingface.co/facebook/galactica-30b 

Galactica 30B Benchmarks

LLME Score: 0.06659
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Galactica 30B (facebook/galactica-30b)
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Galactica 30B Parameters and Internals

Model Type 
Transformer, decoder-only
Use Cases 
Areas:
science, research
Applications:
citation prediction, scientific QA, mathematical reasoning, summarization, document generation, molecular property prediction, entity extraction
Primary Use Cases:
scientific tasks, scientific tooling
Limitations:
hallucinations, popularity bias, bias on certain measures, prone to exhibit stereotypes and toxicity
Considerations:
The model continues to exhibit a popularity bias and hallucinations especially for less cited scientific concepts.
Additional Notes 
GALACTICA models are designed to perform scientific tasks and are trained on a large-scale scientific corpus.
Training Details 
Data Sources:
papers, textbooks, scientific websites, encyclopedias, reference material, knowledge bases
Data Volume:
106 billion tokens
Model Architecture:
Transformer based architecture in a decoder-only setup with a few modifications
LLM NameGalactica 30B
Repository 🤗https://huggingface.co/facebook/galactica-30b 
Model Size30b
Required VRAM60.8 GB
Updated2025-08-18
Maintainerfacebook
Model Typeopt
Model Files  9.8 GB: 1-of-7   9.9 GB: 2-of-7   9.9 GB: 3-of-7   9.9 GB: 4-of-7   9.9 GB: 5-of-7   9.9 GB: 6-of-7   1.5 GB: 7-of-7
Model ArchitectureOPTForCausalLM
Licensecc-by-nc-4.0
Context Length2048
Model Max Length2048
Transformers Version4.21.0.dev0
Tokenizer ClassPreTrainedTokenizerFast
Vocabulary Size50000
Torch Data Typefloat16
Activation Functiongelu

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Note: green Score (e.g. "73.2") means that the model is better than facebook/galactica-30b.

Rank the Galactica 30B 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 v20241124
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