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Gem Marketing by marketeam

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  Autotrain compatible   En   Endpoints compatible   Gemma   Marketing   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/marketeam/Gem-Marketing 

Gem Marketing Benchmarks

LLME Score: 0.19619
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gem Marketing (marketeam/Gem-Marketing)

Gem Marketing Parameters and Internals

Model Type 
Causal decoder-only transformer language model, text generation
Use Cases 
Areas:
Further testing and assessment
Applications:
Marketing question answering, General marketing information queries, Function-calls on marketing platforms
Primary Use Cases:
Text generation in marketing domain, Knowledge exploration for marketing research, NLP research and experimentation
Limitations:
Not adapted to deliver knowledge appropriately or safely, Recommended against deploying in real-world practice settings
Considerations:
SFT or RLHF-tuned recommended for specific downstream tasks
Additional Notes 
Early checkpoint released to the AI community.
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:
43B tokens
Methodology:
Continue pretraining on marketing domain data with a small portion of previously seen data as a strategy to prevent catastrophic forgetting.
Context Length:
3000
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
Performance Tips:
In-context learning with 1000-1500 tokens added to the prompt is recommended
LLM NameGem Marketing
Repository 🤗https://huggingface.co/marketeam/Gem-Marketing 
Model Size2b
Required VRAM5 GB
Updated2025-02-22
Maintainermarketeam
Model Typegemma
Model Files  4.0 GB: 1-of-2   1.0 GB: 2-of-2
Supported Languagesen
Model ArchitectureGemmaForCausalLM
Licensegemma
Context Length8192
Model Max Length8192
Transformers Version4.40.0
Vocabulary Size256000
Torch Data Typefloat16

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

Rank the Gem 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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Release v20241227
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