Instructions to use lmms-lab/Aero-1-Audio with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lmms-lab/Aero-1-Audio with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="lmms-lab/Aero-1-Audio", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("lmms-lab/Aero-1-Audio", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lmms-lab/Aero-1-Audio with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lmms-lab/Aero-1-Audio"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lmms-lab/Aero-1-Audio",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lmms-lab/Aero-1-Audio

SGLang

How to use lmms-lab/Aero-1-Audio with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "lmms-lab/Aero-1-Audio" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lmms-lab/Aero-1-Audio",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "lmms-lab/Aero-1-Audio" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lmms-lab/Aero-1-Audio",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lmms-lab/Aero-1-Audio with Docker Model Runner:
```
docker model run hf.co/lmms-lab/Aero-1-Audio
```

Aero-1-Audio / processing_aero.py

kcz358

Update processing_aero.py

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# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Processor class for Aero.
"""

import math
import os
from typing import List, Optional, Union

import numpy as np
from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput
from transformers.video_utils import VideoInput
from transformers.models.auto import AutoFeatureExtractor
from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
from transformers.utils import logging

logger = logging.get_logger(__name__)


class AeroProcessorKwargs(ProcessingKwargs, total=False):
    _defaults = {
        "text_kwargs": {
            "padding": False,
        },
        "audio_kwargs": {
        },
    }


class AeroProcessor(ProcessorMixin):
    attributes = ["tokenizer", "audio_processor"]
    valid_kwargs = [
        "chat_template",
        "audio_token",
    ]
    tokenizer_class = "AutoTokenizer"
    audio_processor_class = "AutoFeatureExtractor"

    def __init__(
        self,
        tokenizer=None,
        audio_processor=None,
        chat_template=None,
        audio_token="<|AUDIO|>",
        **kwargs,
    ):
        self.audio_token = (
            tokenizer.audio_token if hasattr(tokenizer, "audio_token") else audio_token
        )
        if chat_template is None:
            chat_template = self.default_chat_template
        super().__init__(
            tokenizer,
            audio_processor,
            chat_template=chat_template,
        )

    def __call__(
        self,
        text: Union[
            TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]
        ] = None,
        audios: Union[np.ndarray, List[np.ndarray]] = None,
        videos: VideoInput = None,
        images: ImageInput = None,
        sampling_rate: Optional[int] = None,
        **kwargs: Unpack[AeroProcessorKwargs],
    ) -> BatchFeature:
        """
        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
        and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
        the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
        LlavaNextImageProcessor's [`~LlavaNextImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
        of the above two methods for more information.
        Args:
            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
                tensor. Both channels-first and channels-last formats are supported.
            text (`str`, `List[str]`, `List[List[str]]`):
                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
            videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
                The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
        Returns:
            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
              `None`).
            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
            - **pixel_values_videos** -- Pixel values of a video input to be fed to a model. Returned when `videos` is not `None`.
            - **image_sizes** -- Size of each image that will be used to unpad an image. Returned when `images` is not `None`.
        """

        output_kwargs = self._merge_kwargs(
            AeroProcessorKwargs,
            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
            **kwargs,
        )
        # Make sure no duplicate padding
        if "padding" in output_kwargs["audio_kwargs"]:
            output_kwargs["audio_kwargs"].pop("padding")

        if isinstance(text, str):
            text = [text]
        elif not isinstance(text, list) and not isinstance(text[0], str):
            raise ValueError(
                "Invalid input text. Please provide a string, or a list of strings"
            )

        audio_inputs = {}

        if audios is not None:
            audio_inputs = self.audio_processor(
                audios,
                sampling_rate=sampling_rate,
                return_attention_mask=True,
                padding="max_length",
                **output_kwargs["audio_kwargs"],
            )
            audio_inputs["audio_attention_mask"] = audio_inputs.pop(
                "attention_mask"
            )  # rename attention_mask to prevent conflicts later on
            audio_inputs["audio_values"] = audio_inputs.pop(
                "input_features"
            )  # rename input_features to audio_features for clarification
            # Computes the output length of the convolutional layers and the output length of the audio encoder
            input_lengths = (audio_inputs["audio_attention_mask"].sum(-1) - 1) // 2 + 1
            num_audio_tokens = (input_lengths - 2) // 2 + 1
            text = self.expand_audio_tokens(text, num_audio_tokens, self.audio_token)

        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
        return BatchFeature(data={**text_inputs, **audio_inputs})

    def expand_audio_tokens(
        self,
        text: List[TextInput],
        num_audio_tokens: List[int],
        special_token: str,
    ):
        prompt_strings = []
        current_audio_idx = 0
        for sample in text:
            while special_token in sample:
                num_audio_token = num_audio_tokens[current_audio_idx]
                sample = sample.replace(
                    special_token, "<placeholder>" * num_audio_token, 1
                )
                current_audio_idx += 1
            prompt_strings.append(sample)
        text = [
            sample.replace("<placeholder>", special_token) for sample in prompt_strings
        ]
        return text

    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
    def batch_decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
        refer to the docstring of this method for more information.
        """
        return self.tokenizer.batch_decode(*args, **kwargs)

    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
    def decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
        the docstring of this method for more information.
        """
        return self.tokenizer.decode(*args, **kwargs)

    def batch_encode(self, *args, **kwargs):
        """
        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_encode`]. Please
        refer to the docstring of this method for more information.
        """
        return self.tokenizer.batch_encode(*args, **kwargs)

    def encode(self, *args, **kwargs):
        """
        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.encode`]. Please refer to
        the docstring of this method for more information.
        """
        return self.tokenizer.encode(*args, **kwargs)

    @property
    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
    def model_input_names(self):
        tokenizer_input_names = self.tokenizer.model_input_names
        image_processor_input_names = self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))

    @property
    def default_chat_template(self):
        # fmt: off
        return (
            "{% set audio_count = namespace(value=0) %}"
            "{% for message in messages %}"
                "{% if loop.first and message['role'] != 'system' %}"
                    "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
                "{% endif %}"
                "<|im_start|>{{ message['role'] }}\n"
                "{% if message['content'] is string %}"
                    "{{ message['content'] }}<|im_end|>\n"
                "{% else %}"
                    "{% for content in message['content'] %}"
                        "{% if 'audio' in content or 'audio_url' in content or content['type'] == 'audio'%}"
                            "{% set audio_count.value = audio_count.value + 1 %}"
                            "<|AUDIO|>\n"
                        "{% elif 'text' in content %}"
                            "{{ content['text'] }}"
                        "{% endif %}"
                    "{% endfor %}"
                    "<|im_end|>\n"
                "{% endif %}"
            "{% endfor %}"
            "{% if add_generation_prompt %}"
                "<|im_start|>assistant\n"
            "{% endif %}"
        )
        # fmt: on

	# coding=utf-8
	# Copyright 2024 The HuggingFace Inc. team.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""
	Processor class for Aero.
	"""

	import math
	import os
	from typing import List, Optional, Union

	import numpy as np
	from transformers.feature_extraction_utils import BatchFeature
	from transformers.image_utils import ImageInput
	from transformers.video_utils import VideoInput
	from transformers.models.auto import AutoFeatureExtractor
	from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
	from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
	from transformers.utils import logging

	logger = logging.get_logger(__name__)


	class AeroProcessorKwargs(ProcessingKwargs, total=False):
	_defaults = {
	"text_kwargs": {
	"padding": False,
	},
	"audio_kwargs": {
	},
	}


	class AeroProcessor(ProcessorMixin):
	attributes = ["tokenizer", "audio_processor"]
	valid_kwargs = [
	"chat_template",
	"audio_token",
	]
	tokenizer_class = "AutoTokenizer"
	audio_processor_class = "AutoFeatureExtractor"

	def __init__(
	self,
	tokenizer=None,
	audio_processor=None,
	chat_template=None,
	audio_token="<\|AUDIO\|>",
	**kwargs,
	):
	self.audio_token = (
	tokenizer.audio_token if hasattr(tokenizer, "audio_token") else audio_token
	)
	if chat_template is None:
	chat_template = self.default_chat_template
	super().__init__(
	tokenizer,
	audio_processor,
	chat_template=chat_template,
	)

	def __call__(
	self,
	text: Union[
	TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]
	] = None,
	audios: Union[np.ndarray, List[np.ndarray]] = None,
	videos: VideoInput = None,
	images: ImageInput = None,
	sampling_rate: Optional[int] = None,
	**kwargs: Unpack[AeroProcessorKwargs],
	) -> BatchFeature:
	"""
	Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
	and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
	the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
	LlavaNextImageProcessor's [`~LlavaNextImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
	of the above two methods for more information.

	Args:
	images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
	The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
	tensor. Both channels-first and channels-last formats are supported.
	text (`str`, `List[str]`, `List[List[str]]`):
	The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
	(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
	`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
	videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
	The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch

	Returns:
	[`BatchFeature`]: A [`BatchFeature`] with the following fields:

	- input_ids -- List of token ids to be fed to a model. Returned when `text` is not `None`.
	- attention_mask -- List of indices specifying which tokens should be attended to by the model (when
	`return_attention_mask=True` or if "attention_mask" is in `self.model_input_names` and if `text` is not
	`None`).
	- pixel_values -- Pixel values to be fed to a model. Returned when `images` is not `None`.
	- pixel_values_videos -- Pixel values of a video input to be fed to a model. Returned when `videos` is not `None`.
	- image_sizes -- Size of each image that will be used to unpad an image. Returned when `images` is not `None`.
	"""

	output_kwargs = self._merge_kwargs(
	AeroProcessorKwargs,
	tokenizer_init_kwargs=self.tokenizer.init_kwargs,
	**kwargs,
	)
	# Make sure no duplicate padding
	if "padding" in output_kwargs["audio_kwargs"]:
	output_kwargs["audio_kwargs"].pop("padding")

	if isinstance(text, str):
	text = [text]
	elif not isinstance(text, list) and not isinstance(text[0], str):
	raise ValueError(
	"Invalid input text. Please provide a string, or a list of strings"
	)

	audio_inputs = {}

	if audios is not None:
	audio_inputs = self.audio_processor(
	audios,
	sampling_rate=sampling_rate,
	return_attention_mask=True,
	padding="max_length",
	**output_kwargs["audio_kwargs"],
	)
	audio_inputs["audio_attention_mask"] = audio_inputs.pop(
	"attention_mask"
	) # rename attention_mask to prevent conflicts later on
	audio_inputs["audio_values"] = audio_inputs.pop(
	"input_features"
	) # rename input_features to audio_features for clarification
	# Computes the output length of the convolutional layers and the output length of the audio encoder
	input_lengths = (audio_inputs["audio_attention_mask"].sum(-1) - 1) // 2 + 1
	num_audio_tokens = (input_lengths - 2) // 2 + 1
	text = self.expand_audio_tokens(text, num_audio_tokens, self.audio_token)

	text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
	return BatchFeature(data={text_inputs, audio_inputs})

	def expand_audio_tokens(
	self,
	text: List[TextInput],
	num_audio_tokens: List[int],
	special_token: str,
	):
	prompt_strings = []
	current_audio_idx = 0
	for sample in text:
	while special_token in sample:
	num_audio_token = num_audio_tokens[current_audio_idx]
	sample = sample.replace(
	special_token, "<placeholder>" * num_audio_token, 1
	)
	current_audio_idx += 1
	prompt_strings.append(sample)
	text = [
	sample.replace("<placeholder>", special_token) for sample in prompt_strings
	]
	return text

	# Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
	def batch_decode(self, args, *kwargs):
	"""
	This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
	refer to the docstring of this method for more information.
	"""
	return self.tokenizer.batch_decode(args, *kwargs)

	# Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
	def decode(self, args, *kwargs):
	"""
	This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
	the docstring of this method for more information.
	"""
	return self.tokenizer.decode(args, *kwargs)

	def batch_encode(self, args, *kwargs):
	"""
	This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_encode`]. Please
	refer to the docstring of this method for more information.
	"""
	return self.tokenizer.batch_encode(args, *kwargs)

	def encode(self, args, *kwargs):
	"""
	This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.encode`]. Please refer to
	the docstring of this method for more information.
	"""
	return self.tokenizer.encode(args, *kwargs)

	@property
	# Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
	def model_input_names(self):
	tokenizer_input_names = self.tokenizer.model_input_names
	image_processor_input_names = self.image_processor.model_input_names
	return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))

	@property
	def default_chat_template(self):
	# fmt: off
	return (
	"{% set audio_count = namespace(value=0) %}"
	"{% for message in messages %}"
	"{% if loop.first and message['role'] != 'system' %}"
	"<\|im_start\|>system\nYou are a helpful assistant.<\|im_end\|>\n"
	"{% endif %}"
	"<\|im_start\|>{{ message['role'] }}\n"
	"{% if message['content'] is string %}"
	"{{ message['content'] }}<\|im_end\|>\n"
	"{% else %}"
	"{% for content in message['content'] %}"
	"{% if 'audio' in content or 'audio_url' in content or content['type'] == 'audio'%}"
	"{% set audio_count.value = audio_count.value + 1 %}"
	"<\|AUDIO\|>\n"
	"{% elif 'text' in content %}"
	"{{ content['text'] }}"
	"{% endif %}"
	"{% endfor %}"
	"<\|im_end\|>\n"
	"{% endif %}"
	"{% endfor %}"
	"{% if add_generation_prompt %}"
	"<\|im_start\|>assistant\n"
	"{% endif %}"
	)
	# fmt: on