Instructions to use raxtemur/sonar-llm-300m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raxtemur/sonar-llm-300m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="raxtemur/sonar-llm-300m")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("raxtemur/sonar-llm-300m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use raxtemur/sonar-llm-300m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "raxtemur/sonar-llm-300m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "raxtemur/sonar-llm-300m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/raxtemur/sonar-llm-300m
- SGLang
How to use raxtemur/sonar-llm-300m 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 "raxtemur/sonar-llm-300m" \ --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": "raxtemur/sonar-llm-300m", "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 "raxtemur/sonar-llm-300m" \ --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": "raxtemur/sonar-llm-300m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use raxtemur/sonar-llm-300m with Docker Model Runner:
docker model run hf.co/raxtemur/sonar-llm-300m
Download pytorch_model.bin from raxtemur/sonar-llm-300m: direct link, hf CLI and curl.
- Browser
- Download file 1.2 GB
-
https://huggingface.co/raxtemur/sonar-llm-300m/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://raxtemur/sonar-llm-300m/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/raxtemur/sonar-llm-300m/resolve/main/pytorch_model.bin
1.2 GB
- Xet hash:
- 86a369e5c35736dc4fa1edc1c5b7fcd32d2bd0617f6220e16ee078ce7523ed84
- Size of remote file:
- 1.2 GB
- SHA256:
- 99730076a888df2aa898d4655963268e11048e06021854d9a2944e46ae7ee21f
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