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41 lines
1.6 KiB
Markdown
41 lines
1.6 KiB
Markdown
<!--Copyright 2025 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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# SpQR
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The [SpQR](https://hf.co/papers/2306.03078) quantization algorithm involves a 16x16 tiled bi-level group 3-bit quantization structure with sparse outliers.
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<div class="flex justify-center">
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/spqr-diagram.png">
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</div>
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> [!TIP]
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> To quantize a model with SpQR, refer to the [Vahe1994/SpQR](https://github.com/Vahe1994/SpQR) repository.
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Load a SpQR-quantized model with [`~PreTrainedModel.from_pretrained`].
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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quantized_model = AutoModelForCausalLM.from_pretrained(
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"elvircrn/Llama-2-7b-SPQR-3Bit-16x16-red_pajama-hf",
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dtype=torch.half,
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained("elvircrn/Llama-2-7b-SPQR-3Bit-16x16-red_pajama-hf")
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```
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