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Llama3 70B RAG by WendyHoang

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  Autotrain compatible   Conversational   Endpoints compatible   Ggml   Gguf   Instruct   Llama   Pytorch   Q4   Quantized   Region:us   Sharded

Llama3 70B RAG Benchmarks

LLME Score: 0.24268
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

Llama3 70B RAG Parameters and Internals

LLM NameLlama3 70B RAG
Repository 🤗https://huggingface.co/WendyHoang/Llama3-70B-RAG 
Model Size70b
Required VRAM141.9 GB
Updated2024-09-18
MaintainerWendyHoang
Model Typellama
Instruction-BasedYes
Model Files  42.5 GB   48.0 GB: 1-of-2   9.9 GB: 2-of-2   4.6 GB: 1-of-30   4.7 GB: 2-of-30   5.0 GB: 3-of-30   5.0 GB: 4-of-30   4.7 GB: 5-of-30   4.7 GB: 6-of-30   4.7 GB: 7-of-30   5.0 GB: 8-of-30   5.0 GB: 9-of-30   4.7 GB: 10-of-30   4.7 GB: 11-of-30   4.7 GB: 12-of-30   5.0 GB: 13-of-30   5.0 GB: 14-of-30   4.7 GB: 15-of-30   4.7 GB: 16-of-30   4.7 GB: 17-of-30   5.0 GB: 18-of-30   5.0 GB: 19-of-30   4.7 GB: 20-of-30   4.7 GB: 21-of-30   4.7 GB: 22-of-30   5.0 GB: 23-of-30   5.0 GB: 24-of-30   4.7 GB: 25-of-30   4.7 GB: 26-of-30   4.7 GB: 27-of-30   5.0 GB: 28-of-30   5.0 GB: 29-of-30   2.1 GB: 30-of-30
GGML QuantizationYes
GGUF QuantizationYes
Quantization Typeggml|q4|gguf|q4_k
Model ArchitectureLlamaForCausalLM
Context Length8192
Model Max Length8192
Transformers Version4.41.1
Tokenizer ClassPreTrainedTokenizerFast
Padding Token<|end_of_text|>
Vocabulary Size128256
Torch Data Typebfloat16
Llama3 70B RAG (WendyHoang/Llama3-70B-RAG)

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Note: green Score (e.g. "73.2") means that the model is better than WendyHoang/Llama3-70B-RAG.

Rank the Llama3 70B RAG 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v2024072803
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