Instructions to use AIgot/copywriting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AIgot/copywriting with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AIgot/copywriting")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AIgot/copywriting") model = AutoModelForCausalLM.from_pretrained("AIgot/copywriting") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AIgot/copywriting with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AIgot/copywriting" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AIgot/copywriting", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AIgot/copywriting
- SGLang
How to use AIgot/copywriting 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 "AIgot/copywriting" \ --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": "AIgot/copywriting", "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 "AIgot/copywriting" \ --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": "AIgot/copywriting", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AIgot/copywriting with Docker Model Runner:
docker model run hf.co/AIgot/copywriting
| Step | Training Loss |
|---|---|
| 25 | 1.180900 |
| 50 | 0.551100 |
| 75 | 0.833000 |
| 100 | 0.497000 |
| 125 | 0.770000 |
| 150 | 0.479200 |
| 175 | 0.686700 |
| 200 | 0.472500 |
| 225 | 0.684200 |
| 250 | 0.426900 |
| 275 | 0.693900 |
| 300 | 0.443500 |
| 325 | 0.697600 |
| 350 | 0.415000 |
| 375 | 0.666300 |
| 400 | 0.430500 |
| 425 | 0.697800 |
| 450 | 0.392000 |
| 475 | 0.664700 |
| 500 | 0.411900 |
| 525 | 0.636400 |
| 550 | 0.394900 |
| 575 | 0.603400 |
| 600 | 0.389000 |
| 625 | 0.645300 |
| 650 | 0.377600 |
| 675 | 0.563100 |
| 700 | 0.377100 |
| 725 | 0.580400 |
| 750 | 0.351300 |
| 775 | 0.539300 |
| 800 | 0.363600 |
| 825 | 0.527900 |
| 850 | 0.382100 |
| 875 | 0.528600 |
| 900 | 0.358800 |
| 925 | 0.551900 |
| 950 | 0.383300 |
| 975 | 0.503500 |
| 1000 | 0.317800 |
| 1025 | 0.535700 |
| 1050 | 0.332900 |
| 1075 | 0.475400 |
| 1100 | 0.312800 |
| 1125 | 0.565500 |
| 1150 | 0.367300 |
| 1175 | 0.499300 |
| 1200 | 0.339900 |
- Downloads last month
- 6
Model tree for AIgot/copywriting
Base model
mistralai/Mistral-7B-v0.1
docker model run hf.co/AIgot/copywriting