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Llama 30B Instruct by upstage

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  Dataset:gair/lima Dataset:metaeval/scienceqa tex...   Dataset:open-orca/openorca   Dataset:openbookqa   Dataset:sciq   En   Endpoints compatible   Instruct   Instruction   Llama   Pytorch   Region:us   Sharded   Upstage
Model Card on HF 🤗: https://huggingface.co/upstage/llama-30b-instruct 

Llama 30B Instruct Benchmarks

ARC: 62.46 vs 96.7 (so35)-35.4%
HellaSwag: 86.23 vs 95.3 (gpt4)-9.5%
MMLU: 59.37 vs 88.3 (so35)-32.8%
TruthfulQA: 52.78 vs 59 (gpt4)-10.5%
WinoGrande: 80.51 vs 87.5 (gpt4)-8%
GSM8K: 12.13 vs 96.4 (so35)-87.4%
LLME Score: 0.12759
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Llama 30B Instruct (upstage/llama-30b-instruct)
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Llama 30B Instruct Parameters and Internals

Model Type 
Text Generation
Use Cases 
Areas:
Research, Commercial Applications
Additional Notes 
Model can handle longer inputs with special configurations
Supported Languages 
English (Proficient)
Training Details 
Data Sources:
openbookqa, sciq, Open-Orca/OpenOrca, metaeval/ScienceQA_text_only, GAIR/lima
Methodology:
Fine-tuning
Context Length:
1024
Hardware Used:
A100x8 * 1
Model Architecture:
Transformer-based
Input Output 
Input Format:
### System: {System} ### User: {User} ### Assistant: {Assistant}
Accepted Modalities:
Text
Output Format:
Text
Performance Tips:
The model can handle up to 10k+ input tokens with `rope_scaling` enabled
LLM NameLlama 30B Instruct
Repository 🤗https://huggingface.co/upstage/llama-30b-instruct 
Model Size30b
Required VRAM65.2 GB
Updated2025-12-24
Maintainerupstage
Model Typellama
Instruction-BasedYes
Model Files  9.8 GB: 1-of-7   10.0 GB: 2-of-7   9.9 GB: 3-of-7   9.9 GB: 4-of-7   9.9 GB: 5-of-7   10.0 GB: 6-of-7   5.7 GB: 7-of-7
Supported Languagesen
Model ArchitectureLlamaForCausalLM
Context Length2048
Model Max Length2048
Transformers Version4.30.2
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32000
Torch Data Typebfloat16

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

Rank the Llama 30B Instruct 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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Release v20241124
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