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Meta Llama 3 8B Instruct by meta-llama

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Meta Llama 3 8B Instruct is an open-source language model by meta-llama. Features: 8b LLM, VRAM: 16.1GB, Context: 8K, License: llama3, Instruction-Based, HF Score: 66.9, LLM Explorer Score: 0.39, Arc: 60.8, HellaSwag: 78.6, MMLU: 67.1, TruthfulQA: 51.7, WinoGrande: 74.5, GSM8K: 68.7.

  Conversational   Deploy:azure   En   Endpoints compatible   Facebook   Instruct   Llama   Llama-3   Meta   Pytorch   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct 

Meta Llama 3 8B Instruct Benchmarks

MMLU Pro: 28.79
GPQA: 5.7
MUSR: 5.4
BBH: 26.8
IFEval: 47.82 vs 88 (so35)-45.7%
ARC: 60.75 vs 96.7 (so35)-37.2%
HellaSwag: 78.55 vs 95.3 (gpt4)-17.6%
MMLU: 67.07 vs 88.3 (so35)-24%
TruthfulQA: 51.65 vs 59 (gpt4)-12.5%
WinoGrande: 74.51 vs 87.5 (gpt4)-14.8%
GSM8K: 68.69 vs 96.4 (so35)-28.7%
MATH Lvl 5: 9.14
LLME Score: 0.38739
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 (meta-llama/Meta-Llama-3-8B-Instruct)
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Meta Llama 3 8B Instruct Parameters and Internals

Model Type 
text generation, dialogue optimization
Use Cases 
Areas:
research, commercial applications
Applications:
assistant-like chat, natural language generation
Primary Use Cases:
Instruction tuned for chat-oriented tasks, Pretrained model adaptation
Limitations:
English language focus
Considerations:
Follow Responsible Use Guide and implement safety tools.
Additional Notes 
Developed with openness, inclusivity, and helpfulness in mind.
Supported Languages 
English (Fluent)
Training Details 
Data Sources:
publicly available online data
Data Volume:
15 trillion tokens
Methodology:
Supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF)
Context Length:
8000
Training Time:
7.7 million GPU hours
Hardware Used:
H100-80GB GPUs
Model Architecture:
Auto-regressive transformer
Safety Evaluation 
Methodologies:
red-teaming, adversarial evaluations
Findings:
Improved safety measures than previous versions
Risk Categories:
misinformation, cybersecurity, child safety
Ethical Considerations:
Ensures transparency and responsibility in deployment.
Responsible Ai Considerations 
Fairness:
Developers are encouraged to implement safety tools and assess risks.
Transparency:
Open source tooling and contribution encouraged.
Accountability:
Meta provides safety benchmarks and regular updates.
Mitigation Strategies:
Implement safeguards using Meta Llama Guard.
Input Output 
Input Format:
Text input
Accepted Modalities:
text
Output Format:
Generates text and code
Performance Tips:
Use specified transformers libraries for enhanced performance.
Release Notes 
Version:
3.1
Date:
April 18, 2024
Notes:
Introduced new variants with fine-tuning optimizations.
LLM NameMeta Llama 3 8B Instruct
Repository 🤗https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct 
Model Size8b
Required VRAM16.1 GB
Updated2026-04-23
Maintainermeta-llama
Model Typellama
Instruction-BasedYes
Model Files  5.0 GB: 1-of-4   5.0 GB: 2-of-4   4.9 GB: 3-of-4   1.2 GB: 4-of-4
Supported Languagesen
Model ArchitectureLlamaForCausalLM
Licensellama3
Context Length8192
Model Max Length8192
Transformers Version4.40.0.dev0
Tokenizer ClassPreTrainedTokenizerFast
Vocabulary Size128256
Torch Data Typebfloat16

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VRAM
Meta Llama 3 8B Instruct GGUF1011686982 GB
OrcaAgent Llama3.2 8B4716 GB
Nimue 8B Merged135 GB
Meta Llama 3 8B Instruct AWQ06365 GB
...ama3 8B Chinese Chat GGUF 8bit16814548 GB
Ai Medical Model 32bit11042 GB
Meta Llama 3 8B Instruct GPTQ611525 GB
...ama3 8B Chinese Chat GGUF 4bit275914 GB
...eta Llama 3 8B Instruct Zh 10K03016 GB
Meta Llama 3 8B Instruct GGUF57393 GB

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...otron 8B UltraLong 4M Instruct4192K / 32.1 GB89119
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...otron 8B UltraLong 2M Instruct2096K / 32.1 GB9117
Cthulhu 8B V1.41048K / 16.1 GB129
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Zero Llama 3.1 8B Beta61048K / 16.1 GB71
...dger Nu Llama 3.1 8B UltraLong1048K / 16.2 GB103

Rank the Meta Llama 3 8B Instruct 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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