Multimodal¶

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
This example shows how to use vLLM for running offline inference
with the correct prompt format on audio language models.

For most models, the prompt format should follow corresponding examples
on HuggingFace model repository.
"""

import os
from typing import Any, NamedTuple

from huggingface_hub import snapshot_download
from transformers import AutoTokenizer

from vllm import LLM, EngineArgs, SamplingParams
from vllm.assets.audio import AudioAsset
from vllm.lora.request import LoRARequest
from vllm.utils.argparse_utils import FlexibleArgumentParser

audio_assets = [AudioAsset("mary_had_lamb"), AudioAsset("winning_call")]
question_per_audio_count = {
    0: "What is 1+1?",
    1: "What is recited in the audio?",
    2: "What sport and what nursery rhyme are referenced?",
}


class ModelRequestData(NamedTuple):
    engine_args: EngineArgs
    prompt: str | None = None
    prompt_token_ids: dict[str, list[int]] | None = None
    multi_modal_data: dict[str, Any] | None = None
    stop_token_ids: list[int] | None = None
    lora_requests: list[LoRARequest] | None = None


# NOTE: The default `max_num_seqs` and `max_model_len` may result in OOM on
# lower-end GPUs.
# Unless specified, these settings have been tested to work on a single L4.


# AudioFlamingo3
def run_audioflamingo3(question: str, audio_count: int) -> ModelRequestData:
    model_name = "nvidia/audio-flamingo-3-hf"
    engine_args = EngineArgs(
        model=model_name,
        max_model_len=4096,
        max_num_seqs=2,
        limit_mm_per_prompt={"audio": audio_count},
        enforce_eager=True,
    )

    # AudioFlamingo3 uses <sound> token for audio
    audio_placeholder = "<sound>" * audio_count

    prompt = (
        "<|im_start|>system\n"
        "You are a helpful assistant.<|im_end|>\n"
        "<|im_start|>user\n"
        f"{audio_placeholder}{question}<|im_end|>\n"
        "<|im_start|>assistant\n"
    )

    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompt,
    )


# CohereASR
def run_cohere_asr(question: str, audio_count: int) -> ModelRequestData:
    assert audio_count == 1, "CohereASR only support single audio input per prompt"
    model_name = "CohereLabs/cohere-transcribe-03-2026"

    prompt = (
        "<|startofcontext|><|startoftranscript|>"
        "<|emo:undefined|><|en|><|en|><|pnc|><|noitn|>"
        "<|notimestamp|><|nodiarize|>"
    )
    engine_args = EngineArgs(
        model=model_name,
        limit_mm_per_prompt={"audio": audio_count},
        trust_remote_code=True,
    )

    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompt,
    )


# MusicFlamingo
def run_musicflamingo(question: str, audio_count: int) -> ModelRequestData:
    model_name = "nvidia/music-flamingo-2601-hf"
    engine_args = EngineArgs(
        model=model_name,
        max_model_len=4096,
        max_num_seqs=2,
        limit_mm_per_prompt={"audio": audio_count},
        enforce_eager=True,
    )

    # MusicFlamingo prompt placeholders use <sound>; vLLM's MusicFlamingo
    # multimodal processor expands each one into <|sound_bos|> + audio tokens +
    # <|sound_eos|> based on extracted audio feature lengths.
    audio_placeholder = "<sound>" * audio_count
    system_prompt = (
        "You are Music Flamingo, a multimodal assistant for language and music. "
        "On each turn you receive an audio clip which contains music and optional "
        "text, you will receive at least one or both; use your world knowledge and "
        "reasoning to help the user with any task. Interpret the entirety of the "
        "content any input music--regardlenss of whether the user calls it audio, "
        "music, or sound."
    )

    prompt = (
        "<|im_start|>system\n"
        f"{system_prompt}<|im_end|>\n"
        "<|im_start|>user\n"
        f"{audio_placeholder}{question}<|im_end|>\n"
        "<|im_start|>assistant\n"
    )

    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompt,
    )


# Gemma3N
def run_gemma3n(question: str, audio_count: int) -> ModelRequestData:
    model_name = "google/gemma-3n-E2B-it"
    engine_args = EngineArgs(
        model=model_name,
        max_model_len=2048,
        max_num_batched_tokens=2048,
        max_num_seqs=2,
        limit_mm_per_prompt={"audio": audio_count},
        enforce_eager=True,
    )
    prompt = f"<start_of_turn>user\n<audio_soft_token>{question}"
    "<end_of_turn>\n<start_of_turn>model\n"
    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompt,
    )


# GLM-ASR
def run_glmasr(question: str, audio_count: int) -> ModelRequestData:
    model_name = "zai-org/GLM-ASR-Nano-2512"

    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

    # GLM-ASR uses <|pad|> token for audio
    audio_placeholder = "<|pad|>" * audio_count

    messages = [{"role": "user", "content": f"{audio_placeholder}{question}"}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

    engine_args = EngineArgs(
        model=model_name,
        trust_remote_code=True,
        max_model_len=4096,
        max_num_seqs=2,
        limit_mm_per_prompt={"audio": audio_count},
    )

    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompt,
    )


# FunAudioChat
def run_funaudiochat(question: str, audio_count: int) -> ModelRequestData:
    # NOTE: FunAudioChat is not available on the HuggingFace Hub at the time of
    # writing. Pass a local model path via `--model`.
    model_name = "funaudiochat"

    engine_args = EngineArgs(
        model=model_name,
        max_model_len=4096,
        max_num_seqs=2,
        limit_mm_per_prompt={"audio": audio_count},
        enforce_eager=True,
    )

    audio_in_prompt = "".join(
        ["<|audio_bos|><|AUDIO|><|audio_eos|>\n" for _ in range(audio_count)]
    )
    prompt = f"{audio_in_prompt}{question}"

    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompt,
    )


# Granite Speech
def run_granite_speech(question: str, audio_count: int) -> ModelRequestData:
    # NOTE - the setting in this example are somewhat different from what is
    # optimal for granite speech, and it is generally recommended to use beam
    # search. Check the model README for suggested settings.
    # https://huggingface.co/ibm-granite/granite-speech-3.3-8b
    model_name = "ibm-granite/granite-speech-3.3-8b"

    engine_args = EngineArgs(
        model=model_name,
        trust_remote_code=True,
        max_model_len=2048,
        max_num_seqs=2,
        enable_lora=True,
        max_lora_rank=64,
        limit_mm_per_prompt={"audio": audio_count},
    )

    # The model has an audio-specific lora directly in its model dir;
    # it should be enabled whenever you pass audio inputs to the model.
    speech_lora_path = model_name
    audio_placeholder = "<|audio|>" * audio_count
    prompts = f"<|start_of_role|>system<|end_of_role|>Knowledge Cutoff Date: April 2024.\nToday's Date: December 19, 2024.\nYou are Granite, developed by IBM. You are a helpful AI assistant<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>{audio_placeholder}{question}<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>"  # noqa: E501

    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompts,
        lora_requests=[LoRARequest("speech", 1, speech_lora_path)],
    )


# Kimi-Audio-7B-Instruct
def run_kimi_audio(question: str, audio_count: int) -> ModelRequestData:
    """Kimi-Audio-7B-Instruct for audio transcription and understanding."""
    model_name = "moonshotai/Kimi-Audio-7B-Instruct"

    engine_args = EngineArgs(
        model=model_name,
        trust_remote_code=True,
        max_model_len=4096,
        max_num_seqs=2,
        limit_mm_per_prompt={"audio": audio_count},
    )

    # Kimi-Audio uses <|im_kimia_text_blank|> as placeholder for audio features
    audio_placeholder = "<|im_kimia_text_blank|>" * audio_count
    # Default prompt for transcription
    if not question:
        question = "Please transcribe the audio"
    prompt = f"{audio_placeholder}{question}"

    # Stop at EOS token (151644) to prevent repetition
    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompt,
        stop_token_ids=[151644],
    )


# MiDashengLM
def run_midashenglm(question: str, audio_count: int):
    model_name = "mispeech/midashenglm-7b"

    engine_args = EngineArgs(
        model=model_name,
        trust_remote_code=True,
        max_model_len=4096,
        max_num_seqs=5,
        limit_mm_per_prompt={"audio": audio_count},
    )

    audio_in_prompt = "".join(
        ["<|audio_bos|><|AUDIO|><|audio_eos|>" for idx in range(audio_count)]
    )

    default_system = "You are a helpful language and speech assistant."

    prompt = (
        f"<|im_start|>system\n{default_system}<|im_end|>\n"
        "<|im_start|>user\n"
        f"{audio_in_prompt}{question}<|im_end|>\n"
        "<|im_start|>assistant\n"
    )
    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompt,
    )


# MiniCPM-O
def run_minicpmo(question: str, audio_count: int) -> ModelRequestData:
    model_name = "openbmb/MiniCPM-o-2_6"
    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
    engine_args = EngineArgs(
        model=model_name,
        trust_remote_code=True,
        max_model_len=4096,
        max_num_seqs=2,
        limit_mm_per_prompt={"audio": audio_count},
    )

    stop_tokens = ["<|im_end|>", "<|endoftext|>"]
    stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens]

    audio_placeholder = "(<audio>./</audio>)" * audio_count
    audio_chat_template = "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n<|spk_bos|><|spk|><|spk_eos|><|tts_bos|>' }}{% endif %}"  # noqa: E501
    messages = [{"role": "user", "content": f"{audio_placeholder}\n{question}"}]
    prompt = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True,
        chat_template=audio_chat_template,
    )

    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompt,
        stop_token_ids=stop_token_ids,
    )


# Phi-4-multimodal-instruct
def run_phi4mm(question: str, audio_count: int) -> ModelRequestData:
    """
    Phi-4-multimodal-instruct supports both image and audio inputs. Here, we
    show how to process audio inputs.
    """
    model_path = snapshot_download("microsoft/Phi-4-multimodal-instruct")
    # Since the vision-lora and speech-lora co-exist with the base model,
    # we have to manually specify the path of the lora weights.
    speech_lora_path = os.path.join(model_path, "speech-lora")
    placeholders = "".join([f"<|audio_{i + 1}|>" for i in range(audio_count)])

    prompts = f"<|user|>{placeholders}{question}<|end|><|assistant|>"

    engine_args = EngineArgs(
        model=model_path,
        trust_remote_code=True,
        max_model_len=12800,
        max_num_seqs=2,
        enable_lora=True,
        max_lora_rank=320,
        limit_mm_per_prompt={"audio": audio_count},
    )

    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompts,
        lora_requests=[LoRARequest("speech", 1, speech_lora_path)],
    )


# Qwen2-Audio
def run_qwen2_audio(question: str, audio_count: int) -> ModelRequestData:
    model_name = "Qwen/Qwen2-Audio-7B-Instruct"

    engine_args = EngineArgs(
        model=model_name,
        max_model_len=4096,
        max_num_seqs=5,
        limit_mm_per_prompt={"audio": audio_count},
    )

    audio_in_prompt = "".join(
        [
            f"Audio {idx + 1}: <|audio_bos|><|AUDIO|><|audio_eos|>\n"
            for idx in range(audio_count)
        ]
    )

    prompt = (
        "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
        "<|im_start|>user\n"
        f"{audio_in_prompt}{question}<|im_end|>\n"
        "<|im_start|>assistant\n"
    )

    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompt,
    )


# Qwen2.5-Omni
def run_qwen2_5_omni(question: str, audio_count: int):
    model_name = "Qwen/Qwen2.5-Omni-7B"

    engine_args = EngineArgs(
        model=model_name,
        max_model_len=4096,
        max_num_seqs=5,
        limit_mm_per_prompt={"audio": audio_count},
    )

    audio_in_prompt = "".join(
        ["<|audio_bos|><|AUDIO|><|audio_eos|>\n" for idx in range(audio_count)]
    )

    default_system = (
        "You are Qwen, a virtual human developed by the Qwen Team, Alibaba "
        "Group, capable of perceiving auditory and visual inputs, as well as "
        "generating text and speech."
    )

    prompt = (
        f"<|im_start|>system\n{default_system}<|im_end|>\n"
        "<|im_start|>user\n"
        f"{audio_in_prompt}{question}<|im_end|>\n"
        "<|im_start|>assistant\n"
    )
    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompt,
    )


def run_qwen3_asr(question: str, audio_count: int) -> ModelRequestData:
    model_name = "Qwen/Qwen3-Asr-1.7B"

    audio_in_prompt = "<|audio_start|><|audio_pad|><|audio_end|>\n" * audio_count
    prompt = f"<|im_start|>user\n{audio_in_prompt}<|im_end|>\n<|im_start|>assistant\n"

    engine_args = EngineArgs(
        model=model_name,
        max_model_len=4096,
        max_num_seqs=5,
        limit_mm_per_prompt={"audio": audio_count},
    )

    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompt,
    )


# Ultravox 0.5-1B
def run_ultravox(question: str, audio_count: int) -> ModelRequestData:
    model_name = "fixie-ai/ultravox-v0_5-llama-3_2-1b"

    tokenizer = AutoTokenizer.from_pretrained(model_name)
    messages = [{"role": "user", "content": "<|audio|>\n" * audio_count + question}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

    engine_args = EngineArgs(
        model=model_name,
        max_model_len=4096,
        max_num_seqs=5,
        trust_remote_code=True,
        limit_mm_per_prompt={"audio": audio_count},
    )

    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompt,
    )


# Voxtral
# Make sure to install mistral-common[audio].
def run_voxtral(question: str, audio_count: int) -> ModelRequestData:
    from mistral_common.audio import Audio
    from mistral_common.protocol.instruct.chunk import (
        AudioChunk,
        RawAudio,
        TextChunk,
    )
    from mistral_common.protocol.instruct.messages import (
        UserMessage,
    )
    from mistral_common.protocol.instruct.request import ChatCompletionRequest
    from mistral_common.tokens.tokenizers.mistral import MistralTokenizer

    model_name = "mistralai/Voxtral-Mini-3B-2507"
    tokenizer = MistralTokenizer.from_hf_hub(model_name)

    engine_args = EngineArgs(
        model=model_name,
        max_model_len=8192,
        max_num_seqs=2,
        limit_mm_per_prompt={"audio": audio_count},
        config_format="mistral",
        load_format="mistral",
        tokenizer_mode="mistral",
        enforce_eager=True,
        enable_chunked_prefill=False,
    )

    text_chunk = TextChunk(text=question)
    audios = [
        Audio.from_file(str(audio_assets[i].get_local_path()), strict=False)
        for i in range(audio_count)
    ]
    audio_chunks = [
        AudioChunk(input_audio=RawAudio.from_audio(audio)) for audio in audios
    ]

    messages = [UserMessage(content=[*audio_chunks, text_chunk])]

    req = ChatCompletionRequest(messages=messages, model=model_name)

    tokens = tokenizer.encode_chat_completion(req)
    prompt_ids, audios = tokens.tokens, tokens.audios

    audios_and_sr = [(au.audio_array, au.sampling_rate) for au in audios]

    multi_modal_data = {"audio": audios_and_sr}

    return ModelRequestData(
        engine_args=engine_args,
        prompt_token_ids=prompt_ids,
        multi_modal_data=multi_modal_data,
    )


# Whisper
def run_whisper(question: str, audio_count: int) -> ModelRequestData:
    assert audio_count == 1, "Whisper only support single audio input per prompt"
    model_name = "openai/whisper-large-v3-turbo"

    prompt = "<|startoftranscript|>"

    engine_args = EngineArgs(
        model=model_name,
        max_model_len=448,
        max_num_seqs=5,
        limit_mm_per_prompt={"audio": audio_count},
    )

    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompt,
    )


# FireRedLID
def run_fireredlid(question: str, audio_count: int) -> ModelRequestData:
    assert audio_count == 1, "FireRedLID only supports single audio input per prompt"
    model_name = "PatchyTisa/FireRedLID-vllm"

    prompt = "<sos>"

    engine_args = EngineArgs(
        model=model_name,
        max_model_len=8,
        max_num_seqs=5,
        limit_mm_per_prompt={"audio": audio_count},
    )

    return ModelRequestData(
        engine_args=engine_args,
        prompt=prompt,
    )


model_example_map = {
    "audioflamingo3": run_audioflamingo3,
    "cohere_asr": run_cohere_asr,
    "fireredlid": run_fireredlid,
    "funaudiochat": run_funaudiochat,
    "gemma3n": run_gemma3n,
    "glmasr": run_glmasr,
    "granite_speech": run_granite_speech,
    "kimi_audio": run_kimi_audio,
    "midashenglm": run_midashenglm,
    "minicpmo": run_minicpmo,
    "musicflamingo": run_musicflamingo,
    "phi4_mm": run_phi4mm,
    "qwen2_audio": run_qwen2_audio,
    "qwen2_5_omni": run_qwen2_5_omni,
    "qwen3_asr": run_qwen3_asr,
    "ultravox": run_ultravox,
    "voxtral": run_voxtral,
    "whisper": run_whisper,
}


def parse_args():
    parser = FlexibleArgumentParser(
        description="Demo on using vLLM for offline inference with "
        "audio language models"
    )
    parser.add_argument(
        "--model-type",
        "-m",
        type=str,
        default="ultravox",
        choices=model_example_map.keys(),
        help='Huggingface "model_type".',
    )
    parser.add_argument(
        "--model",
        type=str,
        default=None,
        help="Model ID or local path override. Required for funaudiochat.",
    )
    parser.add_argument(
        "--num-prompts", type=int, default=1, help="Number of prompts to run."
    )
    parser.add_argument(
        "--num-audios",
        type=int,
        default=1,
        choices=[0, 1, 2],
        help="Number of audio items per prompt.",
    )
    parser.add_argument(
        "--seed",
        type=int,
        default=0,
        help="Set the seed when initializing `vllm.LLM`.",
    )
    parser.add_argument(
        "--tensor-parallel-size",
        "-tp",
        type=int,
        default=None,
        help="Tensor parallel size to override the model's default setting. ",
    )

    return parser.parse_args()


def main(args):
    model = args.model_type
    if model not in model_example_map:
        raise ValueError(f"Model type {model} is not supported.")

    if model == "funaudiochat" and not args.model:
        raise ValueError("--model is required when --model-type=funaudiochat")

    if args.tensor_parallel_size is not None and args.tensor_parallel_size < 1:
        raise ValueError(
            f"tensor_parallel_size must be a positive integer, "
            f"got {args.tensor_parallel_size}"
        )

    audio_count = args.num_audios
    req_data = model_example_map[model](
        question_per_audio_count[audio_count], audio_count
    )
    if model == "funaudiochat":
        req_data.engine_args.model = args.model

    # Disable other modalities to save memory
    default_limits = {"image": 0, "video": 0, "audio": 0}
    req_data.engine_args.limit_mm_per_prompt = default_limits | dict(
        req_data.engine_args.limit_mm_per_prompt or {}
    )

    engine_args = vars(req_data.engine_args) | {"seed": args.seed}
    if args.tensor_parallel_size is not None:
        engine_args["tensor_parallel_size"] = args.tensor_parallel_size
    llm = LLM(**engine_args)

    # We set temperature to 0.2 so that outputs can be different
    # even when all prompts are identical when running batch inference.
    sampling_params = SamplingParams(
        temperature=0.2, max_tokens=64, stop_token_ids=req_data.stop_token_ids
    )

    def get_input(start, end):
        mm_data = req_data.multi_modal_data
        if not mm_data:
            mm_data = {}
            if end - start > 0:
                mm_data = {
                    "audio": [
                        asset.audio_and_sample_rate for asset in audio_assets[start:end]
                    ]
                }

        inputs = {"multi_modal_data": mm_data}

        if req_data.prompt:
            inputs["prompt"] = req_data.prompt
        else:
            inputs["prompt_token_ids"] = req_data.prompt_token_ids

        return inputs

    # Batch inference
    assert args.num_prompts > 0
    if audio_count != 1:
        inputs = get_input(0, audio_count)
        inputs = [inputs] * args.num_prompts
    else:
        # For single audio input, we need to vary the audio input
        # to avoid deduplication in vLLM engine.
        inputs = []
        for i in range(args.num_prompts):
            start = i % len(audio_assets)
            inp = get_input(start, start + 1)
            inputs.append(inp)

    # Add LoRA request if applicable
    lora_request = (
        req_data.lora_requests * args.num_prompts if req_data.lora_requests else None
    )

    outputs = llm.generate(
        inputs,
        sampling_params=sampling_params,
        lora_request=lora_request,
    )

    for o in outputs:
        generated_text = o.outputs[0].text
        print(generated_text)


if __name__ == "__main__":
    args = parse_args()
    main(args)

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
This example shows how to use vLLM for running offline inference with
the explicit/implicit prompt format on enc-dec LMMs for text generation.
"""

import os
import time
from collections.abc import Sequence
from typing import NamedTuple

from vllm import LLM, EngineArgs, PromptType, SamplingParams
from vllm.assets.audio import AudioAsset
from vllm.utils.argparse_utils import FlexibleArgumentParser


class ModelRequestData(NamedTuple):
    engine_args: EngineArgs
    prompts: Sequence[PromptType]


def run_whisper():
    os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"

    engine_args = EngineArgs(
        model="openai/whisper-large-v3-turbo",
        max_model_len=448,
        max_num_seqs=16,
        limit_mm_per_prompt={"audio": 1},
        dtype="half",
    )

    prompts = [
        {  # Test implicit prompt
            "prompt": "<|startoftranscript|>",
            "multi_modal_data": {
                "audio": AudioAsset("mary_had_lamb").audio_and_sample_rate,
            },
        },
        {  # Test explicit encoder/decoder prompt
            "encoder_prompt": {
                "prompt": "",
                "multi_modal_data": {
                    "audio": AudioAsset("winning_call").audio_and_sample_rate,
                },
            },
            "decoder_prompt": "<|startoftranscript|>",
        },
    ]

    return ModelRequestData(
        engine_args=engine_args,
        prompts=prompts,
    )


def run_fireredasr2():
    """
    FireRedASR2 – Automatic Speech Recognition model.

    This model uses a Conformer encoder + Qwen2 LLM decoder architecture
    for speech-to-text transcription.  Audio is passed via the implicit
    prompt format with the ``<|AUDIO|>`` placeholder token.
    """
    engine_args = EngineArgs(
        model="allendou/FireRedASR2-LLM-vllm",
        max_model_len=448,
        max_num_seqs=16,
        limit_mm_per_prompt={"audio": 1},
    )

    prompt_str = (
        "<|im_start|>user\n<|AUDIO|>请转写音频为文字<|im_end|>\n<|im_start|>assistant\n"
    )

    prompts = [
        {  # Implicit prompt with audio
            "prompt": prompt_str,
            "multi_modal_data": {
                "audio": AudioAsset("mary_had_lamb").audio_and_sample_rate,
            },
        },
        {  # Another audio sample
            "prompt": prompt_str,
            "multi_modal_data": {
                "audio": AudioAsset("winning_call").audio_and_sample_rate,
            },
        },
    ]

    return ModelRequestData(
        engine_args=engine_args,
        prompts=prompts,
    )


def run_fireredlid():
    """
    FireRedLID – Language Identification model.

    This encoder-decoder model identifies the spoken language of an audio
    clip. It outputs at most 2 tokens representing the detected language
    (e.g. "en", "zh mandarin").
    """
    engine_args = EngineArgs(
        model="PatchyTisa/FireRedLID-vllm",
        max_model_len=8,
        max_num_seqs=16,
        limit_mm_per_prompt={"audio": 1},
    )

    prompts = [
        {  # Test explicit encoder/decoder prompt
            "encoder_prompt": {
                "prompt": "",
                "multi_modal_data": {
                    "audio": AudioAsset("mary_had_lamb").audio_and_sample_rate,
                },
            },
            "decoder_prompt": "<sos>",
        },
        {  # Another audio sample
            "encoder_prompt": {
                "prompt": "",
                "multi_modal_data": {
                    "audio": AudioAsset("winning_call").audio_and_sample_rate,
                },
            },
            "decoder_prompt": "<sos>",
        },
    ]

    return ModelRequestData(
        engine_args=engine_args,
        prompts=prompts,
    )


model_example_map = {
    "fireredasr2": run_fireredasr2,
    "fireredlid": run_fireredlid,
    "whisper": run_whisper,
}


def parse_args():
    parser = FlexibleArgumentParser(
        description="Demo on using vLLM for offline inference with "
        "vision language models for text generation"
    )
    parser.add_argument(
        "--model-type",
        "-m",
        type=str,
        default="whisper",
        choices=model_example_map.keys(),
        help='Huggingface "model_type".',
    )
    parser.add_argument(
        "--seed",
        type=int,
        default=0,
        help="Set the seed when initializing `vllm.LLM`.",
    )
    return parser.parse_args()


def main(args):
    model = args.model_type
    if model not in model_example_map:
        raise ValueError(f"Model type {model} is not supported.")

    req_data = model_example_map[model]()

    # Disable other modalities to save memory
    engine_args = req_data.engine_args
    default_limits = {"image": 0, "video": 0, "audio": 0}
    limit_mm_per_prompt = default_limits | (engine_args.limit_mm_per_prompt or {})
    engine_args.limit_mm_per_prompt = limit_mm_per_prompt
    engine_args.seed = args.seed
    llm = LLM.from_engine_args(engine_args)

    prompts = req_data.prompts

    # Create a sampling params object.
    sampling_params = SamplingParams(
        temperature=0,
        top_p=1.0,
        max_tokens=64,
        skip_special_tokens=False,
    )

    start = time.time()

    # Generate output tokens from the prompts. The output is a list of
    # RequestOutput objects that contain the prompt, generated
    # text, and other information.
    outputs = llm.generate(prompts, sampling_params)

    # Print the outputs.
    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Decoder prompt: {prompt!r}, Generated text: {generated_text!r}")

    duration = time.time() - start

    print("Duration:", duration)
    print("RPS:", len(prompts) / duration)


if __name__ == "__main__":
    args = parse_args()
    main(args)

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

# ruff: noqa
import argparse

from vllm import LLM
from vllm.sampling_params import SamplingParams
from vllm.assets.image import ImageAsset
from vllm.multimodal.utils import encode_image_url

# This script is an offline demo for running Mistral-Small-3.1
#
# If you want to run a server/client setup, please follow this code:
#
# - Server:
#
# ```bash
# # Mistral format
# vllm serve mistralai/Mistral-Small-3.1-24B-Instruct-2503 \
#   --tokenizer-mode mistral --config-format mistral --load-format mistral \
#   --limit-mm-per-prompt.image 4 --max-model-len 16384
#
# # HF format
# vllm serve mistralai/Mistral-Small-3.1-24B-Instruct-2503 \
#   --limit-mm-per-prompt.image 4 --max-model-len 16384
# ```
#
# - Client:
#
# ```bash
# curl --location 'http://<your-node-url>:8000/v1/chat/completions' \
# --header 'Content-Type: application/json' \
# --header 'Authorization: Bearer token' \
# --data '{
#     "model": "mistralai/Mistral-Small-3.1-24B-Instruct-2503",
#     "messages": [
#       {
#         "role": "user",
#         "content": [
#             {"type" : "text", "text": "Describe this image in detail please."},
#             {"type": "image_url", "image_url": {"url": "https://s3.amazonaws.com/cms.ipressroom.com/338/files/201808/5b894ee1a138352221103195_A680%7Ejogging-edit/A680%7Ejogging-edit_hero.jpg"}},
#             {"type" : "text", "text": "and this one as well. Answer in French."},
#             {"type": "image_url", "image_url": {"url": "https://www.wolframcloud.com/obj/resourcesystem/images/a0e/a0ee3983-46c6-4c92-b85d-059044639928/6af8cfb971db031b.png"}}
#         ]
#       }
#     ]
#   }'
# ```
#
# Usage:
#     python demo.py simple
#     python demo.py advanced

# Lower max_model_len and/or max_num_seqs on low-VRAM GPUs.
# These scripts have been tested on 2x L40 GPUs


def run_simple_demo(args: argparse.Namespace):
    model_name = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
    sampling_params = SamplingParams(max_tokens=8192)

    llm = LLM(
        model=model_name,
        tokenizer_mode="mistral" if args.format == "mistral" else "hf",
        config_format="mistral" if args.format == "mistral" else "hf",
        load_format="mistral" if args.format == "mistral" else "hf",
        limit_mm_per_prompt={"image": 1},
        max_model_len=4096,
        max_num_seqs=2,
        tensor_parallel_size=2,
        mm_processor_cache_gb=0 if args.disable_mm_processor_cache else 4,
    )

    prompt = "Describe this image in one sentence."

    messages = [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": prompt},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": encode_image_url(ImageAsset("cherry_blossom").pil_image)
                    },
                },
            ],
        },
    ]
    outputs = llm.chat(messages, sampling_params=sampling_params)
    print("-" * 50)
    print(outputs[0].outputs[0].text)
    print("-" * 50)


def run_advanced_demo(args: argparse.Namespace):
    model_name = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
    max_img_per_msg = 3
    max_tokens_per_img = 4096

    sampling_params = SamplingParams(max_tokens=8192, temperature=0.7)
    llm = LLM(
        model=model_name,
        tokenizer_mode="mistral" if args.format == "mistral" else "hf",
        config_format="mistral" if args.format == "mistral" else "hf",
        load_format="mistral" if args.format == "mistral" else "hf",
        limit_mm_per_prompt={"image": max_img_per_msg},
        max_model_len=max_img_per_msg * max_tokens_per_img,
        tensor_parallel_size=2,
        mm_processor_cache_gb=0 if args.disable_mm_processor_cache else 4,
    )

    prompt = "Describe the following image."

    url_1 = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"
    url_2 = "https://picsum.photos/seed/picsum/200/300"
    url_3 = "https://picsum.photos/id/32/512/512"

    messages = [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": prompt},
                {"type": "image_url", "image_url": {"url": url_1}},
                {"type": "image_url", "image_url": {"url": url_2}},
            ],
        },
        {
            "role": "assistant",
            "content": "The images show nature.",
        },
        {
            "role": "user",
            "content": "More details please and answer only in French!.",
        },
        {
            "role": "user",
            "content": [
                {"type": "image_url", "image_url": {"url": url_3}},
            ],
        },
    ]

    outputs = llm.chat(messages=messages, sampling_params=sampling_params)
    print("-" * 50)
    print(outputs[0].outputs[0].text)
    print("-" * 50)


def parse_args():
    parser = argparse.ArgumentParser(
        description="Run a demo in simple or advanced mode."
    )

    parser.add_argument(
        "mode",
        choices=["simple", "advanced"],
        help="Specify the demo mode: 'simple' or 'advanced'",
    )

    parser.add_argument(
        "--format",
        choices=["mistral", "hf"],
        default="mistral",
        help="Specify the format of the model to load.",
    )

    parser.add_argument(
        "--disable-mm-processor-cache",
        action="store_true",
        help="If True, disables caching of multi-modal processor.",
    )
    return parser.parse_args()


def main():
    args = parse_args()

    if args.mode == "simple":
        print("Running simple demo...")
        run_simple_demo(args)
    elif args.mode == "advanced":
        print("Running advanced demo...")
        run_advanced_demo(args)


if __name__ == "__main__":
    main()

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""An example showing how to use vLLM to serve multimodal models
and run online serving with OpenAI client.

Launch the vLLM server with the following command:

(single image inference with Llava)
vllm serve llava-hf/llava-1.5-7b-hf

(multi-image inference with Phi-3.5-vision-instruct)
vllm serve microsoft/Phi-3.5-vision-instruct --runner generate \
    --trust-remote-code --max-model-len 4096 --limit-mm-per-prompt.image 2

(audio inference with Ultravox)
vllm serve fixie-ai/ultravox-v0_5-llama-3_2-1b \
    --max-model-len 4096 --trust-remote-code

run the script with
python openai_chat_completion_client_for_multimodal.py --chat-type audio
"""

import os

import pybase64 as base64
import requests
from openai import OpenAI

from vllm.utils.argparse_utils import FlexibleArgumentParser

# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

client = OpenAI(
    # defaults to os.environ.get("OPENAI_API_KEY")
    api_key=openai_api_key,
    base_url=openai_api_base,
)

headers = {"User-Agent": "vLLM Example Client"}


def encode_base64_content_from_url(content_url: str) -> str:
    """Encode a content retrieved from a remote url to base64 format."""

    with requests.get(content_url, headers=headers) as response:
        response.raise_for_status()
        result = base64.b64encode(response.content).decode("utf-8")

    return result


def encode_base64_content_from_file(file_path: str) -> str:
    """Encode a local file content to base64 format."""

    with open(file_path, "rb") as file:
        file_content = file.read()
        result = base64.b64encode(file_content).decode("utf-8")

    return result


# Text-only inference
def run_text_only(model: str, max_completion_tokens: int) -> None:
    chat_completion = client.chat.completions.create(
        messages=[{"role": "user", "content": "What's the capital of France?"}],
        model=model,
        max_completion_tokens=max_completion_tokens,
    )

    result = chat_completion.choices[0].message.content
    print("Chat completion output:\n", result)


# Single-image input inference
def run_single_image(model: str, max_completion_tokens: int) -> None:
    ## Use image url in the payload
    image_url = "https://vllm-public-assets.s3.us-west-2.amazonaws.com/vision_model_images/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
    image_file = "/path/to/image.jpg"  # local file
    chat_completion_from_url = client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "What's in this image?"},
                    {
                        "type": "image_url",
                        "image_url": {"url": image_url},
                    },
                ],
            }
        ],
        model=model,
        max_completion_tokens=max_completion_tokens,
    )

    result = chat_completion_from_url.choices[0].message.content
    print("Chat completion output from image url:\n", result)

    ## Use local image url in the payload
    # Launch the API server/engine with the --allowed-local-media-path argument.
    if os.path.exists(image_file):
        chat_completion_from_local_image_url = client.chat.completions.create(
            messages=[
                {
                    "role": "user",
                    "content": [
                        {"type": "text", "text": "What's in this image?"},
                        {
                            "type": "image_url",
                            "image_url": {"url": f"file://{image_file}"},
                        },
                    ],
                }
            ],
            model=model,
            max_completion_tokens=max_completion_tokens,
        )
        result = chat_completion_from_local_image_url.choices[0].message.content
        print("Chat completion output from local image file:\n", result)
    else:
        print(f"Local image file not found at {image_file}, skipping local file test.")

    ## Use base64 encoded image in the payload
    image_base64 = encode_base64_content_from_url(image_url)
    chat_completion_from_base64 = client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "What's in this image?"},
                    {
                        "type": "image_url",
                        "image_url": {"url": f"data:image/jpeg;base64,{image_base64}"},
                    },
                ],
            }
        ],
        model=model,
        max_completion_tokens=max_completion_tokens,
    )

    result = chat_completion_from_base64.choices[0].message.content
    print("Chat completion output from base64 encoded image:", result)

    ## Use base64 encoded local image in the payload
    if os.path.exists(image_file):
        local_image_base64 = encode_base64_content_from_file(image_file)
        chat_completion_from_local_image_base64 = client.chat.completions.create(
            messages=[
                {
                    "role": "user",
                    "content": [
                        {"type": "text", "text": "What's in this image?"},
                        {
                            "type": "image_url",
                            "image_url": {
                                "url": f"data:image/jpeg;base64,{local_image_base64}"
                            },
                        },
                    ],
                }
            ],
            model=model,
            max_completion_tokens=max_completion_tokens,
        )

        result = chat_completion_from_local_image_base64.choices[0].message.content
        print("Chat completion output from base64 encoded local image:", result)
    else:
        print(f"Local image file not found at {image_file}, skipping local file test.")


# Multi-image input inference
def run_multi_image(model: str, max_completion_tokens: int) -> None:
    image_url_duck = "https://vllm-public-assets.s3.us-west-2.amazonaws.com/multimodal_asset/duck.jpg"
    image_url_lion = "https://vllm-public-assets.s3.us-west-2.amazonaws.com/multimodal_asset/lion.jpg"
    chat_completion_from_url = client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "What are the animals in these images?"},
                    {
                        "type": "image_url",
                        "image_url": {"url": image_url_duck},
                    },
                    {
                        "type": "image_url",
                        "image_url": {"url": image_url_lion},
                    },
                ],
            }
        ],
        model=model,
        max_completion_tokens=max_completion_tokens,
    )

    result = chat_completion_from_url.choices[0].message.content
    print("Chat completion output:\n", result)


# Video input inference
def run_video(model: str, max_completion_tokens: int) -> None:
    video_url = "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ForBiggerFun.mp4"
    video_base64 = encode_base64_content_from_url(video_url)

    ## Use video url in the payload
    chat_completion_from_url = client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "What's in this video?"},
                    {
                        "type": "video_url",
                        "video_url": {"url": video_url},
                    },
                ],
            }
        ],
        model=model,
        max_completion_tokens=max_completion_tokens,
    )

    result = chat_completion_from_url.choices[0].message.content
    print("Chat completion output from video url:\n", result)

    ## Use base64 encoded video in the payload
    chat_completion_from_base64 = client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "What's in this video?"},
                    {
                        "type": "video_url",
                        "video_url": {"url": f"data:video/mp4;base64,{video_base64}"},
                    },
                ],
            }
        ],
        model=model,
        max_completion_tokens=max_completion_tokens,
    )

    result = chat_completion_from_base64.choices[0].message.content
    print("Chat completion output from base64 encoded video:\n", result)


# Audio input inference
def run_audio(model: str, max_completion_tokens: int) -> None:
    from vllm.assets.audio import AudioAsset

    audio_url = AudioAsset("winning_call").url
    audio_base64 = encode_base64_content_from_url(audio_url)

    # OpenAI-compatible schema (`input_audio`)
    chat_completion_from_base64 = client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "What's in this audio?"},
                    {
                        "type": "input_audio",
                        "input_audio": {
                            # Any format supported by soundfile/PyAV is supported
                            "data": audio_base64,
                            "format": "wav",
                        },
                    },
                ],
            }
        ],
        model=model,
        max_completion_tokens=max_completion_tokens,
    )

    result = chat_completion_from_base64.choices[0].message.content
    print("Chat completion output from input audio:\n", result)

    # HTTP URL
    chat_completion_from_url = client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "What's in this audio?"},
                    {
                        "type": "audio_url",
                        "audio_url": {
                            # Any format supported by soundfile/PyAV is supported
                            "url": audio_url
                        },
                    },
                ],
            }
        ],
        model=model,
        max_completion_tokens=max_completion_tokens,
    )

    result = chat_completion_from_url.choices[0].message.content
    print("Chat completion output from audio url:\n", result)

    # base64 URL
    chat_completion_from_base64 = client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "What's in this audio?"},
                    {
                        "type": "audio_url",
                        "audio_url": {
                            # Any format supported by soundfile/PyAV is supported
                            "url": f"data:audio/ogg;base64,{audio_base64}"
                        },
                    },
                ],
            }
        ],
        model=model,
        max_completion_tokens=max_completion_tokens,
    )

    result = chat_completion_from_base64.choices[0].message.content
    print("Chat completion output from base64 encoded audio:\n", result)


def run_multi_audio(model: str, max_completion_tokens: int) -> None:
    from vllm.assets.audio import AudioAsset

    # Two different audios to showcase batched inference.
    audio_url = AudioAsset("winning_call").url
    audio_base64 = encode_base64_content_from_url(audio_url)
    audio_url2 = AudioAsset("azacinto_foscolo").url
    audio_base64_2 = encode_base64_content_from_url(audio_url2)

    # OpenAI-compatible schema (`input_audio`)
    chat_completion_from_base64 = client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "Are these two audios the same?"},
                    {
                        "type": "input_audio",
                        "input_audio": {
                            "data": audio_base64,
                            "format": "wav",
                        },
                    },
                    {
                        "type": "input_audio",
                        "input_audio": {
                            "data": audio_base64_2,
                            "format": "wav",
                        },
                    },
                ],
            }
        ],
        model=model,
        max_completion_tokens=max_completion_tokens,
    )

    result = chat_completion_from_base64.choices[0].message.content
    print("Chat completion output from input audio:\n", result)


example_function_map = {
    "text-only": run_text_only,
    "single-image": run_single_image,
    "multi-image": run_multi_image,
    "multi-audio": run_multi_audio,
    "video": run_video,
    "audio": run_audio,
}


def parse_args():
    parser = FlexibleArgumentParser(
        description="Demo on using OpenAI client for online serving with "
        "multimodal language models served with vLLM."
    )
    parser.add_argument(
        "--chat-type",
        "-c",
        type=str,
        default="single-image",
        choices=list(example_function_map.keys()),
        help="Conversation type with multimodal data.",
    )
    parser.add_argument(
        "--max-completion-tokens",
        "-n",
        type=int,
        default=128,
        help="Maximum number of tokens to generate for each completion.",
    )
    return parser.parse_args()


def main(args) -> None:
    chat_type = args.chat_type
    model = client.models.list().data[0].id
    example_function_map[chat_type](model, args.max_completion_tokens)


if __name__ == "__main__":
    args = parse_args()
    main(args)