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

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+
+# BitNet
+
+[BitNet](https://huggingface.co/papers/2402.17764) replaces traditional linear layers in Multi-Head Attention and feed-forward networks with specialized BitLinear layers. The BitLinear layers quantize the weights using ternary precision (with values of -1, 0, and 1) and quantize the activations to 8-bit precision.
+
+<figure style="text-align: center;">
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/1.58llm_extreme_quantization/bitlinear.png" alt="Alt Text" />
+  <figcaption>The architecture of BitNet with BitLinear layers.</figcaption>
+</figure>
+
+BitNet models can't be quantized on the fly. They need to be quantized during pretraining or fine-tuning because it is a Quantization-Aware Training (QAT) technique. During training, the weights are quantized to ternary values with symmetric per tensor quantization.
+
+1. Compute the average of the absolute values of the weight matrix and use as a scale.
+2. Divide the weights by the scale, round the values, constrain them between -1 and 1, and rescale them to continue in full precision.
+3. Activations are quantized to a specified bit-width (8-bit) using [absmax](https://huggingface.co/papers/2208.07339) quantization (symmetric per channel quantization). This involves scaling the activations into a range of [−128,127].
+
+Refer to this [PR](https://github.com/huggingface/nanotron/pull/180) to pretrain or fine-tune a 1.58-bit model with [Nanotron](https://github.com/huggingface/nanotron). For fine-tuning, convert a model from the Hugging Face to Nanotron format. Find the conversion steps in this [PR](https://github.com/huggingface/nanotron/pull/174).
+
+Load a BitNet quantized model with [`~PreTrainedModel.from_pretrained`].
+
+```py
+from transformers import AutoModelForCausalLM
+path = "/path/to/model"
+model = AutoModelForCausalLM.from_pretrained(path, device_map="auto")
+```
+
+## Kernels
+
+`@torch.compile` is used to unpack the weights and perform the forward pass. It's very straightforward to implement and delivers significant speed improvements. Additional optimized kernels will be integrated in future versions.
+
+## Resources
+
+Read [Fine-tuning LLMs to 1.58bit: extreme quantization made easy](https://huggingface.co/blog/1_58_llm_extreme_quantization) to learn more about how BitNet models are trained and fine-tuned.