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 Settings
- 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
readme
Browse files
README.md
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# SimpleTool: Parallel Decoding for Real-Time LLM Function Calling
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[**Hugging Face**](https://huggingface.co/Cialtion/SimpleTool) | [**ModelScope**](https://www.modelscope.cn/models/cialtion/SimpleTool) | [**GitHub**](https://github.com/HaxxorCialtion/SimpleTool)
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This repository contains the weights for **RT-Qwen** (RealtimeTool), a series of models optimized for low-latency, parallel LLM function calling.
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## 📁 Model Directory Structure
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The models are organized by scale, quantization format, and inference framework.
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### 1. SFT & AWQ Models (vLLM / Transformers)
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Directly use these folders for inference via `vLLM` or `Transformers`.
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* **RT-Qwen2.5-0.5B** / **-0.5B-AWQ**
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* **RT-Qwen2.5-1.5B** / **-1.5B-AWQ**
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* **RT-Qwen2.5-3B** / **-3B-AWQ**
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* **RT-Qwen2.5-7B** / **-7B-AWQ**
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* **RT-Qwen2.5-14B** / **-14B-AWQ**
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* **RT-Qwen3-4B** / **-4B-AWQ**
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* **RT-Qwen3-30B** / **-30B-AWQ**
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### 2. GGUF Models (llama.cpp)
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* **`gguf_models/`**: Full-precision (F16) GGUF files for all versions.
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* **`gguf_quantized/`**: Quantized GGUF versions including **Q4_K_M**, **Q5_K_M**, and **Q8_0**.
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---
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## 📝 TODO
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- [ ] Release Arxiv Paper
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- [ ] Complete GitHub Documentation
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- [ ] Add Performance Benchmarks
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- [ ] Provide Citation Info
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---
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**License**: Apache-2.0
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**Status**: Models Uploading / Placeholder README
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