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Opt 2.7B Wikitext2 by lnair

 »  All LLMs  »  lnair  »  Opt 2.7B Wikitext2   URL Share it on

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Opt 2.7B Wikitext2 is an open-source language model by lnair. Features: 2.7b LLM, VRAM: 11.1GB, Context: 2K, LLM Explorer Score: 0.07.

  Autotrain compatible   Dataset:wikitext   Endpoints compatible   Generated from trainer   Opt   Pytorch   Region:us   Sharded   Tensorboard
Model Card on HF 🤗: https://huggingface.co/lnair/opt-2.7b-wikitext2 

Opt 2.7B Wikitext2 Benchmarks

LLME Score: 0.07258
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 Wikitext2 (lnair/opt-2.7b-wikitext2)
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Opt 2.7B Wikitext2 Parameters and Internals

Model Type 
Language Model
Additional Notes 
Training uses Adam optimizer with betas=(0.9,0.999), epsilon=1e-08, learning rate of 3e-05, train and eval batch sizes of 2, using a linear learning rate scheduler over 2 epochs with seed 42.
Training Details 
Data Sources:
wikitext-2-raw-v1
Model Architecture:
Fine-tuned version of the OPT model
LLM NameOpt 2.7B Wikitext2
Repository 🤗https://huggingface.co/lnair/opt-2.7b-wikitext2 
Model Size2.7b
Required VRAM11.1 GB
Updated2025-09-23
Maintainerlnair
Model Typeopt
Model Files  10.0 GB: 1-of-2   1.1 GB: 2-of-2   0.0 GB
Model ArchitectureOPTForCausalLM
Context Length2048
Model Max Length2048
Transformers Version4.27.4
Tokenizer ClassGPT2Tokenizer
Beginning of Sentence Token</s>
End of Sentence Token</s>
Unk Token</s>
Vocabulary Size50272
Torch Data Typefloat32
Activation Functionrelu
Errorsreplace

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Note: green Score (e.g. "73.2") means that the model is better than lnair/opt-2.7b-wikitext2.

Rank the Opt 2.7B Wikitext2 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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