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Meta Llama 3 8B Instruct AWQ by aspenita

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Meta Llama 3 8B Instruct AWQ is an open-source language model by aspenita. Features: 8b LLM, VRAM: 5.8GB, Context: 8K, License: llama3, Quantized, Instruction-Based, HF Score: 64.3, LLM Explorer Score: 0.15, Arc: 59.6, HellaSwag: 78.8, MMLU: 65.1, TruthfulQA: 50.2, WinoGrande: 75, GSM8K: 57.3.

  4-bit   Autoquant   Autotrain compatible   Awq   Conversational   En   Endpoints compatible   Facebook   Instruct   Llama   Llama-3   Meta   Pytorch   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/aspenita/Meta-Llama-3-8B-Instruct-AWQ 

Meta Llama 3 8B Instruct AWQ 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%
TruthfulQA: 50.17 vs 59 (gpt4)-15%
WinoGrande: 74.98 vs 87.5 (gpt4)-14.3%
GSM8K: 57.32 vs 96.4 (so35)-40.5%
LLME Score: 0.1509
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 AWQ (aspenita/Meta-Llama-3-8B-Instruct-AWQ)
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Meta Llama 3 8B Instruct AWQ Parameters and Internals

Model Type 
text-generation
Use Cases 
Areas:
commercial, research
Applications:
assistant-like chat
Primary Use Cases:
instruction-tuned models for dialogue
Limitations:
Use must comply with laws and Llama 3 license policy
Considerations:
Developers should perform safety testing and tuning prior to deployment.
Additional Notes 
Models are optimized for helpfulness and safety through RLHF and SFT.
Supported Languages 
primary (English)
Training Details 
Data Sources:
publicly available online data
Data Volume:
15 trillion tokens
Methodology:
pre-trained, instruction-tuned, RLHF
Context Length:
8000
Hardware Used:
Meta's Research SuperCluster, H100-80GB GPUs
Model Architecture:
auto-regressive transformer with Grouped-Query Attention
Safety Evaluation 
Methodologies:
red teaming, adversarial evaluations
Findings:
reduced residual risk via safety mitigations
Risk Categories:
misinformation, cybersecurity, child safety
Ethical Considerations:
developers must assess risks for specific use cases
Responsible Ai Considerations 
Fairness:
Safety benchmarks are transparent and rigorous.
Transparency:
Evaluations and benchmarks are publicly accessible.
Accountability:
Users and developers must adhere to guidelines and policies.
Mitigation Strategies:
Incorporate safeguards like Meta Llama Guard 2 and Code Shield
Input Output 
Input Format:
text
Accepted Modalities:
text
Output Format:
text and code
Performance Tips:
Use with transformers pipeline or llama3 codebase for best results.
Release Notes 
Version:
April 18, 2024
Date:
April 18, 2024
Notes:
Initial release of Llama 3 models, including optimized transformers architecture.
LLM NameMeta Llama 3 8B Instruct AWQ
Repository 🤗https://huggingface.co/aspenita/Meta-Llama-3-8B-Instruct-AWQ 
Base Model(s)  Meta Llama 3 13B Instruct   andrijdavid/Meta-Llama-3-13B-Instruct
Model Size8b
Required VRAM5.8 GB
Updated2025-09-23
Maintaineraspenita
Model Typellama
Instruction-BasedYes
Model Files  4.7 GB: 1-of-2   1.1 GB: 2-of-2
Supported Languagesen
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureLlamaForCausalLM
Licensellama3
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 aspenita/Meta-Llama-3-8B-Instruct-AWQ.

Rank the Meta Llama 3 8B Instruct 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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