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Opt 2.7B by facebook

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  64658   
  Arxiv:2005.14165   Arxiv:2205.01068   Autotrain compatible   En   Jax   Opt   Pytorch   Region:us   Tf
Model Card on HF 🤗: https://huggingface.co/facebook/opt-2.7b 

Opt 2.7B Benchmarks

ARC: 33.96 vs 96.7 (so35)-64.9%
HellaSwag: 61.43 vs 95.3 (gpt4)-35.5%
MMLU: 25.43 vs 88.3 (so35)-71.2%
TruthfulQA: 37.43 vs 59 (gpt4)-36.6%
WinoGrande: 61.96 vs 87.5 (gpt4)-29.2%
GSM8K: 0.23 vs 96.4 (so35)-99.8%
LLME Score: 0.1182
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Opt 2.7B (facebook/opt-2.7b)

Opt 2.7B Parameters and Internals

Model Type 
text-generation
Use Cases 
Areas:
research, downstream tasks evaluation, text generation
Primary Use Cases:
prompting for evaluation, fine-tuning on a downstream task
Limitations:
bias and safety issues, generation diversity and hallucination issues
Supported Languages 
English (predominantly supported)
Training Details 
Data Sources:
CommonCrawl, BookCorpus, CC-Stories, The Pile, Pushshift.io Reddit, CCNewsV2
Data Volume:
180B tokens
Methodology:
causal language modeling objective
Context Length:
2048
Training Time:
~33 days on 992 *80GB A100 GPUs
Hardware Used:
992 *80GB A100 GPUs
Model Architecture:
decoder-only
LLM NameOpt 2.7B
Repository 🤗https://huggingface.co/facebook/opt-2.7b 
Model Size2.7b
Required VRAM5.3 GB
Updated2025-05-09
Maintainerfacebook
Model Typeopt
Model Files  5.3 GB
Supported Languagesen
Model ArchitectureOPTForCausalLM
Licenseother
Context Length2048
Model Max Length2048
Transformers Version4.21.0.dev0
Beginning of Sentence Token</s>
End of Sentence Token</s>
Unk Token</s>
Vocabulary Size50272
Torch Data Typefloat16
Activation Functionrelu
Errorsreplace

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Context / RAM
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LongForm OPT 2.7B2K / 5.3 GB83
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OPT 2.7B Nerys V22K / 5.3 GB196
Opt 2.7B Realtime Chat2K / 11.1 GB82
Opt Peter 2.7B2K / 10.6 GB362
Opt 2.7B AWQ2K / 1.6 GB150
Note: green Score (e.g. "73.2") means that the model is better than facebook/opt-2.7b.

Rank the Opt 2.7B 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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Release v20241227
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