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Phi Marketing by marketeam

 »  All LLMs  »  marketeam  »  Phi Marketing   URL Share it on

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  Autotrain compatible   Custom code   Endpoints compatible   Instruct   Marketing   Phi3   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/marketeam/Phi-Marketing 

Phi Marketing Benchmarks

LLME Score: 0.19253
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Phi Marketing (marketeam/Phi-Marketing)

Phi Marketing Parameters and Internals

Model Type 
Causal decoder-only transformer language model
Use Cases 
Areas:
Text Generation, Knowledge Exploration, Natural Language Processing (NLP) Research
Additional Notes 
This is an early checkpoint of the model adapted for marketing domain from a larger model.
Supported Languages 
English (proficient)
Training Details 
Data Sources:
Blogs, Books, Websites, Podcasts, Newsletters, Publications, Social Media, Ad-Campaigns, Landing Pages, Press Releases, Email-Campaigns, Brochures & Flyers, Product Description, Testimonials & Reviews
Data Volume:
more than 43B tokens
Context Length:
3072
Training Time:
±250 hours
Hardware Used:
4 NVIDIA A100 GPUs, p4de.24xlarge machine
Model Architecture:
Causal decoder-only transformer
Input Output 
Input Format:
Text-only
Accepted Modalities:
text
Output Format:
Text-only
Performance Tips:
Apply in-context learning with 1000-1500 tokens added to the prompt.
LLM NamePhi Marketing
Repository 🤗https://huggingface.co/marketeam/Phi-Marketing 
Model Size3.8b
Required VRAM7.6 GB
Updated2025-02-22
Maintainermarketeam
Model Typephi3
Instruction-BasedYes
Model Files  4.0 GB: 1-of-2   3.6 GB: 2-of-2
Model ArchitecturePhi3ForCausalLM
Licensems-pl
Context Length131072
Model Max Length131072
Transformers Version4.40.0
Vocabulary Size32011
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than marketeam/Phi-Marketing.

Rank the Phi Marketing Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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