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Quantized Opt 125M by Lkynnn

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Quantized Opt 125M is an open-source language model by Lkynnn. Features: 125m LLM, VRAM: 0.2GB, Context: 2K, License: mit, Quantized, LLM Explorer Score: 0.14.

  4-bit   4bit   Endpoints compatible   Gptq   Opt   Quantized   Region:us
Model Card on HF 🤗: https://huggingface.co/Lkynnn/Quantized_opt-125m 

Quantized Opt 125M Benchmarks

LLME Score: 0.13757
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Quantized Opt 125M (Lkynnn/Quantized_opt-125m)
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Quantized Opt 125M Parameters and Internals

LLM NameQuantized Opt 125M
Repository 🤗https://huggingface.co/Lkynnn/Quantized_opt-125m 
Model Size125m
Required VRAM0.2 GB
Updated2026-05-15
MaintainerLkynnn
Model Typeopt
Model Files  0.2 GB
GPTQ QuantizationYes
Quantization Typegptq|4bit
Model ArchitectureOPTForCausalLM
Licensemit
Context Length2048
Model Max Length2048
Transformers Version4.27.1
Vocabulary Size50272
Torch Data Typefloat16
Activation Functionrelu

Best Alternatives to Quantized Opt 125M

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Context / RAM
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Opt 125M Gptq2K / 0.1 GB50
Opt 125M Gptq 4bit2K / 0.1 GB50
Opt 125M Gptq2K / 0.1 GB170
Slc Opt 125M Gptq2K / 0.1 GB770
Opt 125M GPTQ 22K / 0.1 GB71
Opt 125M Gptq 4bits2K / 0.1 GB101
Opt 125M Gptq2K / 0.1 GB91
Opt 125M Gptq 4bit2K / 0.1 GB90
Opt 125M Gptq 4bit2K / 0.1 GB3280
Opt 125M 4bit 128g2K / 0.2 GB51
Note: green Score (e.g. "73.2") means that the model is better than Lkynnn/Quantized_opt-125m.

Rank the Quantized Opt 125M 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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