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Phi 3 Mini 4K Instruct 4 Endpoints by dordonezc

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  Autotrain compatible   Code   Conversational   Custom code   En   Endpoints compatible   Instruct   Phi3   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/dordonezc/Phi-3-mini-4k-instruct-4-endpoints 

Phi 3 Mini 4K Instruct 4 Endpoints Benchmarks

LLME Score: 0.19541
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 4 Endpoints (dordonezc/Phi-3-mini-4k-instruct-4-endpoints)

Phi 3 Mini 4K Instruct 4 Endpoints Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
Commercial use, Research use
Applications:
Memory/compute constrained environments, Latency-bound scenarios, Strong reasoning applications such as code, math, and logic
Primary Use Cases:
Accelerating research on language and multimodal models, Generative AI powered features
Limitations:
Not specifically designed or evaluated for all downstream purposes.
Considerations:
Evaluate for accuracy, safety, and fairness before using in specific scenarios. Adhere to relevant laws and regulations.
Additional Notes 
Phi-3 Mini models support ONNX format for cross-platform and hardware usage, ensuring optimized performance.
Supported Languages 
en (English)
Training Details 
Data Sources:
Publicly available documents, Synthetic, 'textbook-like' data
Data Volume:
3.3 trillion tokens
Methodology:
Supervised fine-tuning and Direct Preference Optimization
Context Length:
4000
Training Time:
7 days
Hardware Used:
512 H100-80G, GPUs
Model Architecture:
Dense decoder-only Transformer model
Responsible Ai Considerations 
Fairness:
These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes.
Accountability:
Developers should apply responsible AI best practices and are responsible for ensuring compliance with laws and regulations applicable to their use case.
Mitigation Strategies:
Use available safety classifiers, implement feedback mechanisms, augment with retrieval augmented generation for grounded responses.
Input Output 
Input Format:
Text prompts using chat format.
Accepted Modalities:
text
Output Format:
Generated text
LLM NamePhi 3 Mini 4K Instruct 4 Endpoints
Repository 🤗https://huggingface.co/dordonezc/Phi-3-mini-4k-instruct-4-endpoints 
Model Size3.8b
Required VRAM7.7 GB
Updated2025-02-22
Maintainerdordonezc
Model Typephi3
Instruction-BasedYes
Model Files  5.0 GB: 1-of-2   2.7 GB: 2-of-2
Supported Languagesen
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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Note: green Score (e.g. "73.2") means that the model is better than dordonezc/Phi-3-mini-4k-instruct-4-endpoints.

Rank the Phi 3 Mini 4K Instruct 4 Endpoints 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 v20241227
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