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Meta Llama 3.1 8B by meta-llama

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Meta Llama 3.1 8B is an open-source language model by meta-llama. Features: 8b LLM, VRAM: 16.1GB, Context: 128K, License: meta, LLM Explorer Score: 0.34.

  Arxiv:2204.05149   Autotrain compatible   De   En   Endpoints compatible   Es   Facebook   Fr   Hi   It   Llama   Llama-3   Meta   Pt   Pytorch   Region:us   Safetensors   Sharded   Tensorflow   Th
Model Card on HF 🤗: https://huggingface.co/meta-llama/Meta-Llama-3.1-8B 

Meta Llama 3.1 8B Benchmarks

MMLU Pro: 24.95
GPQA: 6.15
MUSR: 8.98
BBH: 25.29
IFEval: 12.7 vs 88 (so35)-85.6%
MATH Lvl 5: 5.14
LLME Score: 0.3407
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 8B (meta-llama/Meta-Llama-3.1-8B)
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Meta Llama 3.1 8B Parameters and Internals

Model Type 
text generation, multilingual
Use Cases 
Areas:
Commercial, Research
Applications:
Assistant-like chat, Natural language generation, Synthetic data generation
Primary Use Cases:
Multilingual dialogue, Instruction tuning
Limitations:
Usage in unsupported languages, Under circumstances violating applicable laws
Considerations:
Appropriate safeguards and responsible usage.
Additional Notes 
Encouraged for research and commercial applications with responsibility and adherence to guidelines.
Supported Languages 
English (Supported), German (Supported), French (Supported), Italian (Supported), Portuguese (Supported), Hindi (Supported), Spanish (Supported), Thai (Supported)
Training Details 
Data Sources:
publicly available sources, synthetically generated examples
Data Volume:
~15 trillion tokens
Methodology:
Auto-regressive transformer with 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:
Auto-regressive transformer
Safety Evaluation 
Methodologies:
Safety fine-tuning, Adversarial testing, Red-teaming
Risk Categories:
CBRNE helpfulness, Child Safety, Cyber attack enablement
Ethical Considerations:
Ensuring safe and responsible use.
Responsible Ai Considerations 
Fairness:
Emphasis on non-discrimination and safe engagement across languages.
Transparency:
Information on methodology and safety evaluations are shared.
Accountability:
Meta holds responsibility with guidelines for safe integration.
Mitigation Strategies:
Safety guardrails and community guidelines.
Input Output 
Accepted Modalities:
Multilingual Text
Output Format:
Multilingual Text and code
Performance Tips:
Ensure use of safeguards and compliance with guidelines.
Release Notes 
Version:
Llama 3.1
Date:
July 23, 2024
Notes:
Release of multilingual large models with improved tuning for safety and performance.
LLM NameMeta Llama 3.1 8B
Repository 🤗https://huggingface.co/meta-llama/Meta-Llama-3.1-8B 
Model Size8b
Required VRAM16.1 GB
Updated2024-09-25
Maintainermeta-llama
Model Typellama
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 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

Quantized Models of the Meta Llama 3.1 8B

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Likes
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VRAM
...eta Llama 3.1 8B Instruct FP16114784616 GB
Meta Llama 3.1 8B Bnb 4bit110272515 GB
Llama 3.1 Storm 8B Bnb 4bit7175 GB

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...xis Bookwriter Llama3.1 8B Sft1048K / 16.1 GB124
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Rank the Meta Llama 3.1 8B 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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