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qwen3-coder
Instructions to use MrMoz33/tokioai-coder-iot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MrMoz33/tokioai-coder-iot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MrMoz33/tokioai-coder-iot")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MrMoz33/tokioai-coder-iot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MrMoz33/tokioai-coder-iot with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MrMoz33/tokioai-coder-iot" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MrMoz33/tokioai-coder-iot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MrMoz33/tokioai-coder-iot
- SGLang
How to use MrMoz33/tokioai-coder-iot 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 "MrMoz33/tokioai-coder-iot" \ --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": "MrMoz33/tokioai-coder-iot", "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 "MrMoz33/tokioai-coder-iot" \ --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": "MrMoz33/tokioai-coder-iot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MrMoz33/tokioai-coder-iot with Docker Model Runner:
docker model run hf.co/MrMoz33/tokioai-coder-iot
File size: 1,036 Bytes
4b97c3b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | #!/usr/bin/env python3
"""
TokioAI Engine -- Custom Tools Example
Shows how to add your own tools to the engine.
Modify engine/schemas.py to add tool definitions,
or pass them through the API.
"""
from engine.pipeline import Pipeline, EngineConfig
from engine.schemas import TOOL_DEFINITIONS
# You can inspect the built-in tools
print("Built-in tools:")
for tool in TOOL_DEFINITIONS:
name = tool["function"]["name"]
desc = tool["function"]["description"][:60]
print(f" {name:20s} -- {desc}")
print()
# Use the engine normally -- it already knows all TokioAI tools
config = EngineConfig(
model_name="qwen3-coder",
verbose=True,
)
engine = Pipeline(config)
# The engine's router will classify this as tool_call
# and the guided generator will constrain output to valid tool names
result = engine.process("search for TODO comments in all python files")
print(f"\nResult: {result}")
if result.tool_calls:
print(f"Tool: {result.tool_calls[0]['name']}")
print(f"Args: {result.tool_calls[0]['arguments']}")
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