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Meta Llama 3.1 70B by meta-llama

 »  All LLMs  »  meta-llama  »  Meta Llama 3.1 70B   URL Share it on

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

Meta Llama 3.1 70B Benchmarks

MMLU Pro: 40.6
GPQA: 18.34
MUSR: 16.58
BBH: 46.4
IFEval: 16.84 vs 88 (so35)-80.9%
MATH Lvl 5: 18.43
LLME Score: 0.33733
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 (meta-llama/Meta-Llama-3.1-70B)
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Meta Llama 3.1 70B Parameters and Internals

Model Type 
text generation, multimodal
Use Cases 
Areas:
Research, Commercial applications
Applications:
Multilingual dialogue, Natural language generation, Synthetic data generation, Distillation
Primary Use Cases:
Assistant-like chat, Multilingual text completion
Limitations:
Use is limited to supported languages unless fine-tuned for others.
Considerations:
Developers must ensure safe use using guidelines provided by Meta.
Additional Notes 
Llama 3.1 enables inference on large GPU infrastructures and requires adherence to responsible use practices.
Supported Languages 
en (English), de (German), fr (French), it (Italian), pt (Portuguese), hi (Hindi), es (Spanish), th (Thai)
Training Details 
Data Sources:
A new mix of publicly available online data
Data Volume:
~15 trillion tokens
Methodology:
Supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF)
Context Length:
128000
Training Time:
39.3M GPU hours
Hardware Used:
H100-80GB GPUs
Model Architecture:
Optimized transformer architecture
Safety Evaluation 
Methodologies:
Red-teaming exercises, Safety fine-tuning
Findings:
Potential risks in chemical, biological, radiological, nuclear areas, child safety, cyber attack enablement
Risk Categories:
CBRNE, Child Safety, Cyber attack
Ethical Considerations:
Llama 3.1's use should follow the Responsible Use Guide to mitigate risks.
Responsible Ai Considerations 
Fairness:
Model developed to mitigate bias and fairness issues through dedicated red-teaming and evaluation.
Transparency:
Meta provides detailed model and usage guidelines to ensure transparency.
Accountability:
Users are responsible for tailoring model safeguards for compliance with use policies.
Mitigation Strategies:
Meta provides best practices and resources to aid in safe deployment.
Input Output 
Input Format:
Multilingual text
Accepted Modalities:
Text, Code
Output Format:
Multilingual text and code
Performance Tips:
For longer context windows and additional languages, appropriate fine-tuning is needed.
Release Notes 
Version:
3.1
Date:
July 23, 2024
Notes:
Model release with improved safety and performance measures, longer context windows, and multilingual support.
LLM NameMeta Llama 3.1 70B
Repository 🤗https://huggingface.co/meta-llama/Meta-Llama-3.1-70B 
Model Size70b
Required VRAM141.9 GB
Updated2024-09-25
Maintainermeta-llama
Model Typellama
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 Version4.43.0.dev0
Tokenizer ClassPreTrainedTokenizerFast
Vocabulary Size128256
Torch Data Typebfloat16

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Rank the Meta Llama 3.1 70B 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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