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transformers/docs/source/en/quantization/hqq.md

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+
+# HQQ
+
+[Half-Quadratic Quantization (HQQ)](https://github.com/mobiusml/hqq/) supports fast on-the-fly quantization for 8, 4, 3, 2, and even 1-bits. It doesn't require calibration data, and it is compatible with any model modality (LLMs, vision, etc.).
+
+HQQ further supports fine-tuning with [PEFT](https://huggingface.co/docs/peft) and is fully compatible with [torch.compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) for even faster inference and training.
+
+Install HQQ with the following command to get the latest version and to build its corresponding CUDA kernels if you are using a cuda device. It also support Intel XPU with pure pytorch implementation.
+
+```bash
+pip install hqq
+```
+
+You can choose to either replace all the linear layers in a model with the same quantization config or dedicate a specific quantization config for specific linear layers.
+
+<hfoptions id="hqq">
+<hfoption id="replace all layers">
+
+Quantize a model by creating a [`HqqConfig`] and specifying the `nbits` and `group_size` to replace for all the linear layers ([torch.nn.Linear](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html)) of the model.
+
+``` py
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, HqqConfig
+
+quant_config = HqqConfig(nbits=8, group_size=64)
+model = transformers.AutoModelForCausalLM.from_pretrained(
+    "meta-llama/Llama-3.1-8B", 
+    dtype=torch.float16, 
+    device_map="auto", 
+    quantization_config=quant_config
+)
+```
+
+</hfoption>
+<hfoption id="specific layers only">
+
+Quantize a model by creating a dictionary specifying the `nbits` and `group_size` for the linear layers to quantize. Pass them to [`HqqConfig`] and set which layers to quantize with the config. This approach is especially useful for quantizing mixture-of-experts (MoEs) because they are less affected ly lower quantization settings.
+
+``` py
+q4_config = {'nbits':4, 'group_size':64}
+q3_config = {'nbits':3, 'group_size':32}
+quant_config  = HqqConfig(dynamic_config={
+  'self_attn.q_proj':q4_config,
+  'self_attn.k_proj':q4_config,
+  'self_attn.v_proj':q4_config,
+  'self_attn.o_proj':q4_config,
+
+  'mlp.gate_proj':q3_config,
+  'mlp.up_proj'  :q3_config,
+  'mlp.down_proj':q3_config,
+})
+
+model = transformers.AutoModelForCausalLM.from_pretrained(
+    "meta-llama/Llama-3.1-8B", 
+    dtype=torch.float16, 
+    device_map="auto", 
+    quantization_config=quant_config
+)
+```
+
+</hfoption>
+</hfoptions>
+
+## Backends
+
+HQQ supports various backends, including pure PyTorch and custom dequantization CUDA kernels. These backends are suitable for older GPUs and PEFT/QLoRA training.
+
+```py
+from hqq.core.quantize import *
+
+HQQLinear.set_backend(HQQBackend.PYTORCH)
+```
+
+For faster inference, HQQ supports 4-bit fused kernels (torchao and Marlin) after a model is quantized. These can reach up to 200 tokens/sec on a single 4090. The example below demonstrates enabling the torchao_int4 backend.
+
+```py
+from hqq.utils.patching import prepare_for_inference
+
+prepare_for_inference("model", backend="torchao_int4")
+```
+
+Refer to the [Backend](https://github.com/mobiusml/hqq/#backend) guide for more details.
+
+## Resources
+
+Read the [Half-Quadratic Quantization of Large Machine Learning Models](https://mobiusml.github.io/hqq_blog/) blog post for more details about HQQ.