Text Generation
Transformers
Safetensors
nemotron_h
audio
tts
snac
multilingual
continued-pretraining
pretrain
nemotron-h
hybrid-mamba
calliope
custom_code
Instructions to use zeroae/calliope-snac-4b-base-4k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zeroae/calliope-snac-4b-base-4k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zeroae/calliope-snac-4b-base-4k", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("zeroae/calliope-snac-4b-base-4k", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("zeroae/calliope-snac-4b-base-4k", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use zeroae/calliope-snac-4b-base-4k with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zeroae/calliope-snac-4b-base-4k" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zeroae/calliope-snac-4b-base-4k", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/zeroae/calliope-snac-4b-base-4k
- SGLang
How to use zeroae/calliope-snac-4b-base-4k 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 "zeroae/calliope-snac-4b-base-4k" \ --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": "zeroae/calliope-snac-4b-base-4k", "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 "zeroae/calliope-snac-4b-base-4k" \ --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": "zeroae/calliope-snac-4b-base-4k", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use zeroae/calliope-snac-4b-base-4k with Docker Model Runner:
docker model run hf.co/zeroae/calliope-snac-4b-base-4k
- Xet hash:
- 8bd4894a4e8e0ef9b5ab4ee08f17e5103fdc464e4363b53dbc1137629b4d4765
- Size of remote file:
- 19.7 MB
- SHA256:
- 838307355c9f04fd4de175277bd442fac7bb5f56164e0fbd69684fd2026f0cad
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.