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
See https://onnxruntime.ai/docs/genai/howto/install
-
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 .
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Install the API
pip install numpy pip install --pre onnxruntime-genai
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Run the model
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) params.input_ids = input_tokens generator = og.Generator(model, params) print("Output: ", end='', flush=True) try: while not generator.is_done(): generator.compute_logits() 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
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