ONNX Runtime generate() API

Run Llama, Phi, Gemma, Mistral with ONNX Runtime.

This API gives you an easy, flexible and performant way of running LLMs on device.

It implements the generative AI loop for ONNX models, including pre and post processing, inference with ONNX Runtime, logits processing, search and sampling, and KV cache management.

You can call a high level generate() method to generate all of the output at once, or stream the output one token at a time.

See documentation at https://onnxruntime.ai/docs/genai.

Support matrix	Supported now	Under development	On the roadmap
Model architectures	Gemma Llama * Mistral + Phi (language + vision) Qwen Nemotron	Whisper	Stable diffusion
API	Python C# C/C++ Java ^	Objective-C
Platform	Linux Windows Mac ^ Android ^		iOS
Architecture	x86 x64 Arm64 ~
Hardware Acceleration	CUDA DirectML	QNN OpenVINO ROCm
Features		Interactive decoding Customization (fine-tuning)	Speculative decoding

* The Llama model architecture supports similar model families such as CodeLlama, Vicuna, Yi, and more.

+ The Mistral model architecture supports similar model families such as Zephyr.

^ Requires build from source

~ Windows builds available, requires build from source for other platforms

Installation

See https://onnxruntime.ai/docs/genai/howto/install

Sample code for Phi-3 in Python

1. Download the model

   huggingface-cli download microsoft/Phi-3-mini-4k-instruct-onnx --include cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/* --local-dir .

2. Install the API

   pip install numpy
   pip install --pre onnxruntime-genai

3. Run the model

   Build from source / Next release (0.6.0)

   import onnxruntime_genai as og
   
   model = og.Model('cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4')
   tokenizer = og.Tokenizer(model)
   tokenizer_stream = tokenizer.create_stream()
    
   # Set the max length to something sensible by default,
   # since otherwise it will be set to the entire context length
   search_options = {}
   search_options['max_length'] = 2048
   search_options['batch_size'] = 1
   
   chat_template = '<|user|>\n{input} <|end|>\n<|assistant|>'
   
   text = input("Input: ")
   if not text:
      print("Error, input cannot be empty")
      exit
   
   prompt = f'{chat_template.format(input=text)}'
   
   input_tokens = tokenizer.encode(prompt)
   
   params = og.GeneratorParams(model)
   params.set_search_options(**search_options)
   generator = og.Generator(model, params)
   
   print("Output: ", end='', flush=True)
   
   try:
      generator.append_tokens(input_tokens)
      while not generator.is_done():
        generator.generate_next_token()
   
        new_token = generator.get_next_tokens()[0]
        print(tokenizer_stream.decode(new_token), end='', flush=True)
   except KeyboardInterrupt:
       print("  --control+c pressed, aborting generation--")
   
   print()
   del generator

   Current release (until 0.5.x)

   import onnxruntime_genai as og
   
   model = og.Model('cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4')
   tokenizer = og.Tokenizer(model)
   tokenizer_stream = tokenizer.create_stream()
    
   # Set the max length to something sensible by default,
   # since otherwise it will be set to the entire context length
   search_options = {}
   search_options['max_length'] = 2048
   
   chat_template = '<|user|>\n{input} <|end|>\n<|assistant|>'
   
   text = input("Input: ")
   if not text:
      print("Error, input cannot be empty")
      exit
   
   prompt = f'{chat_template.format(input=text)}'
   
   input_tokens = tokenizer.encode(prompt)
   
   params = og.GeneratorParams(model)
   params.set_search_options(**search_options)
   
   generator = og.Generator(model, params)
   generator.append_tokens(input_tokens)
   
   print("Output: ", end='', flush=True)
   
   try:
      while not generator.is_done():
        generator.generate_next_token()
   
        new_token = generator.get_next_tokens()[0]
        print(tokenizer_stream.decode(new_token), end='', flush=True)
   except KeyboardInterrupt:
       print("  --control+c pressed, aborting generation--")
   
   print()
   del generator

Roadmap

See the Discussions to request new features and up-vote existing requests.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.