Meta Llama 3 8B Instruct AWQ by solidrust

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  4-bit   Autotrain compatible   Awq Base model:meta-llama/meta-lla... Base model:quantized:meta-llam...   Conversational   Endpoints compatible   Instruct   Llama   Quantized   Region:us   Safetensors   Sharded   Tensorflow

Meta Llama 3 8B Instruct AWQ Benchmarks

Meta Llama 3 8B Instruct AWQ (solidrust/Meta-Llama-3-8B-Instruct-AWQ)

Meta Llama 3 8B Instruct AWQ Parameters and Internals

Model Type 
text-generation
Additional Notes 
AWQ is an efficient low-bit weight quantization method.
Input Output 
Input Format:
Token IDs
Accepted Modalities:
text
Output Format:
Token IDs
Performance Tips:
Use NVidia GPUs for best performance.
LLM NameMeta Llama 3 8B Instruct AWQ
Repository ๐Ÿค—https://huggingface.co/solidrust/Meta-Llama-3-8B-Instruct-AWQ 
Base Model(s)  Meta Llama 3 8B Instruct   meta-llama/Meta-Llama-3-8B-Instruct
Model Size8b
Required VRAM5.8 GB
Updated2024-12-30
Maintainersolidrust
Model Typellama
Instruction-BasedYes
Model Files  4.7 GB: 1-of-2   1.1 GB: 2-of-2
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureLlamaForCausalLM
Context Length8192
Model Max Length8192
Transformers Version4.38.2
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 solidrust/Meta-Llama-3-8B-Instruct-AWQ.

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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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241227