init

2025-10-09 16:47:16 +08:00
parent c8feb4deb5
commit e27e3f16bb
5248 changed files with 1778505 additions and 0 deletions
--- a/transformers/docs/source/en/quantization/aqlm.md
+++ b/transformers/docs/source/en/quantization/aqlm.md
@@ -0,0 +1,56 @@
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# AQLM
+
+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.
+
+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.
+
+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+.
+
+```bash
+pip install aqlm[gpu,cpu]
+```
+
+Load an AQLM-quantized model with [`~PreTrainedModel.from_pretrained`].
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+quantized_model = AutoModelForCausalLM.from_pretrained(
+    "ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf",
+    dtype="auto", 
+    device_map="auto"
+)
+```
+
+## Configurations
+
+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.
+
+| 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 | ❌                  | ✅                  |
+
+## Resources
+
+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.
+
+For more example demo notebooks, visit the AQLM [repository](https://github.com/Vahe1994/AQLM).