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| # AQLM | |
| > [!TIP] | |
| > Try AQLM on [Google Colab](https://colab.research.google.com/drive/1-xZmBRXT5Fm3Ghn4Mwa2KRypORXb855X?usp=sharing)! | |
| Additive Quantization of Language Models ([AQLM](https://arxiv.org/abs/2401.06118)) is a Large Language Models compression method. It 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. | |
| Inference support for AQLM is realised in the `aqlm` library. Make sure to install it to run the models (note aqlm works only with python>=3.10): | |
| ```bash | |
| pip install aqlm[gpu,cpu] | |
| ``` | |
| The library provides efficient kernels for both GPU and CPU inference and training. | |
| The instructions on how to quantize models yourself, as well as all the relevant code can be found in the corresponding GitHub [repository](https://github.com/Vahe1994/AQLM). To run AQLM models simply load a model that has been quantized with AQLM: | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| quantized_model = AutoModelForCausalLM.from_pretrained( | |
| "ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf", | |
| torch_dtype="auto", | |
| device_map="auto" | |
| ) | |
| tokenizer = AutoTokenizer.from_pretrained("ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf") | |
| ``` | |
| ## PEFT | |
| Starting with version `aqlm 1.0.2`, AQLM supports Parameter-Efficient Fine-Tuning in a form of [LoRA](https://huggingface.co/docs/peft/package_reference/lora) integrated into the [PEFT](https://huggingface.co/blog/peft) library. | |
| ## AQLM configurations | |
| AQLM quantization setups vary mainly on the number of codebooks used as well as codebook sizes in bits. The most popular setups, as well as inference kernels they support are: | |
| | Kernel | Number of codebooks | Codebook size, bits | Notation | Accuracy | Speedup | Fast GPU inference | Fast CPU inference | | |
| |---|---------------------|---------------------|----------|-------------|-------------|--------------------|--------------------| | |
| | Triton | K | N | KxN | - | Up to ~0.7x | ✅ | ❌ | | |
| | CUDA | 1 | 16 | 1x16 | Best | Up to ~1.3x | ✅ | ❌ | | |
| | CUDA | 2 | 8 | 2x8 | OK | Up to ~3.0x | ✅ | ❌ | | |
| | Numba | K | 8 | Kx8 | Good | Up to ~4.0x | ❌ | ✅ | | |