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Meta Llama 3.1 70B Instruct by meta-llama

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Model Card on HF 🤗: https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct 

Meta Llama 3.1 70B Instruct Benchmarks

MMLU Pro: 47.88
GPQA: 14.21
MUSR: 17.69
BBH: 55.93
IFEval: 86.69 vs 88 (so35)-1.5%
MATH Lvl 5: 38.07
LLME Score: 0.43294
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Meta Llama 3.1 70B Instruct (meta-llama/Meta-Llama-3.1-70B-Instruct)
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Meta Llama 3.1 70B Instruct Parameters and Internals

Model Type 
text generation, multimodal
Use Cases 
Areas:
Commercial, Research
Applications:
Natural Language Generation, Multilingual Dialogue
Primary Use Cases:
Assistant-like chat, Synthetic data generation
Limitations:
Use in unsupported languages without additional safety checks
Considerations:
The Llama 3.1 Community License and Acceptable Use Policy guide permissible use.
Additional Notes 
Developers should adhere to the Llama 3.1 Community License when using the model.
Supported Languages 
English (High), German (High), French (High), Italian (High), Portuguese (High), Hindi (High), Spanish (High), Thai (High)
Training Details 
Data Sources:
Publicly available online data
Data Volume:
15 trillion tokens
Methodology:
Supervised fine-tuning and reinforcement learning with human feedback
Context Length:
128000
Training Time:
39.3M GPU hours
Hardware Used:
Meta's custom built GPU cluster
Model Architecture:
Transformer-based auto-regressive language model
Safety Evaluation 
Methodologies:
Red-teaming, Adversarial prompting
Findings:
Cybersecurity uplift, Chemical safety assessments
Risk Categories:
Misinformation, Child Safety, Cybersecurity
Ethical Considerations:
Engagement in responsible AI practices is encouraged.
Responsible Ai Considerations 
Fairness:
Emphasis on mitigation of biases through careful selection and use of data.
Transparency:
Documentation and community engagement to enhance transparency.
Accountability:
Developers are responsible for tailoring model use to their policy requirements.
Mitigation Strategies:
Comprehensive red-teaming.
Input Output 
Input Format:
Multilingual text input
Accepted Modalities:
Text
Output Format:
Multilingual text and code output
Performance Tips:
Use Grouped-Query Attention for enhanced scalability.
Release Notes 
Version:
3.1
Date:
July 23, 2024
Notes:
Multilingual support and improved safety protocols.
LLM NameMeta Llama 3.1 70B Instruct
Repository 🤗https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct 
Base Model(s)  meta-llama/Meta-Llama-3.1-70B   meta-llama/Meta-Llama-3.1-70B
Model Size70b
Required VRAM141.9 GB
Updated2024-09-25
Maintainermeta-llama
Model Typellama
Instruction-BasedYes
Model Files  4.6 GB: 1-of-30   4.7 GB: 2-of-30   5.0 GB: 3-of-30   5.0 GB: 4-of-30   4.7 GB: 5-of-30   4.7 GB: 6-of-30   4.7 GB: 7-of-30   5.0 GB: 8-of-30   5.0 GB: 9-of-30   4.7 GB: 10-of-30   4.7 GB: 11-of-30   4.7 GB: 12-of-30   5.0 GB: 13-of-30   5.0 GB: 14-of-30   4.7 GB: 15-of-30   4.7 GB: 16-of-30   4.7 GB: 17-of-30   5.0 GB: 18-of-30   5.0 GB: 19-of-30   4.7 GB: 20-of-30   4.7 GB: 21-of-30   4.7 GB: 22-of-30   5.0 GB: 23-of-30   5.0 GB: 24-of-30   4.7 GB: 25-of-30   4.7 GB: 26-of-30   4.7 GB: 27-of-30   5.0 GB: 28-of-30   5.0 GB: 29-of-30   2.1 GB: 30-of-30
Supported Languagesen de fr it pt hi es th
Model ArchitectureLlamaForCausalLM
Licensemeta
Context Length131072
Model Max Length131072
Transformers Version4.42.3
Tokenizer ClassPreTrainedTokenizerFast
Vocabulary Size128256
Torch Data Typebfloat16

Quantized Models of the Meta Llama 3.1 70B Instruct

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...lama 3.1 70B Instruct Bnb 4bit31630839 GB
...70B Instruct AQLM PV 2Bit 1x16461422 GB
Reflection Llama 3.1 70B Bf166283141 GB
Reflection 70B Bnb 4bit98539 GB
Reflection Llama 3.1 70B F164103141 GB

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Note: green Score (e.g. "73.2") means that the model is better than meta-llama/Meta-Llama-3.1-70B-Instruct.

Rank the Meta Llama 3.1 70B 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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