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yujiepan/
qwen2.5-omni-tiny-random

Name: qwen2.5-omni-tiny-random
Availability: InStock

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This tiny model is for debugging. It is randomly initialized with the config adapted from Qwen/Qwen2.5-Omni-7B.

Example usage:

python

import unittest

import torch

import soundfile as sf
from qwen_omni_utils import process_mm_info
from transformers import (
    Qwen2_5OmniForConditionalGeneration,
    Qwen2_5OmniPreTrainedModel,
    Qwen2_5OmniProcessor,
)

model_id = "yujiepan/qwen2.5-omni-tiny-random"
# model = Qwen2_5OmniModel.from_pretrained(model_id, torch_dtype="auto", device_map="auto").eval()
# We recommend enabling flash_attention_2 for better acceleration and memory saving.

Qwen2_5OmniPreTrainedModel._init_weights = unittest.mock.Mock()
model = Qwen2_5OmniForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
    attn_implementation="flash_attention_2",
).eval()
processor = Qwen2_5OmniProcessor.from_pretrained(model_id)

conversation = [
    {
        "role": "system",
        "content": [
            {"type": "text", "text": "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."}
        ],
    },
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Hi, can you tell me a joke?"},
            # {"type": "audio", "audio": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Audio/glass-breaking-151256.mp3"},
            # {"type": "video", "video": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2.5-Omni/draw.mp4"},
            {"type": "image", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"},
        ],
    },
]

# Preparation for inference
text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
audios, images, videos = process_mm_info(conversation, use_audio_in_video=True)
print('Audios:', audios)
print('Images:', images)
print('Videos:', videos)
inputs = processor(text=text, audios=audios, images=images, videos=videos, return_tensors="pt", padding=True)
inputs = inputs.to(model.device).to(model.dtype)

# Inference: Generation of the output text and audio
text_ids, audio = model.generate(
    **inputs, use_audio_in_video=True,
    thinker_max_new_tokens=16, talker_max_new_tokens=16,
    temperature=0.1,
)

text = processor.batch_decode(text_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(text, '\n' * 3)
sf.write(
    "/tmp/output.wav",
    audio.reshape(-1).detach().cpu().numpy(),
    samplerate=24000,
)

Codes to create this repo:

python

import unittest
from pathlib import Path

import torch

import accelerate
from huggingface_hub import hf_hub_download
from transformers import (
    AutoConfig,
    AutoModelForCausalLM,
    AutoTokenizer,
    GenerationConfig,
    Qwen2_5OmniForConditionalGeneration,
    Qwen2_5OmniPreTrainedModel,
    Qwen2_5OmniProcessor,
    pipeline,
    set_seed,
)

source_model_id = "Qwen/Qwen2.5-Omni-7B"
save_folder = "/tmp/yujiepan/qwen2.5-omni-tiny-random"

processor = Qwen2_5OmniProcessor.from_pretrained(
    source_model_id, trust_remote_code=True,
)
processor.save_pretrained(save_folder)

config = AutoConfig.from_pretrained(
    source_model_id, trust_remote_code=True,
)
OUTPUT_DIM = 16
config.talker_config.num_hidden_layers = 1
config.talker_config.hidden_size = 16
config.talker_config.embedding_size = OUTPUT_DIM
config.talker_config.head_dim = 16
config.talker_config.num_attention_heads = 1
config.talker_config.num_key_value_heads = 1
config.talker_config.intermediate_size = 32
config.talker_config.rope_scaling['mrope_section'] = [2, 2, 4]
assert 2 * sum(config.talker_config.rope_scaling['mrope_section']
               ) == config.talker_config.hidden_size / config.talker_config.num_attention_heads

config.thinker_config.audio_config.num_hidden_layers = 1
config.thinker_config.audio_config.encoder_layers = 1
config.thinker_config.audio_config.d_model = 16
config.thinker_config.audio_config.encoder_attention_heads = 1
config.thinker_config.audio_config.encoder_ffn_dim = 32
config.thinker_config.audio_config.output_dim = OUTPUT_DIM

config.thinker_config.text_config.num_hidden_layers = 1
config.thinker_config.text_config.hidden_size = OUTPUT_DIM
config.thinker_config.text_config.intermediate_size = 32
config.thinker_config.text_config.num_attention_heads = 1
config.thinker_config.text_config.num_key_value_heads = 1
config.thinker_config.text_config.rope_scaling['mrope_section'] = [2, 2, 4]
assert 2 * sum(config.thinker_config.text_config.rope_scaling['mrope_section']
               ) == config.thinker_config.text_config.hidden_size / config.thinker_config.text_config.num_attention_heads

config.thinker_config.vision_config.depth = 2
config.thinker_config.vision_config.embed_dim = 16
config.thinker_config.vision_config.hidden_size = 16
config.thinker_config.vision_config.intermediate_size = 32
config.thinker_config.vision_config.out_hidden_size = OUTPUT_DIM
config.thinker_config.vision_config.num_heads = 1
config.thinker_config.vision_config.fullatt_block_indexes = [1]

config.token2wav_config.bigvgan_config.resblock_dilation_sizes = [[1, 3, 5]]
config.token2wav_config.bigvgan_config.resblock_kernel_sizes = [7]
config.token2wav_config.bigvgan_config.upsample_initial_channel = 32
config.token2wav_config.bigvgan_config.upsample_kernel_sizes = [11, 4]
config.token2wav_config.bigvgan_config.upsample_rates = [5, 2]

config.token2wav_config.dit_config.depth = 2
config.token2wav_config.dit_config.num_hidden_layers = 2
config.token2wav_config.dit_config.hidden_size = 16
config.token2wav_config.dit_config.dim = 16
config.token2wav_config.dit_config.emb_dim = 16
config.token2wav_config.dit_config.enc_attention_channels = 16
config.token2wav_config.dit_config.enc_channels = [32, 32, 32]
config.token2wav_config.dit_config.enc_dilations = [1, 3, 4]
config.token2wav_config.dit_config.enc_kernel_sizes = [5, 3, 1]
config.token2wav_config.dit_config.enc_dim = 16
config.token2wav_config.dit_config.enc_emb_dim = 16
config.token2wav_config.dit_config.enc_lin_neurons = 16
config.token2wav_config.dit_config.head_dim = 16
config.token2wav_config.dit_config.num_attention_heads = 1
config.token2wav_config.dit_config.heads = 1
config.token2wav_config.dit_config.look_ahead_layers = [1]
config.token2wav_config.dit_config.look_backward_layers = [0]
# avoid mismatch in vocab size because this is random model!
config.token2wav_config.dit_config.num_embeds = config.talker_config.vocab_size
print(config)

spk_dict = torch.load(hf_hub_download(source_model_id, 'spk_dict.pt', repo_type='model'))
for _, info in spk_dict.items():
    info['cond'] = info['cond'][:, :config.token2wav_config.dit_config.enc_emb_dim].clone()
torch.save(spk_dict, Path(save_folder, "spk_dict.pt"))

# patch for non-affine layernorm
Qwen2_5OmniPreTrainedModel._init_weights = unittest.mock.Mock()

torch.set_default_dtype(torch.bfloat16)
model = Qwen2_5OmniForConditionalGeneration(
    config,
)
torch.set_default_dtype(torch.float32)
model.generation_config = GenerationConfig.from_pretrained(
    source_model_id, trust_remote_code=True,
)
set_seed(42)
with torch.no_grad():
    for name, p in sorted(model.named_parameters()):
        torch.nn.init.normal_(p, 0, 0.5)
        print(name, p.shape, p.dtype)
model.save_pretrained(save_folder)