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57 lines
3.1 KiB
Markdown
57 lines
3.1 KiB
Markdown
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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# AQLM
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Additive Quantization of Language Models ([AQLM](https://huggingface.co/papers/2401.06118)) quantizes multiple weights together and takes advantage of interdependencies between them. AQLM represents groups of 8-16 weights as a sum of multiple vector codes.
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AQLM also supports fine-tuning with [LoRA](https://huggingface.co/docs/peft/package_reference/lora) with the [PEFT](https://huggingface.co/docs/peft) library, and is fully compatible with [torch.compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) for even faster inference and training.
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Run the command below to install the AQLM library with kernel support for both GPU and CPU inference and training. AQLM only works with Python 3.10+.
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```bash
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pip install aqlm[gpu,cpu]
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```
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Load an AQLM-quantized model with [`~PreTrainedModel.from_pretrained`].
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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quantized_model = AutoModelForCausalLM.from_pretrained(
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"ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf",
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dtype="auto",
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device_map="auto"
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)
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```
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## Configurations
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AQLM quantization setups vary mainly in the number of codebooks used, as well as codebook sizes in bits. The most popular setups and supported inference kernels are shown below.
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| Kernel | Number of codebooks | Codebook size, bits | Notation | Accuracy | Speedup | Fast GPU inference | Fast CPU inference |
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|---|---------------------|---------------------|----------|-------------|-------------|--------------------|--------------------|
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| Triton | K | N | KxN | - | Up to ~0.7x | ✅ | ❌ |
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| CUDA | 1 | 16 | 1x16 | Best | Up to ~1.3x | ✅ | ❌ |
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| CUDA | 2 | 8 | 2x8 | OK | Up to ~3.0x | ✅ | ❌ |
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| Numba | K | 8 | Kx8 | Good | Up to ~4.0x | ❌ | ✅ |
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## Resources
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Run the AQLM demo [notebook](https://colab.research.google.com/drive/1-xZmBRXT5Fm3Ghn4Mwa2KRypORXb855X?usp=sharing) for more examples of how to quantize a model, push a quantized model to the Hub, and more.
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For more example demo notebooks, visit the AQLM [repository](https://github.com/Vahe1994/AQLM).
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