diff --git a/Cargo.lock b/Cargo.lock
index e63d1540..4b673c80 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -3881,7 +3881,7 @@ dependencies = [
  "openssl-probe",
  "rustls-pki-types",
  "schannel",
- "security-framework 3.0.1",
+ "security-framework 3.0.2",
 ]
 
 [[package]]
@@ -3978,7 +3978,7 @@ dependencies = [
 
 [[package]]
 name = "security-framework"
-version = "3.0.1"
+version = "3.0.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "e1415a607e92bec364ea2cf9264646dcce0f91e6d65281bd6f2819cca3bf39c8"
 dependencies = [
diff --git a/README.md b/README.md
index 9842a2a7..6072c9bd 100644
--- a/README.md
+++ b/README.md
@@ -84,7 +84,7 @@ model=HuggingFaceH4/zephyr-7b-beta
 volume=$PWD/data
 
 docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data \
-    ghcr.io/huggingface/text-generation-inference:3.0.0 --model-id $model
+    ghcr.io/huggingface/text-generation-inference:3.0.2 --model-id $model
 ```
 
 And then you can make requests like
@@ -121,7 +121,7 @@ curl localhost:8080/v1/chat/completions \
 
 **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/installation_amd#using-tgi-with-amd-gpus). 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:3.0.0-rocm --model-id $model` instead of the command above.
+**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/installation_amd#using-tgi-with-amd-gpus). 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:3.0.2-rocm --model-id $model` instead of the command above.
 
 To see all options to serve your models (in the [code](https://github.com/huggingface/text-generation-inference/blob/main/launcher/src/main.rs) or in the cli):
 ```
@@ -152,7 +152,7 @@ volume=$PWD/data # share a volume with the Docker container to avoid downloading
 token=<your cli READ token>
 
 docker run --gpus all --shm-size 1g -e HF_TOKEN=$token -p 8080:80 -v $volume:/data \
-    ghcr.io/huggingface/text-generation-inference:3.0.0 --model-id $model
+    ghcr.io/huggingface/text-generation-inference:3.0.2 --model-id $model
 ```
 
 ### A note on Shared Memory (shm)
diff --git a/docs/source/basic_tutorials/gated_model_access.md b/docs/source/basic_tutorials/gated_model_access.md
index 7c32f0b3..6520a6ab 100644
--- a/docs/source/basic_tutorials/gated_model_access.md
+++ b/docs/source/basic_tutorials/gated_model_access.md
@@ -19,6 +19,6 @@ docker run --gpus all \
     --shm-size 1g \
     -e HF_TOKEN=$token \
     -p 8080:80 \
-    -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.0.1 \
+    -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.0.2 \
     --model-id $model
 ```
diff --git a/docs/source/conceptual/quantization.md b/docs/source/conceptual/quantization.md
index f7cc8929..a0eb0c51 100644
--- a/docs/source/conceptual/quantization.md
+++ b/docs/source/conceptual/quantization.md
@@ -19,7 +19,7 @@ bitsandbytes is a library used to apply 8-bit and 4-bit quantization to models.
 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:3.0.1 --model-id $model --quantize bitsandbytes
+docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.0.2 --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.
@@ -27,7 +27,7 @@ docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingf
 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:3.0.1 --model-id $model --quantize bitsandbytes-nf4
+docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.0.2 --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).
@@ -48,7 +48,7 @@ $$({\hat{W}_{l}}^{*} = argmin_{\hat{W_{l}}} ||W_{l}X-\hat{W}_{l}X||^{2}_{2})$$
 TGI allows you to both run an already GPTQ quantized model (see available models [here](https://huggingface.co/models?search=gptq)) or quantize a model of your choice using quantization script. You can run a quantized model by simply passing --quantize like below 👇
 
 ```bash
-docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.0.1 --model-id $model --quantize gptq
+docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.0.2 --model-id $model --quantize gptq
 ```
 
 Note that TGI's GPTQ implementation doesn't use [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) under the hood. However, models quantized using AutoGPTQ or Optimum can still be served by TGI.
diff --git a/docs/source/installation_amd.md b/docs/source/installation_amd.md
index 033add75..797f0a5d 100644
--- a/docs/source/installation_amd.md
+++ b/docs/source/installation_amd.md
@@ -11,7 +11,7 @@ volume=$PWD/data # share a volume with the Docker container to avoid downloading
 docker run --rm -it --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \
     --device=/dev/kfd --device=/dev/dri --group-add video \
     --ipc=host --shm-size 256g --net host -v $volume:/data \
-    ghcr.io/huggingface/text-generation-inference:3.0.1-rocm \
+    ghcr.io/huggingface/text-generation-inference:3.0.2-rocm \
     --model-id $model
 ```
 
diff --git a/docs/source/installation_intel.md b/docs/source/installation_intel.md
index 3329b476..165d1105 100644
--- a/docs/source/installation_intel.md
+++ b/docs/source/installation_intel.md
@@ -12,7 +12,7 @@ volume=$PWD/data # share a volume with the Docker container to avoid downloading
 docker run --rm --privileged --cap-add=sys_nice \
     --device=/dev/dri \
     --ipc=host --shm-size 1g --net host -v $volume:/data \
-    ghcr.io/huggingface/text-generation-inference:3.0.1-intel-xpu \
+    ghcr.io/huggingface/text-generation-inference:3.0.2-intel-xpu \
     --model-id $model --cuda-graphs 0
 ```
 
@@ -29,7 +29,7 @@ volume=$PWD/data # share a volume with the Docker container to avoid downloading
 docker run --rm --privileged --cap-add=sys_nice \
     --device=/dev/dri \
     --ipc=host --shm-size 1g --net host -v $volume:/data \
-    ghcr.io/huggingface/text-generation-inference:3.0.1-intel-cpu \
+    ghcr.io/huggingface/text-generation-inference:3.0.2-intel-cpu \
     --model-id $model --cuda-graphs 0
 ```
 
diff --git a/docs/source/installation_nvidia.md b/docs/source/installation_nvidia.md
index 71f3febd..0092f5ef 100644
--- a/docs/source/installation_nvidia.md
+++ b/docs/source/installation_nvidia.md
@@ -11,7 +11,7 @@ model=teknium/OpenHermes-2.5-Mistral-7B
 volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
 
 docker run --gpus all --shm-size 64g -p 8080:80 -v $volume:/data \
-    ghcr.io/huggingface/text-generation-inference:3.0.1 \
+    ghcr.io/huggingface/text-generation-inference:3.0.2 \
     --model-id $model
 ```
 
diff --git a/docs/source/quicktour.md b/docs/source/quicktour.md
index fc24a7a4..4ffb9728 100644
--- a/docs/source/quicktour.md
+++ b/docs/source/quicktour.md
@@ -11,7 +11,7 @@ model=teknium/OpenHermes-2.5-Mistral-7B
 volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
 
 docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data \
-    ghcr.io/huggingface/text-generation-inference:3.0.1 \
+    ghcr.io/huggingface/text-generation-inference:3.0.2 \
     --model-id $model
 ```
 
@@ -96,7 +96,7 @@ curl 127.0.0.1:8080/generate \
 To see all possible deploy flags and options, you can use the `--help` flag. It's possible to configure the number of shards, quantization, generation parameters, and more.
 
 ```bash
-docker run ghcr.io/huggingface/text-generation-inference:3.0.1 --help
+docker run ghcr.io/huggingface/text-generation-inference:3.0.2 --help
 ```
 
 </Tip>
diff --git a/docs/source/reference/api_reference.md b/docs/source/reference/api_reference.md
index 6b333fdc..f7723187 100644
--- a/docs/source/reference/api_reference.md
+++ b/docs/source/reference/api_reference.md
@@ -163,7 +163,7 @@ hub = {
 
 # create Hugging Face Model Class
 huggingface_model = HuggingFaceModel(
- image_uri=get_huggingface_llm_image_uri("huggingface",version="3.0.1"),
+ image_uri=get_huggingface_llm_image_uri("huggingface",version="3.0.2"),
  env=hub,
  role=role,
 )