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Meta Llama 3 8B Instruct GGUF by alokabhishek

 »  All LLMs  »  alokabhishek  »  Meta Llama 3 8B Instruct GGUF   URL Share it on

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  8b   Autotrain compatible   Conversational   Endpoints compatible   Facebook   Gguf   Instruct   Llama   Llama-3   Meta   Q4   Q4 k m   Q5 k m   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/alokabhishek/Meta-Llama-3-8B-Instruct-GGUF 

Meta Llama 3 8B Instruct GGUF Benchmarks

ARC: 59.64 vs 96.7 (so35)-38.3%
HellaSwag: 78.84 vs 95.3 (gpt4)-17.3%
MMLU: 65.06 vs 88.3 (so35)-26.3%
TruthfulQA: 50.17 vs 59 (gpt4)-15%
WinoGrande: 74.98 vs 87.5 (gpt4)-14.3%
GSM8K: 57.32 vs 96.4 (so35)-40.5%
LLME Score: 0.20009
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 GGUF (alokabhishek/Meta-Llama-3-8B-Instruct-GGUF)

Meta Llama 3 8B Instruct GGUF Parameters and Internals

Model Type 
text generation, auto-regressive
Use Cases 
Areas:
Commercial, Research
Applications:
Chat assistants, Various NLP tasks
Primary Use Cases:
Assistant-like chat
Limitations:
English only, Potential for biased responses
Considerations:
Adherence to the Acceptable Use Policy and suitability testing for specific use cases required.
Additional Notes 
None specified.
Supported Languages 
English (Primary)
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
Training Time:
1.3M GPU hours
Hardware Used:
H100-80GB GPUs
Model Architecture:
Optimized transformer architecture
Safety Evaluation 
Methodologies:
Red teaming, Adversarial evaluations
Findings:
Model is less likely to falsely refuse prompts than previous models
Risk Categories:
Misinformation, Bias, Child Safety
Ethical Considerations:
Residual risks will likely remain; developers should assess these risks in context of their application.
Responsible Ai Considerations 
Fairness:
Enhancements to reduce refusals while maintaining usefulness.
Transparency:
Open sourced tools for community use and contributions.
Accountability:
Developers need to ensure sufficient model safety for their applications.
Mitigation Strategies:
Use of Responsible Use Guide, Llama Guard, and community contributions to mitigate risks.
Input Output 
Input Format:
Text prompts formatted as chat messages.
Accepted Modalities:
text
Output Format:
Generated text or code output.
Release Notes 
Version:
3
Date:
April 18, 2024
Notes:
Introduction of Meta-Llama-3-8B-Instruct with improved safety and helper functions.
LLM NameMeta Llama 3 8B Instruct GGUF
Repository 🤗https://huggingface.co/alokabhishek/Meta-Llama-3-8B-Instruct-GGUF 
Base Model(s)  ...eta Llama 3 8B Instruct FP8 KV   neuralmagic/Meta-Llama-3-8B-Instruct-FP8-KV
Model Size8b
Required VRAM4.9 GB
Updated2025-02-05
Maintaineralokabhishek
Model Typellama
Instruction-BasedYes
Model Files  4.9 GB   5.7 GB
GGUF QuantizationYes
Quantization Typegguf|q4
Model ArchitectureLlamaForCausalLM
Licenseother
Context Length8192
Model Max Length8192
Transformers Version4.40.0.dev0
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 alokabhishek/Meta-Llama-3-8B-Instruct-GGUF.

Rank the Meta Llama 3 8B Instruct GGUF 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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Release v20241227
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