Change TRT-LLM docker to 1.2.0rc0.post1

Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>

Author: kevalmorabia97

.github/workflows/example_tests.yml

@@ -65,7 +65,7 @@ jobs:
       matrix:
         EXAMPLE: [llm_ptq]
     container: &example_container
-      image: nvcr.io/nvidia/tensorrt-llm/release:1.2.0rc2
+      image: nvcr.io/nvidia/tensorrt-llm/release:1.2.0rc0.post1
       env:
         PIP_CONSTRAINT: "" # Disable pip constraint for upgrading packages
         HF_TOKEN: ${{ secrets.HF_TOKEN }}

.gitlab/tests.yml

@@ -62,7 +62,7 @@ example-torch:
 example-trtllm:
   extends: example-torch
   timeout: 60m
-  image: nvcr.io/nvidia/tensorrt-llm/release:1.2.0rc2
+  image: nvcr.io/nvidia/tensorrt-llm/release:1.2.0rc0.post1
   tags: [docker, linux, 2-gpu, sm>=89]
   parallel:
     matrix:

CHANGELOG.rst

@@ -23,7 +23,7 @@ Model Optimizer Changelog (Linux)
 
 **Misc**
 
-- Bump TensorRT-LLM docker to 1.2.0rc2.
+- Bump TensorRT-LLM docker to 1.2.0rc0.post1.
 - Bump minimum recommended transformers version to 4.53.
 
 0.39 (2025-11-11)

docs/source/getting_started/_installation_for_Linux.rst

@@ -18,7 +18,7 @@ Latest Model Optimizer (``nvidia-modelopt``) currently has the following system
 +-------------------------+-----------------------------+
 | PyTorch                 |  >=2.6                      |
 +-------------------------+-----------------------------+
-| TensorRT-LLM (Optional) |  1.2.0rc2                   |
+| TensorRT-LLM (Optional) |  1.2.0rc0.post1             |
 +-------------------------+-----------------------------+
 | ONNX Runtime (Optional) |  1.22                       |
 +-------------------------+-----------------------------+

examples/llm_ptq/README.md

@@ -27,7 +27,7 @@ This section focuses on Post-training quantization, a technique that reduces mod
 
 ### Docker
 
-For Hugging Face models, please use the TensorRT-LLM docker image (e.g., `nvcr.io/nvidia/tensorrt-llm/release:1.2.0rc2`).
+For Hugging Face models, please use the TensorRT-LLM docker image (e.g., `nvcr.io/nvidia/tensorrt-llm/release:1.2.0rc0.post1`).
 For NeMo models, use the NeMo container (e.g., `nvcr.io/nvidia/nemo:25.09`).
 Visit our [installation docs](https://nvidia.github.io/TensorRT-Model-Optimizer/getting_started/2_installation.html) for more information.