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Meta Llama 3.1 8B Instruct by LlamaFinetuneBase

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Meta Llama 3.1 8B Instruct is an open-source language model by LlamaFinetuneBase. Features: 8b LLM, VRAM: 16.1GB, Context: 128K, License: meta, Instruction-Based, LLM Explorer Score: 0.16.

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

Meta Llama 3.1 8B Instruct Benchmarks

LLME Score: 0.155
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 Instruct (LlamaFinetuneBase/Meta-Llama-3.1-8B-Instruct)
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Meta Llama 3.1 8B Instruct Parameters and Internals

Model Type 
text-generation
Use Cases 
Areas:
Commercial, Research
Applications:
Assistant chat, Natural language generation, Synthetic data generation
Primary Use Cases:
Multilingual dialogue, Tool-use integrations, Long context management
Limitations:
Use beyond supported languages without fine-tuning is not recommended, Compliance with acceptable use policy required
Considerations:
Developers should apply safety testing and tuning for their specific applications.
Additional Notes 
Openly releasing the model allows developers to fine-tune for languages and use-cases beyond those explicitly supported.
Supported Languages 
English (Advanced), German (Advanced), French (Advanced), Italian (Advanced), Portuguese (Advanced), Hindi (Advanced), Spanish (Advanced), Thai (Advanced)
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
Hardware Used:
H100-80GB GPU
Model Architecture:
Optimized transformer architecture
Safety Evaluation 
Methodologies:
Red-teaming, Adversarial prompting
Findings:
Model may produce inaccurate, biased or objectionable responses
Risk Categories:
CBRNE, Child Safety, Cyber attack enablement
Ethical Considerations:
Responsible use guidelines should be followed; specific capabilities should be evaluated for safety.
Responsible Ai Considerations 
Fairness:
Inclusion of multiple languages, consideration of cultural perspectives.
Transparency:
Extensive documentation and licensing information provided.
Accountability:
Developers are responsible for the safe deployment and compliance with local laws.
Mitigation Strategies:
Safety guidelines and resources are available to developers.
Input Output 
Input Format:
Multilingual text
Accepted Modalities:
text
Output Format:
Text and code
Performance Tips:
Consider tool-use templates and tokenization strategies for large inputs
Release Notes 
Version:
Llama 3.1
Date:
July 23, 2024
Notes:
A new collection of generative models optimized for multilingual dialogue with improvements in inference scalability.
LLM NameMeta Llama 3.1 8B Instruct
Repository 🤗https://huggingface.co/LlamaFinetuneBase/Meta-Llama-3.1-8B-Instruct 
Base Model(s)  meta-llama/Meta-Llama-3.1-8B   meta-llama/Meta-Llama-3.1-8B
Model Size8b
Required VRAM16.1 GB
Updated2025-04-04
MaintainerLlamaFinetuneBase
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 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

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Note: green Score (e.g. "73.2") means that the model is better than LlamaFinetuneBase/Meta-Llama-3.1-8B-Instruct.

Rank the Meta Llama 3.1 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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