Instructions to use nirajsaran/AdTextGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nirajsaran/AdTextGeneration with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nirajsaran/AdTextGeneration")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nirajsaran/AdTextGeneration") model = AutoModelForCausalLM.from_pretrained("nirajsaran/AdTextGeneration", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nirajsaran/AdTextGeneration with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nirajsaran/AdTextGeneration" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nirajsaran/AdTextGeneration", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nirajsaran/AdTextGeneration
- SGLang
How to use nirajsaran/AdTextGeneration 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 "nirajsaran/AdTextGeneration" \ --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": "nirajsaran/AdTextGeneration", "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 "nirajsaran/AdTextGeneration" \ --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": "nirajsaran/AdTextGeneration", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nirajsaran/AdTextGeneration with Docker Model Runner:
docker model run hf.co/nirajsaran/AdTextGeneration
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license: mit
inference:
parameters:
temperature: 0.7
use_cache: false
max_length: 200
top_k: 5
top_p: 0.9
widget:
- text: "Sony TV"
example_title: "Amazon Ad text Electronics"
- text: "Apple Watch"
example_title: "Amazon Ad text Wearables"
- text: "Last minute shopping for Samsung headphones for"
example_title: "Ads for shopping deals"
- text: "Labor Day discounts for"
example_title: "Ads for Holiday deals"
metrics:
- bleu
- sacrebleu
---
Generates Ad copy, currently for ads for Amazon shopping (fine tuned for electronics and wearables).
**Usage Examples:**
Enter the bolded text below to get the Amazon ad generated by the model.
**Big savings on the new** Roku Streaming Device
**Mothers Day discounts for** Apple Watch Wireless Charger USB Charging Cable
**Big savings on the new Sony**
**Last minute shopping for Samsung headphones for**
You can try entering brand and product names like Samsung Galaxy to see the ad text generator in action.
Currently fine tuned on the EleutherAI/gpt-neo-125M model
**Model Performance:**
The model does quite well on the Electronics and Wearables categories on which it has been fine-tuned. There are, however, occasional hallucinations, though the ad copy is mostly coherent.
In other domains, it doesn't do quite as well...
Tesla for Christmas today,
Honda on sale
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