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+
+# VPTQ
+
+[Vector Post-Training Quantization (VPTQ)](https://github.com/microsoft/VPTQ) is a Post-Training Quantization (PTQ) method that leverages vector quantization to quantize LLMs at an extremely low bit-width (<2-bit). VPTQ can compress a 70B, even a 405B model, to 1-2 bits without retraining and still maintain a high-degree of accuracy. It is a lightweight quantization algorithm that takes ~17 hours to quantize a 405B model. VPTQ features agile quantization inference with low decoding overhead and high throughput and Time To First Token (TTFT).
+
+Run the command below to install VPTQ which provides efficient kernels for inference on NVIDIA and AMD GPUs.
+
+```bash
+pip install vptq
+```
+
+The [VPTQ-community](https://huggingface.co/VPTQ-community) provides a collection of VPTQ-quantized models. The model name contains information about its bitwidth (excluding cookbook, parameter, and padding overhead). Consider the [Meta-Llama-3.1-70B-Instruct-v8-k65536-256-woft] model as an example.
+
+- The model name is Meta-Llama-3.1-70B-Instruct.
+- The number of centroids is given by 65536 (2^16).
+- The number of residual centroids is given by 256 (2^8).
+
+The equivalent bit-width calculation is given by the following.
+
+- index: log2(65536) = 16 / 8 = 2-bits
+- residual index: log2(256) = 8 / 8 = 1-bit
+- total bit-width: 2 + 1 = 3-bits
+
+From here, estimate the model size by multiplying 70B * 3-bits / 8-bits/byte for a total of 26.25GB.
+
+Load a VPTQ quantized model with [`~PreTrainedModel.from_pretrained`].
+
+```py
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+quantized_model = AutoModelForCausalLM.from_pretrained(
+    "VPTQ-community/Meta-Llama-3.1-70B-Instruct-v16-k65536-65536-woft",
+    dtype="auto", 
+    device_map="auto"
+)
+```
+
+To quantize your own model, refer to the [VPTQ Quantization Algorithm Tutorial](https://github.com/microsoft/VPTQ/blob/algorithm/algorithm.md) tutorial.
+
+## Benchmarks
+
+VPTQ achieves better accuracy and higher throughput with lower quantization overhead across models of different sizes. The following experimental results are for reference only; VPTQ can achieve better outcomes under reasonable parameters, especially in terms of model accuracy and inference speed.
+
+| Model       | bitwidth | W2↓  | C4↓  | AvgQA↑ | tok/s↑ | mem(GB) | cost/h↓ |
+| ----------- | -------- | ---- | ---- | ------ | ------ | ------- | ------- |
+| LLaMA-2 7B  | 2.02     | 6.13 | 8.07 | 58.2   | 39.9   | 2.28    | 2       |
+|             | 2.26     | 5.95 | 7.87 | 59.4   | 35.7   | 2.48    | 3.1     |
+| LLaMA-2 13B | 2.02     | 5.32 | 7.15 | 62.4   | 26.9   | 4.03    | 3.2     |
+|             | 2.18     | 5.28 | 7.04 | 63.1   | 18.5   | 4.31    | 3.6     |
+| LLaMA-2 70B | 2.07     | 3.93 | 5.72 | 68.6   | 9.7    | 19.54   | 19      |
+|             | 2.11     | 3.92 | 5.71 | 68.7   | 9.7    | 20.01   | 19      |
+
+## Resources
+
+See an example demo of VPTQ on the VPTQ Online Demo [Space](https://huggingface.co/spaces/microsoft/VPTQ) or try running the VPTQ inference [notebook](https://colab.research.google.com/github/microsoft/VPTQ/blob/main/notebooks/vptq_example.ipynb).
+
+For more information, read the VPTQ [paper](https://huggingface.co/papers/2409.17066).