Instructions to use Cialtion/SimpleTool with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cialtion/SimpleTool with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Cialtion/SimpleTool")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Cialtion/SimpleTool", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Cialtion/SimpleTool with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Cialtion/SimpleTool" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Cialtion/SimpleTool", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Cialtion/SimpleTool
- SGLang
How to use Cialtion/SimpleTool 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 "Cialtion/SimpleTool" \ --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": "Cialtion/SimpleTool", "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 "Cialtion/SimpleTool" \ --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": "Cialtion/SimpleTool", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Cialtion/SimpleTool with Docker Model Runner:
docker model run hf.co/Cialtion/SimpleTool
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| RT-Qwen2.5-14B-AWQ | 14B | ~130ms | [🤗](https://huggingface.co/Cialtion/SimpleTool/tree/main/RT-Qwen2.5-14B-AWQ) | [链接](https://www.modelscope.cn/models/cialtion/SimpleTool/tree/master/RT-Qwen2.5-14B-AWQ) |
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| RT-Qwen3-30B-A3B-AWQ | 30B(A3B) | ~ | [🤗](https://huggingface.co/Cialtion/SimpleTool/tree/main/RT-Qwen3-30B_awq_w4a16) | [链接](https://www.modelscope.cn/models/cialtion/SimpleTool/tree/master/RT-Qwen3-30B_awq_w4a16) |
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> 延迟数据在 RTX 4090 上使用 vLLM 前缀缓存测得。v2 模型采用改进的提示
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| RT-Qwen2.5-14B-AWQ | 14B | ~130ms | [🤗](https://huggingface.co/Cialtion/SimpleTool/tree/main/RT-Qwen2.5-14B-AWQ) | [链接](https://www.modelscope.cn/models/cialtion/SimpleTool/tree/master/RT-Qwen2.5-14B-AWQ) |
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| RT-Qwen3-30B-A3B-AWQ | 30B(A3B) | ~ | [🤗](https://huggingface.co/Cialtion/SimpleTool/tree/main/RT-Qwen3-30B_awq_w4a16) | [链接](https://www.modelscope.cn/models/cialtion/SimpleTool/tree/master/RT-Qwen3-30B_awq_w4a16) |
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> 延迟数据在 RTX 4090 上使用 vLLM 前缀缓存测得。v2 模型采用改进的更干净的提示词;v1 模型使用之前的多头指令头。
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</details>
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