Instructions to use katuni4ka/tiny-random-olmo-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use katuni4ka/tiny-random-olmo-hf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="katuni4ka/tiny-random-olmo-hf")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("katuni4ka/tiny-random-olmo-hf") model = AutoModelForCausalLM.from_pretrained("katuni4ka/tiny-random-olmo-hf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use katuni4ka/tiny-random-olmo-hf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "katuni4ka/tiny-random-olmo-hf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "katuni4ka/tiny-random-olmo-hf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/katuni4ka/tiny-random-olmo-hf
- SGLang
How to use katuni4ka/tiny-random-olmo-hf 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 "katuni4ka/tiny-random-olmo-hf" \ --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": "katuni4ka/tiny-random-olmo-hf", "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 "katuni4ka/tiny-random-olmo-hf" \ --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": "katuni4ka/tiny-random-olmo-hf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use katuni4ka/tiny-random-olmo-hf with Docker Model Runner:
docker model run hf.co/katuni4ka/tiny-random-olmo-hf
Download model.safetensors from katuni4ka/tiny-random-olmo-hf: direct link, hf CLI and curl.
- Browser
- Download file 26.2 MB
-
https://huggingface.co/katuni4ka/tiny-random-olmo-hf/resolve/main/model.safetensors
- Command line
-
hf download hf://katuni4ka/tiny-random-olmo-hf/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/katuni4ka/tiny-random-olmo-hf/resolve/main/model.safetensors
26.2 MB
- Xet hash:
- f64dc688363a0be9f8067bdfb714d748f85c28149c062a743d7c65f005be3fff
- Size of remote file:
- 26.2 MB
- SHA256:
- 49a5bd767851581f9b87b36527418f03697daa0e6a7855f9afe2263c7ec04e0f
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