Update Quantization docs and minor doc fix. (#2368)
* Update Quantization docs and minor doc fix. * update readme with latest quants info * Apply suggestions from code review Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * up --------- Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
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@ -48,6 +48,8 @@ Text Generation Inference (TGI) is a toolkit for deploying and serving Large Lan
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- [GPT-Q](https://arxiv.org/abs/2210.17323)
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- [EETQ](https://github.com/NetEase-FuXi/EETQ)
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- [AWQ](https://github.com/casper-hansen/AutoAWQ)
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- [Marlin](https://github.com/IST-DASLab/marlin)
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- [fp8]()
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- [Safetensors](https://github.com/huggingface/safetensors) weight loading
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- Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
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- Logits warper (temperature scaling, top-p, top-k, repetition penalty, more details see [transformers.LogitsProcessor](https://huggingface.co/docs/transformers/internal/generation_utils#transformers.LogitsProcessor))
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@ -2080,4 +2080,4 @@
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"description": "Hugging Face Text Generation Inference API"
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}
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]
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}
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}
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@ -4,7 +4,7 @@ Text Generation Inference improves the model in several aspects.
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## Quantization
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TGI supports [bits-and-bytes](https://github.com/TimDettmers/bitsandbytes#bitsandbytes), [GPT-Q](https://arxiv.org/abs/2210.17323) and [AWQ](https://arxiv.org/abs/2306.00978) quantization. To speed up inference with quantization, simply set `quantize` flag to `bitsandbytes`, `gptq` or `awq` depending on the quantization technique you wish to use. When using GPT-Q quantization, you need to point to one of the models [here](https://huggingface.co/models?search=gptq) when using AWQ quantization, you need to point to one of the models [here](https://huggingface.co/models?search=awq). To get more information about quantization, please refer to [quantization guide](./../conceptual/quantization)
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TGI supports [bits-and-bytes](https://github.com/TimDettmers/bitsandbytes#bitsandbytes), [GPT-Q](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), [Marlin](https://github.com/IST-DASLab/marlin), [EETQ](https://github.com/NetEase-FuXi/EETQ), [EXL2](https://github.com/turboderp/exllamav2), and [fp8](https://developer.nvidia.com/blog/nvidia-arm-and-intel-publish-fp8-specification-for-standardization-as-an-interchange-format-for-ai/) quantization. To speed up inference with quantization, simply set `quantize` flag to `bitsandbytes`, `gptq`, `awq`, `marlin`, `exl2`, `eetq` or `fp8` depending on the quantization technique you wish to use. When using GPT-Q quantization, you need to point to one of the models [here](https://huggingface.co/models?search=gptq). Similarly, when using AWQ quantization, you need to point to one of [these models](https://huggingface.co/models?search=awq). To get more information about quantization, please refer to [quantization guide](./../conceptual/quantization)
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## RoPE Scaling
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@ -84,7 +84,7 @@ print(chat)
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```
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or with OpenAi's library:
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or with OpenAI's [client library](https://github.com/openai/openai-python):
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```python
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from openai import OpenAI
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@ -1,6 +1,40 @@
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# Quantization
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TGI offers GPTQ and bits-and-bytes quantization to quantize large language models.
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TGI offers many quantization schemes to run LLMs effectively and fast based on your use-case. TGI supports GPTQ, AWQ, bits-and-bytes, EETQ, Marlin, EXL2 and fp8 quantization.
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To leverage GPTQ, AWQ, Marlin and EXL2 quants, you must provide pre-quantized weights. Whereas for bits-and-bytes, EETQ and fp8, weights are quantized by TGI on the fly.
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We recommend using the official quantization scripts for creating your quants:
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1. [AWQ](https://github.com/casper-hansen/AutoAWQ/blob/main/examples/quantize.py)
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2. [GPTQ/ Marlin](https://github.com/AutoGPTQ/AutoGPTQ/blob/main/examples/quantization/basic_usage.py)
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3. [EXL2](https://github.com/turboderp/exllamav2/blob/master/doc/convert.md)
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For on-the-fly quantization you simply need to pass one of the supported quantization types and TGI takes care of the rest.
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## Quantization with bitsandbytes
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bitsandbytes is a library used to apply 8-bit and 4-bit quantization to models. Unlike GPTQ quantization, bitsandbytes doesn't require a calibration dataset or any post-processing – weights are automatically quantized on load. However, inference with bitsandbytes is slower than GPTQ or FP16 precision.
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8-bit quantization enables multi-billion parameter scale models to fit in smaller hardware without degrading performance too much.
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In TGI, you can use 8-bit quantization by adding `--quantize bitsandbytes` like below 👇
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```bash
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docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model --quantize bitsandbytes
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```
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4-bit quantization is also possible with bitsandbytes. You can choose one of the following 4-bit data types: 4-bit float (`fp4`), or 4-bit `NormalFloat` (`nf4`). These data types were introduced in the context of parameter-efficient fine-tuning, but you can apply them for inference by automatically converting the model weights on load.
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In TGI, you can use 4-bit quantization by adding `--quantize bitsandbytes-nf4` or `--quantize bitsandbytes-fp4` like below 👇
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```bash
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docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model --quantize bitsandbytes-nf4
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```
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You can get more information about 8-bit quantization by reading this [blog post](https://huggingface.co/blog/hf-bitsandbytes-integration), and 4-bit quantization by reading [this blog post](https://huggingface.co/blog/4bit-transformers-bitsandbytes).
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Use `eetq` or `fp8` for other quantization schemes.
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In addition to this, TGI allows creating GPTQ quants directly by passing the model weights and a calibration dataset.
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## Quantization with GPTQ
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@ -35,25 +69,4 @@ text-generation-launcher --model-id /data/falcon-40b-gptq/ --sharded true --num-
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You can learn more about the quantization options by running `text-generation-server quantize --help`.
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If you wish to do more with GPTQ models (e.g. train an adapter on top), you can read about transformers GPTQ integration [here](https://huggingface.co/blog/gptq-integration).
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You can learn more about GPTQ from the [paper](https://arxiv.org/pdf/2210.17323.pdf).
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## Quantization with bitsandbytes
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bitsandbytes is a library used to apply 8-bit and 4-bit quantization to models. Unlike GPTQ quantization, bitsandbytes doesn't require a calibration dataset or any post-processing – weights are automatically quantized on load. However, inference with bitsandbytes is slower than GPTQ or FP16 precision.
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8-bit quantization enables multi-billion parameter scale models to fit in smaller hardware without degrading performance too much.
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In TGI, you can use 8-bit quantization by adding `--quantize bitsandbytes` like below 👇
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```bash
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docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model --quantize bitsandbytes
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```
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4-bit quantization is also possible with bitsandbytes. You can choose one of the following 4-bit data types: 4-bit float (`fp4`), or 4-bit `NormalFloat` (`nf4`). These data types were introduced in the context of parameter-efficient fine-tuning, but you can apply them for inference by automatically converting the model weights on load.
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In TGI, you can use 4-bit quantization by adding `--quantize bitsandbytes-nf4` or `--quantize bitsandbytes-fp4` like below 👇
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```bash
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docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model --quantize bitsandbytes-nf4
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```
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You can get more information about 8-bit quantization by reading this [blog post](https://huggingface.co/blog/hf-bitsandbytes-integration), and 4-bit quantization by reading [this blog post](https://huggingface.co/blog/4bit-transformers-bitsandbytes).
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You can learn more about GPTQ from the [paper](https://arxiv.org/pdf/2210.17323.pdf).
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