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>

README.md

@@ -48,6 +48,8 @@ Text Generation Inference (TGI) is a toolkit for deploying and serving Large Lan
   - [GPT-Q](https://arxiv.org/abs/2210.17323)
   - [EETQ](https://github.com/NetEase-FuXi/EETQ)
   - [AWQ](https://github.com/casper-hansen/AutoAWQ)
+  - [Marlin](https://github.com/IST-DASLab/marlin)
+  - [fp8]()
 - [Safetensors](https://github.com/huggingface/safetensors) weight loading
 - Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
 - 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))

docs/openapi.json

@@ -2080,4 +2080,4 @@
       "description": "Hugging Face Text Generation Inference API"
     }
   ]
-}
+}

docs/source/basic_tutorials/preparing_model.md

@@ -4,7 +4,7 @@ Text Generation Inference improves the model in several aspects.
 
 ## Quantization
 
-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)
+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)
 
 
 ## RoPE Scaling

docs/source/basic_tutorials/visual_language_models.md

@@ -84,7 +84,7 @@ print(chat)
 
 ```
 
-or with OpenAi's library:
+or with OpenAI's [client library](https://github.com/openai/openai-python):
 
 ```python
 from openai import OpenAI

docs/source/conceptual/quantization.md

@@ -1,6 +1,40 @@
 # Quantization
 
-TGI offers GPTQ and bits-and-bytes quantization to quantize large language models.
+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.
+
+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.
+
+We recommend using the official quantization scripts for creating your quants:
+1. [AWQ](https://github.com/casper-hansen/AutoAWQ/blob/main/examples/quantize.py)
+2. [GPTQ/ Marlin](https://github.com/AutoGPTQ/AutoGPTQ/blob/main/examples/quantization/basic_usage.py)
+3. [EXL2](https://github.com/turboderp/exllamav2/blob/master/doc/convert.md)
+
+For on-the-fly quantization you simply need to pass one of the supported quantization types and TGI takes care of the rest. 
+
+## Quantization with bitsandbytes
+
+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.
+
+8-bit quantization enables multi-billion parameter scale models to fit in smaller hardware without degrading performance too much.
+In TGI, you can use 8-bit quantization by adding `--quantize bitsandbytes` like below 👇
+
+```bash
+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
+```
+
+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.
+
+In TGI, you can use 4-bit quantization by adding `--quantize bitsandbytes-nf4` or `--quantize bitsandbytes-fp4` like below 👇
+
+```bash
+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
+```
+
+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).
+
+Use `eetq` or `fp8` for other quantization schemes.
+
+In addition to this, TGI allows creating GPTQ quants directly by passing the model weights and a calibration dataset.
 
 ## Quantization with GPTQ
 
@@ -35,25 +69,4 @@ text-generation-launcher --model-id /data/falcon-40b-gptq/ --sharded true --num-
 You can learn more about the quantization options by running `text-generation-server quantize --help`.
 
 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).
-You can learn more about GPTQ from the [paper](https://arxiv.org/pdf/2210.17323.pdf).
+You can learn more about GPTQ from the [paper](https://arxiv.org/pdf/2210.17323.pdf).
-
-## Quantization with bitsandbytes
-
-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.
-
-8-bit quantization enables multi-billion parameter scale models to fit in smaller hardware without degrading performance too much.
-In TGI, you can use 8-bit quantization by adding `--quantize bitsandbytes` like below 👇
-
-```bash
-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
-```
-
-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.
-
-In TGI, you can use 4-bit quantization by adding `--quantize bitsandbytes-nf4` or `--quantize bitsandbytes-fp4` like below 👇
-
-```bash
-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
-```
-
-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).