Offline Inference

To facilitate users in quickly using xLLM for offline inference, we provide Python script examples for launching offline inference.

LLM

LLM inference example: https://github.com/xLLM-AI/xllm/blob/main/examples/generate.py

LLM Beam Search example: https://github.com/xLLM-AI/xllm/blob/main/examples/generate_beam_search.py

Use BeamSearchParams with beam_width greater than 1, then call llm.beam_search(...):

from xllm import BeamSearchParams, LLM

# The devices to run on are selected via ASCEND_RT_VISIBLE_DEVICES
# (or CUDA_VISIBLE_DEVICES / MLU_VISIBLE_DEVICES for other platforms).
llm = LLM(model="/path/models/Qwen2-7B-Instruct")
params = BeamSearchParams(
    beam_width=2,
    top_logprobs=4,
    max_tokens=20,
)

outputs = llm.beam_search(
    [{"prompt": "Hello, my name is "}],
    params=params,
)
print(outputs[0].sequences[0].text)

llm.finish()

For LLM Beam Search, use beam_width as the switch. top_logprobs controls the top-k candidate count used for beam expansion at each decode step. If top_logprobs is left at its default value, xLLM uses beam_width as the top logprob count. Set top_logprobs to a value greater than beam_width when you want each beam to consider more candidate tokens. This beam-search top-k is different from the sampling cutoff parameter top_k. best_of is not the Beam Search switch, and this offline LLM guide does not use num_return_sequences to control the returned beams.

Embedding

Generate embedding example: https://github.com/xLLM-AI/xllm/blob/main/examples/generate_embedding.py

VLM

VLM inference example: https://github.com/xLLM-AI/xllm/blob/main/examples/generate_vlm.py