Instructions to use ZePhyRus6196/ai-marketingassistant-t5-strategy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ZePhyRus6196/ai-marketingassistant-t5-strategy with Transformers:

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

pipe = pipeline("text-generation", model="ZePhyRus6196/ai-marketingassistant-t5-strategy")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ZePhyRus6196/ai-marketingassistant-t5-strategy", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ZePhyRus6196/ai-marketingassistant-t5-strategy with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ZePhyRus6196/ai-marketingassistant-t5-strategy"
# 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-t5-strategy",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ZePhyRus6196/ai-marketingassistant-t5-strategy

SGLang

How to use ZePhyRus6196/ai-marketingassistant-t5-strategy 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-t5-strategy" \
    --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-t5-strategy",
		"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-t5-strategy" \
        --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-t5-strategy",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ZePhyRus6196/ai-marketingassistant-t5-strategy with Docker Model Runner:
```
docker model run hf.co/ZePhyRus6196/ai-marketingassistant-t5-strategy
```

AI Marketing Assistant – Strategy Model (T5)

This model is based on the pre-trained t5-small transformer and is adapted for generating digital marketing strategies for Pakistani SMEs.

Purpose

To generate structured and actionable marketing strategies based on:

Business type
Target audience
Monthly budget (PKR)
Preferred marketing platforms

Adaptation Approach

The model is adapted using prompt engineering and generation configuration. No training from scratch is performed.

Regional Focus

All outputs are guided toward the Pakistani market, considering:

Local consumer behavior
Budget constraints
Common platforms such as Facebook, Instagram, and WhatsApp

Deployment

The model is deployed using Hugging Face hosted inference and accessed through a backend AI service.

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

Model tree for ZePhyRus6196/ai-marketingassistant-t5-strategy

Base model

google-t5/t5-small

Finetuned

(2304)

this model