Instructions to use ZePhyRus6196/ai-marketingassistant-mt5-content with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ZePhyRus6196/ai-marketingassistant-mt5-content with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ZePhyRus6196/ai-marketingassistant-mt5-content")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ZePhyRus6196/ai-marketingassistant-mt5-content", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ZePhyRus6196/ai-marketingassistant-mt5-content with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ZePhyRus6196/ai-marketingassistant-mt5-content"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZePhyRus6196/ai-marketingassistant-mt5-content",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ZePhyRus6196/ai-marketingassistant-mt5-content

SGLang

How to use ZePhyRus6196/ai-marketingassistant-mt5-content with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ZePhyRus6196/ai-marketingassistant-mt5-content" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZePhyRus6196/ai-marketingassistant-mt5-content",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ZePhyRus6196/ai-marketingassistant-mt5-content" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZePhyRus6196/ai-marketingassistant-mt5-content",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ZePhyRus6196/ai-marketingassistant-mt5-content with Docker Model Runner:
```
docker model run hf.co/ZePhyRus6196/ai-marketingassistant-mt5-content
```

AI Marketing Assistant – Content Model (mT5)

This model is based on the pre-trained mt5-small multilingual transformer and is adapted for generating marketing content for Small and Medium Enterprises (SMEs) operating in Pakistan.

Purpose

The model is designed to generate short-form marketing content suitable for digital platforms, including:

Social media captions
Promotional text
Call-to-action statements
Hashtags and post descriptions

Supported Languages

The model supports multilingual content generation in:

English
Urdu
Roman Urdu

Language selection is controlled at the prompt level by the backend application.

Adaptation Approach

This model is not trained from scratch. Instead, it is adapted using structured prompt templates and generation parameter tuning to guide the base mT5 model toward marketing-specific content generation tasks. This lightweight adaptation approach ensures stability, scalability, and reproducibility.

Regional Focus

Content generation is tailored for Pakistani SMEs by incorporating:

Local language preferences
Cultural tone and communication style
Commonly used marketing platforms such as Facebook, Instagram, and WhatsApp

Deployment

The model is deployed using Hugging Face hosted inference and is accessed through a modular AI service layer within the backend system. This allows real-time content generation without requiring local model hosting or GPU infrastructure.

Intended Use

This model is intended for academic and prototyping purposes as part of a Final Year Project. It demonstrates AI-assisted content generation in a localized marketing context.

Limitations

Output quality depends on prompt clarity and input structure
The model does not perform sentiment analysis or content moderation
Cultural nuances are guided by prompts rather than explicit fine-tuning

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for ZePhyRus6196/ai-marketingassistant-mt5-content

Base model

google/mt5-small

Finetuned

(683)

this model