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Meta Llama 3 8B Instruct by rajkosto

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Model Card on HF 🤗: https://huggingface.co/rajkosto/Meta-Llama-3-8B-Instruct 

Meta Llama 3 8B Instruct 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: 0.19996
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 (rajkosto/Meta-Llama-3-8B-Instruct)

Meta Llama 3 8B Instruct Parameters and Internals

Model Type 
text generation, instruction tuned
Use Cases 
Areas:
commercial, research
Applications:
instruction-tuned for chat applications
Primary Use Cases:
chat-oriented generative tasks
Limitations:
Not suitable for language other than English, restricted under Acceptable Use Policy
Considerations:
Developers encouraged to implement safety assessments for specific applications.
Additional Notes 
Model supports fine-tuning for languages beyond English under compliance with the license and use policy.
Supported Languages 
en (Native)
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:
7.7M GPU hours on H100-80GB
Hardware Used:
H100-80GB GPUs
Model Architecture:
optimized transformer architecture
Safety Evaluation 
Methodologies:
red teaming, adversarial evaluations
Findings:
residual risks remain, model refusals reduced
Risk Categories:
CBRNE, cybersecurity, child safety
Ethical Considerations:
Ethical considerations include avoiding misuse of AI in harmful areas.
Responsible Ai Considerations 
Fairness:
Model is optimized to balance helpfulness and alignment, with considerations for avoiding biases.
Transparency:
Open source release with detailed documentation and responsible use guidelines.
Accountability:
Users must comply with license terms and acceptable use policy.
Mitigation Strategies:
Safety tools like Meta Llama Guard and Code Shield provided.
Input Output 
Input Format:
text input
Accepted Modalities:
text
Output Format:
text and code output
Performance Tips:
Use appropriate hardware and fine-tuning methods for optimal performance.
Release Notes 
Version:
April 18, 2024
Date:
2024-04-18
Notes:
Initial release of Llama 3 models.
LLM NameMeta Llama 3 8B Instruct
Repository 🤗https://huggingface.co/rajkosto/Meta-Llama-3-8B-Instruct 
Base Model(s)  mpasila/Meta-Llama-3-11.5B-Instruct   mpasila/Meta-Llama-3-11.5B-Instruct
Model Size8b
Required VRAM4.9 GB
Updated2025-02-22
Maintainerrajkosto
Model Typellama
Instruction-BasedYes
Model Files  4.9 GB   6.6 GB   16.1 GB
Supported Languagesen
GGUF QuantizationYes
Quantization Typegguf
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 rajkosto/Meta-Llama-3-8B-Instruct.

Rank the Meta Llama 3 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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