Any-to-Any
Transformers
Safetensors
lfm3_audio
automatic-speech-recognition
liquid
lfm3
audio
speech
edge
conversational
multilingual
custom_code
Eval Results
Instructions to use LiquidAI/LFM3-Audio-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LiquidAI/LFM3-Audio-2B with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMultimodalLM model = AutoModelForMultimodalLM.from_pretrained("LiquidAI/LFM3-Audio-2B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add evaluation results
#24
by SaylorTwift HF Staff - opened
Add evaluation results for LiquidAI/LFM3-Audio-2B
Summary
This PR adds the benchmark results published on the model card for LiquidAI/LFM3-Audio-2B to .eval_results/, following the Hugging Face Hub evaluation-results specification.
Benchmarks Added
Benchmarks Skipped (Not Registered on Hub)
These benchmarks are on the model card but have no registered eval.yaml on the Hub:
- Multilingual ASR on FLEURS (6.03)
- English TTS UTMOSv2 (3.83)
- VoiceBench (73.13)
- Tool calling, internal eval (58.50)
- MMAR (50.60)
They can be added once their authors register an eval.yaml on the Hub.
Source
- Model card: https://huggingface.co/LiquidAI/LFM3-Audio-2B
- Blog: https://www.liquid.ai/blog/lfm3
Files Added
.eval_results/LFM3-Audio-2B.yaml
Verification
The scores come from the benchmark tables on the model card, as measured by Liquid AI. No verified token is included, because these were not run via HF Jobs with inspect-ai.
To upload:
hf upload LiquidAI/LFM3-Audio-2B .eval_results .eval_results --create-pr --commit-message "Add evaluation results"
SaylorTwift changed pull request status to open