Skywork-SWE-32B
124
77
license:apache-2.0
by
Skywork
Language Model
OTHER
32B params
New
124 downloads
Early-stage
Edge AI:
Mobile
Laptop
Server
72GB+ RAM
Mobile
Laptop
Server
Quick Summary
AI model with specialized capabilities.
Device Compatibility
Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
30GB+ RAM
Code Examples
Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Usagetextvllm
# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Launch a server to deploy Skywork-SWE-32Btextvllm
vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8Deploy This Model
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