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Meta Llama 3 8B Instruct AWQ by study-hjt

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Meta Llama 3 8B Instruct AWQ is an open-source language model by study-hjt. Features: 8b LLM, VRAM: 5.8GB, Context: 8K, License: other, Quantized, Instruction-Based, HF Score: 66.3, LLM Explorer Score: 0.14, Arc: 59.9, HellaSwag: 80, MMLU: 64.8, TruthfulQA: 51.9, WinoGrande: 75.6, GSM8K: 65.7.

  4-bit   Awq   Conversational   En   Endpoints compatible   Facebook   Instruct   Int8   Llama   Llama-3   Llama3   Meta   Pytorch   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/study-hjt/Meta-Llama-3-8B-Instruct-AWQ 

Meta Llama 3 8B Instruct AWQ Benchmarks

ARC: 59.9 vs 96.7 (so35)-38.1%
HellaSwag: 80.01 vs 95.3 (gpt4)-16%
MMLU: 64.8 vs 88.3 (so35)-26.6%
TruthfulQA: 51.87 vs 59 (gpt4)-12.1%
WinoGrande: 75.61 vs 87.5 (gpt4)-13.6%
GSM8K: 65.73 vs 96.4 (so35)-31.8%
LLME Score: 0.14228
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 (study-hjt/Meta-Llama-3-8B-Instruct-AWQ)
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Meta Llama 3 8B Instruct AWQ Parameters and Internals

Model Type 
text-generation, instruction-tuned
Use Cases 
Areas:
commercial, research
Applications:
assistant-like chat, natural language generation
Primary Use Cases:
dialogue systems, AI chatbots
Limitations:
Not suitable for illegal activities, Prohibited use in non-compliant languages
Considerations:
Requires compliance with Meta's Acceptable Use Policy and License.
Additional Notes 
Designed for English language applications. Future updates planned to enhance model safety.
Supported Languages 
en (proficient)
Training Details 
Data Sources:
publicly available online data
Data Volume:
15T+ tokens
Methodology:
auto-regressive language model, optimized transformer architecture, supervised fine-tuning (SFT), reinforcement learning with human feedback (RLHF)
Context Length:
8000
Hardware Used:
Meta's Research SuperCluster, third-party cloud compute
Model Architecture:
optimized transformer architecture
Safety Evaluation 
Methodologies:
red teaming, adversarial evaluations
Findings:
minimal false refusals, high level of safety maintained through Purple Llama safeguards
Risk Categories:
misinformation, bias, cybersecurity, child safety
Ethical Considerations:
Residual risks and potential biases remain; responsible deployment encouraged.
Responsible Ai Considerations 
Fairness:
Efforts to minimize bias and improve model safety.
Transparency:
Model release and documentation publicly available.
Accountability:
Meta holds accountability for model safety and performance.
Mitigation Strategies:
Implemented safety techniques and feedback mechanisms for risk reduction.
Input Output 
Input Format:
text
Accepted Modalities:
text
Output Format:
text and code
Release Notes 
Version:
1.0
Date:
April 18, 2024
Notes:
Initial release with improved performance and safety features.
LLM NameMeta Llama 3 8B Instruct AWQ
Repository 🤗https://huggingface.co/study-hjt/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
Updated2026-05-05
Maintainerstudy-hjt
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
Licenseother
Context Length8192
Model Max Length8192
Transformers Version4.39.3
Tokenizer ClassPreTrainedTokenizerFast
Padding Token<|end_of_text|>
Vocabulary Size128256
Torch Data Typefloat16

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

Rank the Meta Llama 3 8B Instruct AWQ Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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