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* beam_width and max_new_token Signed-off-by: Superjomn <328693+Superjomn@users.noreply.github.com> * remove beam_width Signed-off-by: Superjomn <328693+Superjomn@users.noreply.github.com> * remove min_length Signed-off-by: Superjomn <328693+Superjomn@users.noreply.github.com> * remove return_num_sequences Signed-off-by: Superjomn <328693+Superjomn@users.noreply.github.com> Signed-off-by: Superjomn <328693+Superjomn@users.noreply.github.com> Signed-off-by: Superjomn <328693+Superjomn@users.noreply.github.com> Signed-off-by: Superjomn <328693+Superjomn@users.noreply.github.com> Signed-off-by: Superjomn <328693+Superjomn@users.noreply.github.com> Signed-off-by: Superjomn <328693+Superjomn@users.noreply.github.com> --------- Signed-off-by: Superjomn <328693+Superjomn@users.noreply.github.com>
57 lines
2.5 KiB
Python
57 lines
2.5 KiB
Python
### Generate text with customization
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import tempfile
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from tensorrt_llm.llmapi import LLM, BuildConfig, KvCacheConfig, SamplingParams
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def main():
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# The end user can customize the build configuration with the build_config class and other arguments borrowed from the lower-level APIs
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build_config = BuildConfig()
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build_config.max_batch_size = 128
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build_config.max_num_tokens = 2048
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build_config.max_beam_width = 4
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# Model could accept HF model name or a path to local HF model.
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llm = LLM(
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model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
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build_config=build_config,
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kv_cache_config=KvCacheConfig(
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free_gpu_memory_fraction=0.8
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), # Similar to `build_config`, you can also customize the runtime configuration with the `kv_cache_config`, `runtime_config`, `peft_cache_config` or \
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# other arguments borrowed from the lower-level APIs.
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)
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# 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.
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llm.save(tempfile.mkdtemp())
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# Sample prompts.
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prompts = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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]
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# With SamplingParams, you can customize the sampling strategy, such as beam search, temperature, and so on.
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sampling_params = SamplingParams(temperature=0.8,
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top_p=0.95,
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n=4,
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use_beam_search=True)
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for output in llm.generate(prompts, sampling_params):
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print(
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f"Prompt: {output.prompt!r}, Generated text: {output.outputs[0].text!r}"
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)
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# Got output like
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# 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'
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# 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'
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# Prompt: 'The capital of France is', Generated text: 'Paris.'
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# 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'
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if __name__ == '__main__':
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main()
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