Instructions to use BigSalmon/InformalToFormalLincolnConciseWordy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BigSalmon/InformalToFormalLincolnConciseWordy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BigSalmon/InformalToFormalLincolnConciseWordy")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincolnConciseWordy") model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincolnConciseWordy") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use BigSalmon/InformalToFormalLincolnConciseWordy with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BigSalmon/InformalToFormalLincolnConciseWordy" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BigSalmon/InformalToFormalLincolnConciseWordy", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/BigSalmon/InformalToFormalLincolnConciseWordy
- SGLang
How to use BigSalmon/InformalToFormalLincolnConciseWordy 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 "BigSalmon/InformalToFormalLincolnConciseWordy" \ --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": "BigSalmon/InformalToFormalLincolnConciseWordy", "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 "BigSalmon/InformalToFormalLincolnConciseWordy" \ --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": "BigSalmon/InformalToFormalLincolnConciseWordy", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use BigSalmon/InformalToFormalLincolnConciseWordy with Docker Model Runner:
docker model run hf.co/BigSalmon/InformalToFormalLincolnConciseWordy
| ``` | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincolnConciseWordy") | |
| model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincolnConciseWordy") | |
| ``` | |
| ``` | |
| wordy: classical music is becoming less popular more and more. | |
| Translate into Concise Text: interest in classic music is fading. | |
| *** | |
| wordy: | |
| ``` | |
| ``` | |
| sweet: savvy voters ousted him. | |
| longer: voters who were informed delivered his defeat. | |
| *** | |
| sweet: | |
| ``` | |
| Keywords to sentences or sentence. |