Instructions to use goldfish-models/umb_latn_10mb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use goldfish-models/umb_latn_10mb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="goldfish-models/umb_latn_10mb")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("goldfish-models/umb_latn_10mb") model = AutoModelForCausalLM.from_pretrained("goldfish-models/umb_latn_10mb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use goldfish-models/umb_latn_10mb with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "goldfish-models/umb_latn_10mb" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "goldfish-models/umb_latn_10mb", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/goldfish-models/umb_latn_10mb
- SGLang
How to use goldfish-models/umb_latn_10mb 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 "goldfish-models/umb_latn_10mb" \ --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": "goldfish-models/umb_latn_10mb", "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 "goldfish-models/umb_latn_10mb" \ --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": "goldfish-models/umb_latn_10mb", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use goldfish-models/umb_latn_10mb with Docker Model Runner:
docker model run hf.co/goldfish-models/umb_latn_10mb
Download pytorch_model.bin from goldfish-models/umb_latn_10mb: direct link, hf CLI and curl.
- Browser
- Download file 157 MB
-
https://huggingface.co/goldfish-models/umb_latn_10mb/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://goldfish-models/umb_latn_10mb/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/goldfish-models/umb_latn_10mb/resolve/main/pytorch_model.bin
157 MB
- Xet hash:
- 1b93eb253bc08dd94dee524c4c5407717ec76d050a99393e638b9d630b6ad9f9
- Size of remote file:
- 157 MB
- SHA256:
- 234d327e0e25a5e9015ae4e605a9c98955f11c9dbf75423af1eb2f990773d948
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