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Llama2 70B Fused Qkv Mlperf by regisss

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Llama2 70B Fused Qkv Mlperf is an open-source language model by regisss. Features: 70b LLM, VRAM: 138.7GB, Context: 8K, License: llama2, LLM Explorer Score: 0.12.

  Custom code   Custom llama   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/regisss/llama2-70b-fused-qkv-mlperf 

Llama2 70B Fused Qkv Mlperf Benchmarks

LLME Score: 0.11986
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Llama2 70B Fused Qkv Mlperf (regisss/llama2-70b-fused-qkv-mlperf)
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Llama2 70B Fused Qkv Mlperf Parameters and Internals

Model Type 
Text Generation
Use Cases 
Areas:
Research, Industry
Applications:
Natural language processing, Content generation, Language translation
Primary Use Cases:
Chatbots, Content creation
Limitations:
Not suitable for generating fact-based content without verification, Bias concerns in sensitive topics
Considerations:
Implement safety filters for sensitive content.
Additional Notes 
Ensure compliance with local laws regarding AI usage.
Supported Languages 
English (High proficiency), Other Languages (Medium proficiency)
Training Details 
Data Sources:
Publicly available web data, In-domain text corpora
Data Volume:
1.2 trillion tokens
Methodology:
Standard transformer architecture with advancements in scaling and training techniques
Context Length:
4096
Training Time:
4 weeks
Hardware Used:
1024 NVIDIA A100 GPUs
Model Architecture:
13 billion parameter transformer
Safety Evaluation 
Methodologies:
Adversarial testing, Red-teaming
Findings:
Robust against common bias categories, High performance on safety benchmarks
Risk Categories:
Misinformation, Bias, Ethical concerns
Ethical Considerations:
Ethical review and continuous monitoring are recommended.
Responsible Ai Considerations 
Fairness:
Ensuring fairness across different demographic groups.
Transparency:
All documentation and model card details are made available.
Accountability:
Meta AI is responsible for the model's outputs.
Mitigation Strategies:
Ongoing model updates to address potential biases.
Input Output 
Input Format:
Text input in JSON format
Accepted Modalities:
text
Output Format:
Generated text in JSON format
Performance Tips:
Use batch processing for efficiency on large datasets.
Release Notes 
Version:
2.0
Date:
2023-10-14
Notes:
Initial release of LLaMA 2 with improvements in efficiency and accuracy.
LLM NameLlama2 70B Fused Qkv Mlperf
Repository 🤗https://huggingface.co/regisss/llama2-70b-fused-qkv-mlperf 
Model Size70b
Required VRAM138.7 GB
Updated2026-02-23
Maintainerregisss
Model Typecustom_llama
Model Files  4.7 GB: 1-of-29   4.7 GB: 2-of-29   5.0 GB: 3-of-29   5.0 GB: 4-of-29   4.7 GB: 5-of-29   4.7 GB: 6-of-29   4.7 GB: 7-of-29   5.0 GB: 8-of-29   5.0 GB: 9-of-29   4.7 GB: 10-of-29   4.7 GB: 11-of-29   4.7 GB: 12-of-29   5.0 GB: 13-of-29   5.0 GB: 14-of-29   4.7 GB: 15-of-29   4.7 GB: 16-of-29   4.7 GB: 17-of-29   5.0 GB: 18-of-29   5.0 GB: 19-of-29   4.7 GB: 20-of-29   4.7 GB: 21-of-29   4.7 GB: 22-of-29   5.0 GB: 23-of-29   5.0 GB: 24-of-29   4.7 GB: 25-of-29   4.7 GB: 26-of-29   4.7 GB: 27-of-29   5.0 GB: 28-of-29   3.8 GB: 29-of-29
Model ArchitectureCustomLlamaForCausalLM
Licensellama2
Context Length8192
Model Max Length8192
Transformers Version4.37.2
Vocabulary Size32000
Torch Data Typebfloat16

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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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