Instructions to use Azrail/smallm_140_rope with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Azrail/smallm_140_rope with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Azrail/smallm_140_rope")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Azrail/smallm_140_rope", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Azrail/smallm_140_rope with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Azrail/smallm_140_rope" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Azrail/smallm_140_rope", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Azrail/smallm_140_rope
- SGLang
How to use Azrail/smallm_140_rope 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 "Azrail/smallm_140_rope" \ --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": "Azrail/smallm_140_rope", "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 "Azrail/smallm_140_rope" \ --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": "Azrail/smallm_140_rope", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Azrail/smallm_140_rope with Docker Model Runner:
docker model run hf.co/Azrail/smallm_140_rope
Download last-checkpoint/rng_state.pth from Azrail/smallm_140_rope: direct link, hf CLI and curl.
- Browser
- Download file 14.2 kB
-
https://huggingface.co/Azrail/smallm_140_rope/resolve/64c1cb33dbb039ab888013b349c5c04271b1e3d7/last-checkpoint/rng_state.pth
- Command line
-
hf download hf://Azrail/smallm_140_rope@64c1cb33dbb039ab888013b349c5c04271b1e3d7/last-checkpoint/rng_state.pth
-
curl -L -o rng_state.pth https://huggingface.co/Azrail/smallm_140_rope/resolve/64c1cb33dbb039ab888013b349c5c04271b1e3d7/last-checkpoint/rng_state.pth
14.2 kB
- Xet hash:
- d2b5e20932b66075641c9cd48f90fd4f7d74c0d45d6ee3db94466984951e0da8
- Size of remote file:
- 14.2 kB
- SHA256:
- a5f9d2ea250bcd3507c62c8571a114db63d14fdd2d31f9df1da7534fe6e55434
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