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.

* 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.

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

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

   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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