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Meta Llama 3 8B Instruct Ct2 Int8 Float16 by avans06

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Meta Llama 3 8B Instruct Ct2 Int8 Float16 is an open-source language model by avans06. Features: 8b LLM, VRAM: 8GB, License: other, Instruction-Based, LLM Explorer Score: 0.14.

  Conversational   Ctranslate2   En   Endpoints compatible   Facebook   Float16   Instruct   Int8   Llama   Llama-3   Meta   Pytorch   Quantization   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/avans06/Meta-Llama-3-8B-Instruct-ct2-int8_float16 

Meta Llama 3 8B Instruct Ct2 Int8 Float16 Benchmarks

LLME Score: 0.13948
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 Instruct Ct2 Int8 Float16 (avans06/Meta-Llama-3-8B-Instruct-ct2-int8_float16)
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Meta Llama 3 8B Instruct Ct2 Int8 Float16 Parameters and Internals

Model Type 
text generation, instruction tuned
Use Cases 
Areas:
Commercial use, Research use, Assistant-like chat
Applications:
Natural language generation tasks, Dialogue-focused applications
Primary Use Cases:
Chatbots, Text generation in English
Limitations:
Use in languages other than English requires compliance with license
Considerations:
Developers should perform safety testing tailored to specific applications
Additional Notes 
Fine-tuning recommended for specific language support
Supported Languages 
English (native)
Training Details 
Data Sources:
Publicly available online data, Instruction datasets, Human-annotated examples
Data Volume:
15 trillion+ tokens
Methodology:
Supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF)
Context Length:
8000
Training Time:
7.7M GPU hours
Hardware Used:
H100-80GB GPUs
Model Architecture:
Optimized transformer architecture with Grouped-Query Attention (GQA)
Safety Evaluation 
Methodologies:
Red teaming, Adversarial evaluations, CyberSecEval safety eval suite
Findings:
Reduced residual risks of LLM Systems, Implemented safety mitigations
Risk Categories:
Cybersecurity, Child Safety
Ethical Considerations:
Industry-standard safety benchmarks and risk assessments were conducted
Responsible Ai Considerations 
Fairness:
Openness, inclusivity, and helpfulness are core values
Transparency:
Commitment to responsible AI development and open community collaboration
Accountability:
Meta is responsible for maintaining safety guidelines and providing resources
Mitigation Strategies:
Implemented safeguards, refusal optimization, and community feedback loops
Input Output 
Input Format:
Text only
Accepted Modalities:
text
Output Format:
Generates text and code only
Release Notes 
Version:
Meta-Llama-3-8B-Instruct-ct2-int8_float16
Date:
2024-4
Notes:
Quantized version with int8_float16 for CTranslate2
LLM NameMeta Llama 3 8B Instruct Ct2 Int8 Float16
Repository 🤗https://huggingface.co/avans06/Meta-Llama-3-8B-Instruct-ct2-int8_float16 
Model Size8b
Required VRAM8 GB
Updated2025-10-24
Maintaineravans06
Instruction-BasedYes
Model Files  8.0 GB
Supported Languagesen
Model ArchitectureAutoModel
Licenseother
Tokenizer ClassPreTrainedTokenizerFast

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Note: green Score (e.g. "73.2") means that the model is better than avans06/Meta-Llama-3-8B-Instruct-ct2-int8_float16.

Rank the Meta Llama 3 8B Instruct Ct2 Int8 Float16 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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