Instructions to use Gunulhona/S2S_LLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gunulhona/S2S_LLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Gunulhona/S2S_LLM")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Gunulhona/S2S_LLM") model = AutoModelForSeq2SeqLM.from_pretrained("Gunulhona/S2S_LLM") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Gunulhona/S2S_LLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Gunulhona/S2S_LLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Gunulhona/S2S_LLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Gunulhona/S2S_LLM
- SGLang
How to use Gunulhona/S2S_LLM 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 "Gunulhona/S2S_LLM" \ --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": "Gunulhona/S2S_LLM", "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 "Gunulhona/S2S_LLM" \ --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": "Gunulhona/S2S_LLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Gunulhona/S2S_LLM with Docker Model Runner:
docker model run hf.co/Gunulhona/S2S_LLM
[WIP] Upload folder using huggingface_hub (multi-commit 26a6a3eb1402b78cafdfc935b5d50513c97d73486ee998c98d2e30f64d4ae79e)
#2
by Gunulhona - opened
Upload folder using huggingface_hub
Multi commit ID: 26a6a3eb1402b78cafdfc935b5d50513c97d73486ee998c98d2e30f64d4ae79e
Scheduled commits:
- Upload 1 file(s) totalling 14.1G (438bc32b35e194986f83530204f1cab2494b895f54b384fd7df29495f293881c)
- Upload 1 file(s) totalling 84.5G (fafe5fe61e052e81cf072d6fdfc7b0ec4c7bbfd5c9a8f34d3de5ba29bd95a04d)
- Upload 2 file(s) totalling 24.3K (c1595486b0767922f8cd62f488fd507f85ee8cfa82bb7140872fdd1c1d30bbec)
This is a PR opened using the huggingface_hub library in the context of a multi-commit. PR can be commented as a usual PR. However, please be aware that manually updating the PR description, changing the PR status, or pushing new commits, is not recommended as it might corrupt the commit process. Learn more about multi-commits in this guide.