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Phi3 Rag Relevance Judge Merge by grounded-ai

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Phi3 Rag Relevance Judge Merge is an open-source language model by grounded-ai. Features: 3.8b LLM, VRAM: 15.4GB, Context: 4K, Instruction-Based, LLM Explorer Score: 0.14.

  Autotrain compatible   Conversational   Custom code   Endpoints compatible   Instruct   Phi3   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/grounded-ai/phi3-rag-relevance-judge-merge 

Phi3 Rag Relevance Judge Merge Benchmarks

LLME Score: 0.13694
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Phi3 Rag Relevance Judge Merge (grounded-ai/phi3-rag-relevance-judge-merge)
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Phi3 Rag Relevance Judge Merge Parameters and Internals

Model Type 
Relevance Classification, Binary Classification
Use Cases 
Areas:
Binary classification task
Additional Notes 
The model has been merged from previous architectures and aims to achieve even better performance across various benchmarks and tasks as it continues to improve and fine-tune.
Input Output 
Input Format:
Formatted input comparing a reference text to a question
Accepted Modalities:
text
Output Format:
Single word: 'relevant' or 'unrelated'
Performance Tips:
Start with recommended prompting strategies and tweak as necessary.
LLM NamePhi3 Rag Relevance Judge Merge
Repository 🤗https://huggingface.co/grounded-ai/phi3-rag-relevance-judge-merge 
Model Size3.8b
Required VRAM15.4 GB
Updated2025-09-23
Maintainergrounded-ai
Model Typephi3
Instruction-BasedYes
Model Files  5.0 GB: 1-of-4   5.0 GB: 2-of-4   5.0 GB: 3-of-4   0.4 GB: 4-of-4
Model ArchitecturePhi3ForCausalLM
Context Length4096
Model Max Length4096
Transformers Version4.41.2
Tokenizer ClassLlamaTokenizer
Padding Token<|endoftext|>
Vocabulary Size32064
Torch Data Typefloat32

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Note: green Score (e.g. "73.2") means that the model is better than grounded-ai/phi3-rag-relevance-judge-merge.

Rank the Phi3 Rag Relevance Judge Merge 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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