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* Fixed typo: insted to instead * Fixed typo: relase to release * Fixed typo: nighlty to nightly * Fixed typos: versatible, benchamarks, becnhmark to versatile, benchmark, benchmarks * Fixed typo in comment: quantizd to quantized * Fixed typo: architecutre to architecture * Fixed typo: contibution to contribution * Fixed typo: Presequities to Prerequisites * Fixed typo: faste to faster * Fixed typo: extendeding to extending * Fixed typo: segmetantion_maps to segmentation_maps * Fixed typo: Alternativelly to Alternatively * Fixed incorrectly defined variable: output to output_disabled * Fixed typo in library name: tranformers.onnx to transformers.onnx * Fixed missing import: import tensorflow as tf * Fixed incorrectly defined variable: token_tensor to tokens_tensor * Fixed missing import: import torch * Fixed incorrectly defined variable and typo: uromaize to uromanize * Fixed incorrectly defined variable and typo: uromaize to uromanize * Fixed typo in function args: numpy.ndarry to numpy.ndarray * Fixed Inconsistent Library Name: Torchscript to TorchScript * Fixed Inconsistent Class Name: OneformerProcessor to OneFormerProcessor * Fixed Inconsistent Class Named Typo: TFLNetForMultipleChoice to TFXLNetForMultipleChoice * Fixed Inconsistent Library Name Typo: Pytorch to PyTorch * Fixed Inconsistent Function Name Typo: captureWarning to captureWarnings * Fixed Inconsistent Library Name Typo: Pytorch to PyTorch * Fixed Inconsistent Class Name Typo: TrainingArgument to TrainingArguments * Fixed Inconsistent Model Name Typo: Swin2R to Swin2SR * Fixed Inconsistent Model Name Typo: EART to BERT * Fixed Inconsistent Library Name Typo: TensorFLow to TensorFlow * Fixed Broken Link for Speech Emotion Classification with Wav2Vec2 * Fixed minor missing word Typo * Fixed minor missing word Typo * Fixed minor missing word Typo * Fixed minor missing word Typo * Fixed minor missing word Typo * Fixed minor missing word Typo * Fixed minor missing word Typo * Fixed minor missing word Typo * Fixed Punctuation: Two commas * Fixed Punctuation: No Space between XLM-R and is * Fixed Punctuation: No Space between [~accelerate.Accelerator.backward] and method * Added backticks to display model.fit() in codeblock * Added backticks to display openai-community/gpt2 in codeblock * Fixed Minor Typo: will to with * Fixed Minor Typo: is to are * Fixed Minor Typo: in to on * Fixed Minor Typo: inhibits to exhibits * Fixed Minor Typo: they need to it needs * Fixed Minor Typo: cast the load the checkpoints To load the checkpoints * Fixed Inconsistent Class Name Typo: TFCamembertForCasualLM to TFCamembertForCausalLM * Fixed typo in attribute name: outputs.last_hidden_states to outputs.last_hidden_state * Added missing verbosity level: fatal * Fixed Minor Typo: take To takes * Fixed Minor Typo: heuristic To heuristics * Fixed Minor Typo: setting To settings * Fixed Minor Typo: Content To Contents * Fixed Minor Typo: millions To million * Fixed Minor Typo: difference To differences * Fixed Minor Typo: while extract To which extracts * Fixed Minor Typo: Hereby To Here * Fixed Minor Typo: addition To additional * Fixed Minor Typo: supports To supported * Fixed Minor Typo: so that benchmark results TO as a consequence, benchmark * Fixed Minor Typo: a To an * Fixed Minor Typo: a To an * Fixed Minor Typo: Chain-of-though To Chain-of-thought
47 lines
2.2 KiB
Markdown
47 lines
2.2 KiB
Markdown
<!--Copyright 2024 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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specific language governing permissions and limitations under the License.
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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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# EETQ
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The [EETQ](https://github.com/NetEase-FuXi/EETQ) library supports int8 per-channel weight-only quantization for NVIDIA GPUS. The high-performance GEMM and GEMV kernels are from FasterTransformer and TensorRT-LLM. It requires no calibration dataset and does not need to pre-quantize your model. Moreover, the accuracy degradation is negligible owing to the per-channel quantization.
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Make sure you have eetq installed from the [release page](https://github.com/NetEase-FuXi/EETQ/releases)
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```
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pip install --no-cache-dir https://github.com/NetEase-FuXi/EETQ/releases/download/v1.0.0/EETQ-1.0.0+cu121+torch2.1.2-cp310-cp310-linux_x86_64.whl
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```
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or via the source code https://github.com/NetEase-FuXi/EETQ. EETQ requires CUDA capability <= 8.9 and >= 7.0
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```
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git clone https://github.com/NetEase-FuXi/EETQ.git
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cd EETQ/
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git submodule update --init --recursive
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pip install .
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```
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An unquantized model can be quantized via "from_pretrained".
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```py
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from transformers import AutoModelForCausalLM, EetqConfig
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path = "/path/to/model"
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quantization_config = EetqConfig("int8")
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model = AutoModelForCausalLM.from_pretrained(path, device_map="auto", quantization_config=quantization_config)
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```
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A quantized model can be saved via "saved_pretrained" and be reused again via the "from_pretrained".
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```py
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quant_path = "/path/to/save/quantized/model"
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model.save_pretrained(quant_path)
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model = AutoModelForCausalLM.from_pretrained(quant_path, device_map="auto")
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``` |