LLM Examples Introduction
Here is a simple example to show how to use the LLM with TinyLlama.
1from tensorrt_llm import LLM, SamplingParams
2
3
4def main():
5
6 prompts = [
7 "Hello, my name is",
8 "The president of the United States is",
9 "The capital of France is",
10 "The future of AI is",
11 ]
12 sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
13
14 llm = LLM(model="TinyLlama/TinyLlama-1.1B-Chat-v1.0")
15
16 outputs = llm.generate(prompts, sampling_params)
17
18 # Print the outputs.
19 for output in outputs:
20 prompt = output.prompt
21 generated_text = output.outputs[0].text
22 print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
23
24
25# The entry point of the program need to be protected for spawning processes.
26if __name__ == '__main__':
27 main()
The LLM API can be used for both offline or online usage. See more examples of the LLM API here:
LLM API Examples
- Generate Text Asynchronously
- Generate Text in Streaming
- Distributed LLM Generation
- Control generated text using logits post processor
- Generate text with multiple LoRA adapters
- Generate Text Using Lookahead Decoding
- Generate text with guided decoding
- Generate text
- Generate text with customization
- Generate Text Using Medusa Decoding
- Generation with Quantization
- Automatic Parallelism with LLM
For more details on how to fully utilize this API, check out: