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Phi 3.5 Mini Instruct Onnx by microsoft

 »  All LLMs  »  microsoft  »  Phi 3.5 Mini Instruct Onnx   URL Share it on

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  Arxiv:2306.00978   Conversational   Custom code   Dml   Instruct   Onnx   Onnxruntime   Phi3   Region:us
Model Card on HF 🤗: https://huggingface.co/microsoft/Phi-3.5-mini-instruct-onnx 

Phi 3.5 Mini Instruct Onnx Benchmarks

LLME Score: 0.2233
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Phi 3.5 Mini Instruct Onnx (microsoft/Phi-3.5-mini-instruct-onnx)

Phi 3.5 Mini Instruct Onnx Parameters and Internals

Model Type 
ONNX
Use Cases 
Areas:
commercial use, research
Applications:
general purpose AI systems
Primary Use Cases:
memory/compute constrained environments, latency bound scenarios, strong reasoning (especially code, math and logic)
Considerations:
Developers should consider common limitations of language models as they select use cases and evaluate and mitigate for accuracy, safety, and fairness.
Additional Notes 
Activation Aware Quantization (AWQ) works by identifying the top 1% most salient weights that are most important for maintaining accuracy and quantizing the remaining 99% of weights.
Training Details 
Data Sources:
synthetic data, filtered publicly available websites
Methodology:
supervised fine-tuning, proximal policy optimization, direct preference optimization
Context Length:
128000
Release Notes 
Version:
Phi-3.5-Mini-Instruct ONNX
Notes:
Update over the instruction-tuned Phi-3 Mini ONNX model release.
LLM NamePhi 3.5 Mini Instruct Onnx
Repository 🤗https://huggingface.co/microsoft/Phi-3.5-mini-instruct-onnx 
Updated2025-03-25
Maintainermicrosoft
Model Typephi3
Instruction-BasedYes
Model ArchitecturePhi3ForCausalLM
Licensemit
Context Length131072
Model Max Length131072
Transformers Version4.43.3
Vocabulary Size32064
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than microsoft/Phi-3.5-mini-instruct-onnx.

Rank the Phi 3.5 Mini Instruct Onnx 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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