Update documentation for Supported models (#2386)

* Minor doc fixes

* up.

* Other minor updates.

README.md

@@ -42,6 +42,7 @@ Text Generation Inference (TGI) is a toolkit for deploying and serving Large Lan
 - Tensor Parallelism for faster inference on multiple GPUs
 - Token streaming using Server-Sent Events (SSE)
 - Continuous batching of incoming requests for increased total throughput
+- [Messages API](https://huggingface.co/docs/text-generation-inference/en/messages_api) compatible with Open AI Chat Completion API
 - Optimized transformers code for inference using [Flash Attention](https://github.com/HazyResearch/flash-attention) and [Paged Attention](https://github.com/vllm-project/vllm) on the most popular architectures
 - Quantization with :
   - [bitsandbytes](https://github.com/TimDettmers/bitsandbytes)
@@ -49,7 +50,7 @@ Text Generation Inference (TGI) is a toolkit for deploying and serving Large Lan
   - [EETQ](https://github.com/NetEase-FuXi/EETQ)
   - [AWQ](https://github.com/casper-hansen/AutoAWQ)
   - [Marlin](https://github.com/IST-DASLab/marlin)
-  - [fp8]()
+  - [fp8](https://developer.nvidia.com/blog/nvidia-arm-and-intel-publish-fp8-specification-for-standardization-as-an-interchange-format-for-ai/)
 - [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))
@@ -94,6 +95,29 @@ curl 127.0.0.1:8080/generate_stream \
     -H 'Content-Type: application/json'
 ```
 
+You can also use [TGI's Messages API](https://huggingface.co/docs/text-generation-inference/en/messages_api) to obtain Open AI Chat Completion API compatible responses.
+
+```bash
+curl localhost:3000/v1/chat/completions \
+    -X POST \
+    -d '{
+  "model": "tgi",
+  "messages": [
+    {
+      "role": "system",
+      "content": "You are a helpful assistant."
+    },
+    {
+      "role": "user",
+      "content": "What is deep learning?"
+    }
+  ],
+  "stream": true,
+  "max_tokens": 20
+}' \
+    -H 'Content-Type: application/json'
+```
+
 **Note:** To use NVIDIA GPUs, you need to install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). We also recommend using NVIDIA drivers with CUDA version 12.2 or higher. For running the Docker container on a machine with no GPUs or CUDA support, it is enough to remove the `--gpus all` flag and add `--disable-custom-kernels`, please note CPU is not the intended platform for this project, so performance might be subpar.
 
 **Note:** TGI supports AMD Instinct MI210 and MI250 GPUs. Details can be found in the [Supported Hardware documentation](https://huggingface.co/docs/text-generation-inference/supported_models#supported-hardware). To use AMD GPUs, please use `docker run --device /dev/kfd --device /dev/dri --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.2.0-rocm --model-id $model` instead of the command above.
@@ -122,7 +146,7 @@ For example, if you want to serve the gated Llama V2 model variants:
 or with Docker:
 
 ```shell
-model=meta-llama/Llama-2-7b-chat-hf
+model=meta-llama/Meta-Llama-3.1-8B-Instruct
 volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
 token=<your cli READ token>
 
@@ -234,7 +258,7 @@ text-generation-launcher --model-id mistralai/Mistral-7B-Instruct-v0.2
 
 ### Quantization
 
-You can also quantize the weights with bitsandbytes to reduce the VRAM requirement:
+You can also run pre-quantized weights (AWQ, GPTQ, Marlin) or on-the-fly quantize weights with bitsandbytes, EETQ, fp8, to reduce the VRAM requirement:
 
 ```shell
 text-generation-launcher --model-id mistralai/Mistral-7B-Instruct-v0.2 --quantize
@@ -242,6 +266,8 @@ text-generation-launcher --model-id mistralai/Mistral-7B-Instruct-v0.2 --quantiz
 
 4bit quantization is available using the [NF4 and FP4 data types from bitsandbytes](https://arxiv.org/pdf/2305.14314.pdf). It can be enabled by providing `--quantize bitsandbytes-nf4` or `--quantize bitsandbytes-fp4` as a command line argument to `text-generation-launcher`.
 
+Read more about quantization in the [Quantization documentation](https://huggingface.co/docs/text-generation-inference/en/conceptual/quantization).
+
 ## Develop
 
 ```shell

docs/source/conceptual/quantization.md

@@ -11,7 +11,7 @@ We recommend using the official quantization scripts for creating your quants:
 
 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
+## Quantization with bitsandbytes, EETQ & fp8
 
 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.
 
@@ -32,7 +32,7 @@ docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingf
 
 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.
+Similarly you can use pass you can pass `--quantize eetq` or `--quantize fp8` for respective quantization schemes.
 
 In addition to this, TGI allows creating GPTQ quants directly by passing the model weights and a calibration dataset.
 

docs/source/quicktour.md

@@ -21,7 +21,7 @@ TGI supports various hardware. Make sure to check the [Using TGI with Nvidia GPU
 
 ## Consuming TGI
 
-Once TGI is running, you can use the `generate` endpoint by doing requests. To learn more about how to query the endpoints, check the [Consuming TGI](./basic_tutorials/consuming_tgi) section, where we show examples with utility libraries and UIs. Below you can see a simple snippet to query the endpoint.
+Once TGI is running, you can use the `generate` endpoint or the Open AI Chat Completion API compatible [Messages API](https://huggingface.co/docs/text-generation-inference/en/messages_api) by doing requests. To learn more about how to query the endpoints, check the [Consuming TGI](./basic_tutorials/consuming_tgi) section, where we show examples with utility libraries and UIs. Below you can see a simple snippet to query the endpoint.
 
 <inferencesnippet>
 <python>

docs/source/supported_models.md

@@ -1,22 +1,22 @@
 
 # Supported Models and Hardware
 
-Text Generation Inference enables serving optimized models on specific hardware for the highest performance. The following sections list which models are hardware are supported.
+Text Generation Inference enables serving optimized models on specific hardware for the highest performance. The following sections list which models (VLMs & LLMs) are supported.
 
 ## Supported Models
 
 - [Deepseek V2](https://huggingface.co/deepseek-ai/DeepSeek-V2)
 - [Idefics 2](https://huggingface.co/HuggingFaceM4/idefics2-8b) (Multimodal)
 - [Llava Next (1.6)](https://huggingface.co/llava-hf/llava-v1.6-vicuna-13b-hf) (Multimodal)
-- [Llama](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
+- [Llama](https://huggingface.co/collections/meta-llama/llama-31-669fc079a0c406a149a5738f)
 - [Phi 3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)
 - [Gemma](https://huggingface.co/google/gemma-7b)
 - [PaliGemma](https://huggingface.co/google/paligemma-3b-pt-224)
-- [Gemma2](https://huggingface.co/google/gemma2-9b)
+- [Gemma2](https://huggingface.co/collections/google/gemma-2-release-667d6600fd5220e7b967f315)
 - [Cohere](https://huggingface.co/CohereForAI/c4ai-command-r-plus)
 - [Dbrx](https://huggingface.co/databricks/dbrx-instruct)
 - [Mamba](https://huggingface.co/state-spaces/mamba-2.8b-slimpj)
-- [Mistral](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
+- [Mistral](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407)
 - [Mixtral](https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1)
 - [Gpt Bigcode](https://huggingface.co/bigcode/gpt_bigcode-santacoder)
 - [Phi](https://huggingface.co/microsoft/phi-1_5)

server/text_generation_server/models/__init__.py

@@ -180,7 +180,7 @@ class ModelType(enum.Enum):
     LLAMA = {
         "type": "llama",
         "name": "Llama",
-        "url": "https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct",
+        "url": "https://huggingface.co/collections/meta-llama/llama-31-669fc079a0c406a149a5738f",
     }
     PHI3 = {
         "type": "phi3",
@@ -200,7 +200,7 @@ class ModelType(enum.Enum):
     GEMMA2 = {
         "type": "gemma2",
         "name": "Gemma2",
-        "url": "https://huggingface.co/google/gemma2-9b",
+        "url": "https://huggingface.co/collections/google/gemma-2-release-667d6600fd5220e7b967f315",
     }
     COHERE = {
         "type": "cohere",
@@ -220,7 +220,7 @@ class ModelType(enum.Enum):
     MISTRAL = {
         "type": "mistral",
         "name": "Mistral",
-        "url": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2",
+        "url": "https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407",
     }
     MIXTRAL = {
         "type": "mixtral",

update_doc.py

@@ -7,7 +7,7 @@ import os
 TEMPLATE = """
 # Supported Models and Hardware
 
-Text Generation Inference enables serving optimized models on specific hardware for the highest performance. The following sections list which models are hardware are supported.
+Text Generation Inference enables serving optimized models on specific hardware for the highest performance. The following sections list which models (VLMs & LLMs) are supported.
 
 ## Supported Models