Instructions to use evanarlian/DeepScaleR-1.5B-Preview-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps
- vLLM
How to use evanarlian/DeepScaleR-1.5B-Preview-AWQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "evanarlian/DeepScaleR-1.5B-Preview-AWQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "evanarlian/DeepScaleR-1.5B-Preview-AWQ", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/evanarlian/DeepScaleR-1.5B-Preview-AWQ
- SGLang
How to use evanarlian/DeepScaleR-1.5B-Preview-AWQ 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 "evanarlian/DeepScaleR-1.5B-Preview-AWQ" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "evanarlian/DeepScaleR-1.5B-Preview-AWQ", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "evanarlian/DeepScaleR-1.5B-Preview-AWQ" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "evanarlian/DeepScaleR-1.5B-Preview-AWQ", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use evanarlian/DeepScaleR-1.5B-Preview-AWQ with Docker Model Runner:
docker model run hf.co/evanarlian/DeepScaleR-1.5B-Preview-AWQ
Source: https://huggingface.co/agentica-org/DeepScaleR-1.5B-Preview
quant_config = {
"zero_point": True,
"q_group_size": 128,
"w_bit": 4,
"version": "GEMM",
}
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