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Text generation Python multinomial_causal_lm that supports most popular models like LLaMA 3

This example showcases inference of text-generation Large Language Models (LLMs): chatglm, LLaMA, Qwen and other models with the same signature. The application doesn't have many configuration options to encourage the reader to explore and modify the source code. For example, change the device for inference to GPU. The sample fearures ov::genai::LLMPipeline and configures it to run random sampling algorithm. There is also a Jupyter notebook which provides an example of LLM-powered Chatbot in Python.

This sample also contains example implementation of an iterable streamer with bufferisation.

Download and convert the model and tokenizers

The --upgrade-strategy eager option is needed to ensure optimum-intel is upgraded to the latest version.

It's not required to install ../../requirements.txt for deployment if the model has already been exported.

pip install --upgrade-strategy eager -r ../../requirements.txt
optimum-cli export openvino --trust-remote-code --model TinyLlama/TinyLlama-1.1B-Chat-v1.0 TinyLlama-1.1B-Chat-v1.0

Run

multinomial_causal_lm.py TinyLlama-1.1B-Chat-v1.0 "Why is the Sun yellow?"

Discrete GPUs (dGPUs) usually provide better performance compared to CPUs. It is recommended to run larger models on a dGPU with 32GB+ RAM. For example, the model meta-llama/Llama-2-13b-chat-hf can benefit from being run on a dGPU. Modify the source code to change the device for inference to the GPU.

See https://github.com/openvinotoolkit/openvino.genai/blob/master/src/README.md#supported-models for the list of supported models.

Streaming

This Python example demonstrates custom detokenization with bufferization. The streamer receives integer tokens corresponding to each word or subword, one by one. If tokens are decoded individually, the resulting text misses necessary spaces because of detokenize(tokenize(" a")) == "a".

To address this, the detokenizer needs a larger context. We accumulate tokens in a tokens_cache buffer and decode multiple tokens together, adding the text to the streaming queue only when a complete decoded chunk is ready. We run a separate thread to print all new elements arriving in this queue from the generation pipeline. Each generated chunk of text is put into a synchronized queue, ensuring that all put and get operations are thread-safe and blocked until they can proceed.

Troubleshooting

Unicode characters encoding error on Windows

Example error:

UnicodeEncodeError: 'charmap' codec can't encode character '\u25aa' in position 0: character maps to <undefined>

If you encounter the error described in the example when sample is printing output to the Windows console, it is likely due to the default Windows encoding not supporting certain Unicode characters. To resolve this:

  1. Enable Unicode characters for Windows cmd - open Region settings from Control panel. Administrative->Change system locale->Beta: Use Unicode UTF-8 for worldwide language support->OK. Reboot.
  2. Enable UTF-8 mode by setting environment variable PYTHONIOENCODING="utf8".