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Meta Llama 3 8B Instruct GPTQ by cortecs

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Meta Llama 3 8B Instruct GPTQ is an open-source language model by cortecs. Features: 8b LLM, VRAM: 5.7GB, Context: 8K, License: other, Quantized, Instruction-Based, LLM Explorer Score: 0.14, Arc: 59.6, HellaSwag: 78.8, MMLU: 65.1, GSM8K: 57.3.

  4-bit   4bit   Conversational   Dataset:wikitext   Endpoints compatible   Gptq   Instruct   Llama   Quantized   Region:us
Model Card on HF 🤗: https://huggingface.co/cortecs/Meta-Llama-3-8B-Instruct-GPTQ 

Meta Llama 3 8B Instruct GPTQ Benchmarks

ARC: 59.64 vs 96.7 (so35)-38.3%
HellaSwag: 78.84 vs 95.3 (gpt4)-17.3%
MMLU: 65.06 vs 88.3 (so35)-26.3%
GSM8K: 57.32 vs 96.4 (so35)-40.5%
LLME Score: 0.14212
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Meta Llama 3 8B Instruct GPTQ (cortecs/Meta-Llama-3-8B-Instruct-GPTQ)
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Meta Llama 3 8B Instruct GPTQ Parameters and Internals

Model Type 
text generation
Additional Notes 
Quantized model using GPTQ with 4bit, Act order: True, Group size: 128.
Supported Languages 
English (High), French (Intermediate), German (Intermediate), Italian (Intermediate), Spanish (Intermediate)
Safety Evaluation 
Ethical Considerations:
We did not check for data contamination.
Input Output 
Accepted Modalities:
text
LLM NameMeta Llama 3 8B Instruct GPTQ
Repository 🤗https://huggingface.co/cortecs/Meta-Llama-3-8B-Instruct-GPTQ 
Base Model(s)  ...eta Llama 3 8B Instruct FP8 KV   neuralmagic/Meta-Llama-3-8B-Instruct-FP8-KV
Model Size8b
Required VRAM5.7 GB
Updated2026-01-20
Maintainercortecs
Model Typellama
Instruction-BasedYes
Model Files  5.7 GB
GPTQ QuantizationYes
Quantization Typegptq|4bit
Model ArchitectureLlamaForCausalLM
Licenseother
Context Length8192
Model Max Length8192
Transformers Version4.40.1
Tokenizer ClassPreTrainedTokenizerFast
Vocabulary Size128256
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than cortecs/Meta-Llama-3-8B-Instruct-GPTQ.

Rank the Meta Llama 3 8B Instruct GPTQ 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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