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Facebook Opt 6.7B Qcqa Ub 16 Best For Q Loss by saarvajanik

 »  All LLMs  »  saarvajanik  »  Facebook Opt 6.7B Qcqa Ub 16 Best For Q Loss   URL Share it on

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  Autotrain compatible   En   Endpoints compatible   Opt   Pytorch   Region:us   Sharded
Model Card on HF 🤗: https://huggingface.co/saarvajanik/facebook-opt-6.7b-qcqa-ub-16-best-for-q-loss 

Facebook Opt 6.7B Qcqa Ub 16 Best For Q Loss Benchmarks

ARC: 21.67 vs 96.7 (so35)-77.6%
HellaSwag: 26.65 vs 95.3 (gpt4)-72%
MMLU: 23.15 vs 88.3 (so35)-73.8%
TruthfulQA: 46.81 vs 59 (gpt4)-20.7%
WinoGrande: 51.22 vs 87.5 (gpt4)-41.5%
LLME Score: 0.18045
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Facebook Opt 6.7B Qcqa Ub 16 Best For Q Loss (saarvajanik/facebook-opt-6.7b-qcqa-ub-16-best-for-q-loss)
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Facebook Opt 6.7B Qcqa Ub 16 Best For Q Loss Parameters and Internals

Model Type 
Question Answering, Text Processing
Additional Notes 
This version employs a modified MHA architecture with 16 groups of KV heads per layer.
Supported Languages 
en (High)
LLM NameFacebook Opt 6.7B Qcqa Ub 16 Best For Q Loss
Repository 🤗https://huggingface.co/saarvajanik/facebook-opt-6.7b-qcqa-ub-16-best-for-q-loss 
Model Size6.7b
Required VRAM27.4 GB
Updated2025-09-23
Maintainersaarvajanik
Model Typeopt
Model Files  10.0 GB: 1-of-3   9.9 GB: 2-of-3   7.5 GB: 3-of-3
Supported Languagesen
Model ArchitectureOPTForCausalLM
Licensemit
Context Length2048
Model Max Length2048
Transformers Version4.23.1
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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Rank the Facebook Opt 6.7B Qcqa Ub 16 Best For Q Loss 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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