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Meta Llama 3 70B Instruct AWQ Smashed by PrunaAI

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Model Card on HF 🤗: https://huggingface.co/PrunaAI/Meta-Llama-3-70b-instruct-AWQ-smashed 

Meta Llama 3 70B Instruct AWQ Smashed Benchmarks

LLME Score: 0.18449
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 70B Instruct AWQ Smashed (PrunaAI/Meta-Llama-3-70b-instruct-AWQ-smashed)

Meta Llama 3 70B Instruct AWQ Smashed Parameters and Internals

Model Type 
compressed
Additional Notes 
Sync and Async metrics provided, relevant depending on the use-case.
Training Details 
Data Sources:
WikiText
Methodology:
The model is compressed with awq
Hardware Used:
NVIDIA A100-PCIE-40GB
LLM NameMeta Llama 3 70B Instruct AWQ Smashed
Repository 🤗https://huggingface.co/PrunaAI/Meta-Llama-3-70b-instruct-AWQ-smashed 
Model Size70b
Required VRAM39.9 GB
Updated2025-02-05
MaintainerPrunaAI
Model Typellama
Instruction-BasedYes
Model Files  5.0 GB: 1-of-9   4.9 GB: 2-of-9   4.9 GB: 3-of-9   4.9 GB: 4-of-9   4.9 GB: 5-of-9   4.9 GB: 6-of-9   4.9 GB: 7-of-9   3.4 GB: 8-of-9   2.1 GB: 9-of-9
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureLlamaForCausalLM
Context Length8192
Model Max Length8192
Transformers Version4.40.0
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 PrunaAI/Meta-Llama-3-70b-instruct-AWQ-smashed.

Rank the Meta Llama 3 70B Instruct AWQ Smashed 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 v20241227
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