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Llama 3 8B Instruct V0.4 AWQ by solidrust

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Model Card on HF 🤗: https://huggingface.co/solidrust/Llama-3-8B-Instruct-v0.4-AWQ 

Llama 3 8B Instruct V0.4 AWQ Benchmarks

LLME Score: 0.19064
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Llama 3 8B Instruct V0.4 AWQ (solidrust/Llama-3-8B-Instruct-v0.4-AWQ)

Llama 3 8B Instruct V0.4 AWQ Parameters and Internals

Model Type 
text-generation
Additional Notes 
Utilizes AWQ low-bit weight quantization method for efficient and fast deployment
Training Details 
Methodology:
Quantization method: AWQ (4-bit low-bit weight quantization)
Model Architecture:
Based on meta-llama/Meta-Llama-3-8B-Instruct model
Input Output 
Input Format:
ChatML prompt template using user and system inputs
Accepted Modalities:
text
Output Format:
Text generated based on input prompt
LLM NameLlama 3 8B Instruct V0.4 AWQ
Repository 🤗https://huggingface.co/solidrust/Llama-3-8B-Instruct-v0.4-AWQ 
Model NameLlama-3-8B-Instruct-v0.4
Model CreatorMaziyarPanahi
Base Model(s)  MaziyarPanahi/Llama-3-8B-Instruct-v0.4   MaziyarPanahi/Llama-3-8B-Instruct-v0.4
Model Size8b
Required VRAM5.8 GB
Updated2025-01-15
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
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 solidrust/Llama-3-8B-Instruct-v0.4-AWQ.

Rank the Llama 3 8B Instruct V0.4 AWQ 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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