Text Generation
Transformers
Safetensors
English
Japanese
llama
mergekit
Merge
text-generation-inference
Instructions to use nitky/Swallow-70b-NVE-RP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nitky/Swallow-70b-NVE-RP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nitky/Swallow-70b-NVE-RP")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("nitky/Swallow-70b-NVE-RP") model = AutoModelForMultimodalLM.from_pretrained("nitky/Swallow-70b-NVE-RP") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nitky/Swallow-70b-NVE-RP with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nitky/Swallow-70b-NVE-RP" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nitky/Swallow-70b-NVE-RP", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nitky/Swallow-70b-NVE-RP
- SGLang
How to use nitky/Swallow-70b-NVE-RP 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 "nitky/Swallow-70b-NVE-RP" \ --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": "nitky/Swallow-70b-NVE-RP", "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 "nitky/Swallow-70b-NVE-RP" \ --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": "nitky/Swallow-70b-NVE-RP", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nitky/Swallow-70b-NVE-RP with Docker Model Runner:
docker model run hf.co/nitky/Swallow-70b-NVE-RP
- Xet hash:
- 9a21bb2433c6e862c69ba9f791220223292fd434854505c8d1cf122c56c1d3d0
- Size of remote file:
- 9.99 GB
- SHA256:
- 397f1ac9cdb40a1b934c569251426c790bae8b2b38b17fb25f808a1bb2ea6fd2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.