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Meta Llama 3 8B Alternate Tokenizer by NousResearch

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Meta Llama 3 8B Alternate Tokenizer is an open-source language model by NousResearch. Features: 8b LLM, VRAM: 16.1GB, Context: 8K, License: other, LLM Explorer Score: 0.16.

  Autotrain compatible   Conversational   En   Endpoints compatible   Facebook   Llama   Llama-3   Meta   Pytorch   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer 

Meta Llama 3 8B Alternate Tokenizer Benchmarks

LLME Score: 0.15533
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 Alternate Tokenizer (NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer)
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Meta Llama 3 8B Alternate Tokenizer Parameters and Internals

Model Type 
text-generation
Use Cases 
Areas:
Commercial, Research
Applications:
Assistant-like chat, Natural language generation tasks
Primary Use Cases:
Commercial applications, Research
Limitations:
Out-of-scope use in languages other than English without fine-tuning
Considerations:
Developers are encouraged to fine-tune models for their specific use cases.
Additional Notes 
Pretraining does not include Meta user data. Developers encouraged to share feedback via provided channels. Carbon emissions for training offset by Meta's sustainability program.
Supported Languages 
English (Fully Supported)
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
Hardware Used:
Meta's Research SuperCluster (H100-80GB GPUs)
Model Architecture:
Optimized transformer architecture with Grouped-Query Attention (GQA)
Safety Evaluation 
Methodologies:
Red teaming, Adversarial evaluations
Risk Categories:
CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosives), Cyber security, Child safety
Ethical Considerations:
Residual risks remain; recommended safety evaluations before deployment
Responsible Ai Considerations 
Fairness:
Trade-offs between model helpfulness and alignment are unavoidable.
Transparency:
Efforts towards community standardization and transparency.
Accountability:
Developers should assess safety risks in specific applications.
Mitigation Strategies:
Use of Purple Llama tools and Llama Guard for system-level safety.
Input Output 
Input Format:
Text
Accepted Modalities:
text
Output Format:
Text
Release Notes 
Version:
3
Date:
April 18, 2024
Notes:
Initial release of Meta Llama 3 models (8B and 70B sizes)
LLM NameMeta Llama 3 8B Alternate Tokenizer
Repository 🤗https://huggingface.co/NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer 
Model Size8b
Required VRAM16.1 GB
Updated2025-09-23
MaintainerNousResearch
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
Model ArchitectureLlamaForCausalLM
Licenseother
Context Length8192
Model Max Length8192
Transformers Version4.40.0.dev0
Tokenizer ClassPreTrainedTokenizerFast
Padding Token<|end_of_text|>
Vocabulary Size128256
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer.

Rank the Meta Llama 3 8B Alternate Tokenizer Capabilities

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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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