Automatic Speech Recognition
Transformers
rotorquant
kv-cache-quantization
meralion
whisper
audio
speech-recognition
sea-lion
quantized
Instructions to use majentik/MERaLiON-2-3B-RotorQuant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use majentik/MERaLiON-2-3B-RotorQuant with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="majentik/MERaLiON-2-3B-RotorQuant")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("majentik/MERaLiON-2-3B-RotorQuant", dtype="auto") - Notebooks
- Google Colab
- Kaggle
docs: remove AWQ variants (AWQ lane paused; MLX focus)
Browse files
README.md
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@@ -38,7 +38,6 @@ KV cache compression reduces the memory used by the attention cache during infer
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| Technique | Where it's applied | Savings |
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| Weight quantization (GGUF/MLX/AWQ) | Baked into model file | Reduces disk + weight memory |
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| **RotorQuant KV cache** | At inference time | Reduces attention memory (critical for long context) |
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Both can be combined for maximum efficiency.
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| Technique | Where it's applied | Savings |
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| **RotorQuant KV cache** | At inference time | Reduces attention memory (critical for long context) |
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Both can be combined for maximum efficiency.
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