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| # HIGGS | |
| HIGGS is a 0-shot quantization algorithm that combines Hadamard preprocessing with MSE-Optimal quantization grids to achieve lower quantization error and SOTA performance. You can find more information in the paper [arxiv.org/abs/2411.17525](https://arxiv.org/abs/2411.17525). | |
| Runtime support for HIGGS is implemented through [FLUTE](https://arxiv.org/abs/2407.10960), and its [library](https://github.com/HanGuo97/flute). | |
| ## Quantization Example | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, HiggsConfig | |
| model = AutoModelForCausalLM.from_pretrained( | |
| "google/gemma-2-9b-it", | |
| quantization_config=HiggsConfig(bits=4), | |
| device_map="auto", | |
| ) | |
| tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it") | |
| tokenizer.decode(model.generate( | |
| **tokenizer("Hi,", return_tensors="pt").to(model.device), | |
| temperature=0.5, | |
| top_p=0.80, | |
| )[0]) | |
| ``` | |
| ## Pre-quantized models | |
| Some pre-quantized models can be found in the [official collection](https://huggingface.co/collections/ISTA-DASLab/higgs-675308e432fd56b7f6dab94e) on Hugging Face Hub. | |
| ## Current Limitations | |
| **Architectures** | |
| Currently, FLUTE, and HIGGS by extension, **only support Llama 3 and 3.0 of 8B, 70B and 405B parameters, as well as Gemma-2 9B and 27B**. We're working on allowing to run more diverse models as well as allow arbitrary models by modifying the FLUTE compilation procedure. | |
| **torch.compile** | |
| HIGGS is fully compatible with `torch.compile`. Compiling `model.forward`, as described [here](../perf_torch_compile.md), here're the speedups it provides on RTX 4090 for `Llama-3.1-8B-Instruct` (forward passes/sec): | |
| | Batch Size | BF16 (With `torch.compile`) | HIGGS 4bit (No `torch.compile`) | HIGGS 4bit (With `torch.compile`) | | |
| |------------|-----------------------------|----------------------------------|-----------------------------------| | |
| | 1 | 59 | 41 | 124 | | |
| | 4 | 57 | 42 | 123 | | |
| | 16 | 56 | 41 | 120 | | |
| **Quantized training** | |
| Currently, HIGGS doesn't support quantized training (and backward passes in general). We're working on adding support for it. |