Instructions to use tiny-random/lfm3-omni with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiny-random/lfm3-omni with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForImageTextToText model = AutoModelForImageTextToText.from_pretrained("tiny-random/lfm3-omni", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 783 Bytes
c8adba0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | {
"architectures": [
"LiquidCodecModel"
],
"decoder_window": 30,
"dtype": "bfloat16",
"encoder_conv_dim": 32,
"encoder_dim": 64,
"frame_samples": 1920,
"fsq_levels": [
8,
7,
6,
6
],
"initializer_range": 0.02,
"model_type": "liquid_codec",
"n_q": 12,
"quantizer_dim": 48,
"sample_rate": 24000,
"sampling_rate": 24000,
"stft_frame_stack": 6,
"stft_hop_length": 320,
"stft_n_fft": 1280,
"stft_win_length": 1280,
"transformers_version": "5.19.0.dev0",
"codec_intermediate": 128,
"codec_num_layers": 4,
"codec_num_heads": 2,
"codec_num_kv_heads": 1,
"codec_head_dim": 32,
"auto_map": {
"AutoConfig": "configuration_liquid_codec.LiquidCodecConfig",
"AutoModel": "modeling_liquid_codec.LiquidCodecModel"
}
}
|