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
Download audio_tokenizer/config.json from tiny-random/lfm3-omni: direct link, hf CLI and curl.
- Browser
- Download file 783 Bytes
-
https://huggingface.co/tiny-random/lfm3-omni/resolve/main/audio_tokenizer/config.json
- Command line
-
hf download hf://tiny-random/lfm3-omni/audio_tokenizer/config.json
-
curl -L -o config.json https://huggingface.co/tiny-random/lfm3-omni/resolve/main/audio_tokenizer/config.json
783 Bytes
| { | |
| "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" | |
| } | |
| } | |