### Generate text with customization import tempfile from tensorrt_llm._tensorrt_engine import LLM from tensorrt_llm.llmapi import BuildConfig, KvCacheConfig, SamplingParams def main(): # The end user can customize the build configuration with the build_config class and other arguments borrowed from the lower-level APIs build_config = BuildConfig() build_config.max_batch_size = 128 build_config.max_num_tokens = 2048 build_config.max_beam_width = 4 # Model could accept HF model name or a path to local HF model. llm = LLM( model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", build_config=build_config, kv_cache_config=KvCacheConfig( free_gpu_memory_fraction=0.8 ), # Similar to `build_config`, you can also customize the runtime configuration with the `kv_cache_config`, `runtime_config`, `peft_cache_config` or \ # other arguments borrowed from the lower-level APIs. ) # You can save the engine to disk and load it back later, the LLM class can accept either a HF model or a TRT-LLM engine. llm.save(tempfile.mkdtemp()) # Sample prompts. prompts = [ "Hello, my name is", "The president of the United States is", "The capital of France is", "The future of AI is", ] # With SamplingParams, you can customize the sampling strategy, such as beam search, temperature, and so on. sampling_params = SamplingParams(temperature=0.8, top_p=0.95, n=4, use_beam_search=True) for output in llm.generate(prompts, sampling_params): print( f"Prompt: {output.prompt!r}, Generated text: {output.outputs[0].text!r}" ) # Got output like # Prompt: 'Hello, my name is', Generated text: '\n\nJane Smith. I am a student pursuing my degree in Computer Science at [university]. I enjoy learning new things, especially technology and programming' # Prompt: 'The president of the United States is', Generated text: 'likely to nominate a new Supreme Court justice to fill the seat vacated by the death of Antonin Scalia. The Senate should vote to confirm the' # Prompt: 'The capital of France is', Generated text: 'Paris.' # Prompt: 'The future of AI is', Generated text: 'an exciting time for us. We are constantly researching, developing, and improving our platform to create the most advanced and efficient model available. We are' if __name__ == '__main__': main()