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Phi 3 Mini 4K Instruct Fp16 by Xenova

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  Conversational   Custom code   Dml   Fp16   Instruct   Onnx   Onnxruntime   Phi3   Quantized   Region:us   Transformers.js
Model Card on HF 🤗: https://huggingface.co/Xenova/Phi-3-mini-4k-instruct_fp16 

Phi 3 Mini 4K Instruct Fp16 Benchmarks

LLME Score: 0.20225
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Phi 3 Mini 4K Instruct Fp16 (Xenova/Phi-3-mini-4k-instruct_fp16)

Phi 3 Mini 4K Instruct Fp16 Parameters and Internals

Model Type 
text-generation
Additional Notes 
This is a version of the official phi3 onnx model with modifications for onnxruntime-web, including fp16 with int4 block quantization for weights, output in fp32, and using MHA instead of GQA. The onnx and external data file requirements are to be below 2GB for chromium caching.
LLM NamePhi 3 Mini 4K Instruct Fp16
Repository 🤗https://huggingface.co/Xenova/Phi-3-mini-4k-instruct_fp16 
Base Model(s)  Phi 3 Mini 4K Instruct Fp8   unrahul/Phi-3-mini-4k-instruct-fp8
Updated2025-02-03
MaintainerXenova
Model Typephi3
Instruction-BasedYes
Quantization Typefp16
Model ArchitecturePhi3ForCausalLM
Licensemit
Context Length4096
Model Max Length4096
Transformers Version4.39.3
Tokenizer ClassLlamaTokenizer
Padding Token<|endoftext|>
Vocabulary Size32064
Torch Data Typebfloat16

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Rank the Phi 3 Mini 4K Instruct Fp16 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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