[OMNIML-3036][doc] Re-branding TensorRT-Model-Optimizer as Nvidia Model-Optimizer (NVIDIA#9679)

cjluo-nv · web-flow · commit d252101a769e · 2025-12-07T07:14:05.000-08:00
Signed-off-by: Chenjie Luo &lt;chenjiel@nvidia.com&gt;
diff --git a/ATTRIBUTIONS-Python.md b/ATTRIBUTIONS-Python.md
@@ -25486,7 +25486,7 @@ limitations under the License.
 ```
 
 ### URLs
-  - `Homepage`: https://github.com/NVIDIA/TensorRT-Model-Optimizer
+  - `Homepage`: https://github.com/NVIDIA/Model-Optimizer
 
 
 ## nvidia-modelopt-core (0.33.1)
@@ -25513,7 +25513,7 @@ limitations under the License.
 ```
 
 ### URLs
-  - `Homepage`: https://github.com/NVIDIA/TensorRT-Model-Optimizer
+  - `Homepage`: https://github.com/NVIDIA/Model-Optimizer
 
 
 ## nvidia-nccl-cu12 (2.27.3)
diff --git a/README.md b/README.md
@@ -164,7 +164,7 @@ state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs.<
 [➡️ link](https://www.bentoml.com/blog/tuning-tensor-rt-llm-for-optimal-serving-with-bentoml)
 
 
-* [2024/08/20] 🏎️SDXL with #TensorRT Model Optimizer ⏱️⚡ 🏁 cache diffusion 🏁 quantization aware training 🏁 QLoRA 🏁 #Python 3.12
+* [2024/08/20] 🏎️SDXL with #Model Optimizer ⏱️⚡ 🏁 cache diffusion 🏁 quantization aware training 🏁 QLoRA 🏁 #Python 3.12
 [➡️ link](https://developer.nvidia.com/blog/nvidia-tensorrt-model-optimizer-v0-15-boosts-inference-performance-and-expands-model-support/)
 
 * [2024/08/13] 🐍 DIY Code Completion with #Mamba ⚡ #TensorRT #LLM for speed 🤖 NIM for ease ☁️ deploy anywhere
@@ -209,7 +209,7 @@ Technical Deep Dive for serious coders ✅+99% compression ✅1 set of weights 
 * [2024/05/21] ✨@modal_labs has the codes for serverless @AIatMeta Llama 3 on #TensorRT #LLM ✨👀 📚 Marvelous Modal Manual:
 Serverless TensorRT LLM (LLaMA 3 8B) | Modal Docs [➡️ link](https://modal.com/docs/examples/trtllm_llama)
 
-* [2024/05/08] NVIDIA TensorRT Model Optimizer -- the newest member of the #TensorRT ecosystem is a library of post-training and training-in-the-loop model optimization techniques ✅quantization ✅sparsity ✅QAT [➡️ blog](https://developer.nvidia.com/blog/accelerate-generative-ai-inference-performance-with-nvidia-tensorrt-model-optimizer-now-publicly-available/)
+* [2024/05/08] NVIDIA Model Optimizer -- the newest member of the #TensorRT ecosystem is a library of post-training and training-in-the-loop model optimization techniques ✅quantization ✅sparsity ✅QAT [➡️ blog](https://developer.nvidia.com/blog/accelerate-generative-ai-inference-performance-with-nvidia-tensorrt-model-optimizer-now-publicly-available/)
 
 * [2024/05/07] 🦙🦙🦙 24,000 tokens per second 🛫Meta Llama 3 takes off with #TensorRT #LLM 📚[➡️ link](https://blogs.nvidia.com/blog/meta-llama3-inference-acceleration/)
 
diff --git a/docs/source/blogs/tech_blog/blog14_Scaling_Expert_Parallelism_in_TensorRT-LLM_part3.md b/docs/source/blogs/tech_blog/blog14_Scaling_Expert_Parallelism_in_TensorRT-LLM_part3.md
@@ -46,7 +46,7 @@ In this third blog of our scaling Expert Parallelism (EP) series, we push the pe
 
 The wo GEMM is the final linear layer within the multi-head attention block that produces the final outputs. While DeepSeek R1's MLA modifies the initial projections for keys and values, the wo GEMM operator remains a critical and standard component for finalizing the attention computation. In the term, "wo" is the abbreviation for the weight matrix for the output.
 
-We've evaluated that quantizing the wo GEMM to FP4 still satisfies the accuracy requirements, maintaining a similar MTP accept rate (AR) while improving end-to-end performance. The [NVIDIA TensorRT Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer) team has published checkpoints that additionally quantize the wo module in attention layers to FP4 on HuggingFace:
+We've evaluated that quantizing the wo GEMM to FP4 still satisfies the accuracy requirements, maintaining a similar MTP accept rate (AR) while improving end-to-end performance. The [NVIDIA Model Optimizer](https://github.com/NVIDIA/Model-Optimizer) team has published checkpoints that additionally quantize the wo module in attention layers to FP4 on HuggingFace:
 * https://huggingface.co/nvidia/DeepSeek-R1-FP4-v2
 * https://huggingface.co/nvidia/DeepSeek-R1-0528-FP4-v2
 
diff --git a/docs/source/blogs/tech_blog/blog1_Pushing_Latency_Boundaries_Optimizing_DeepSeek-R1_Performance_on_NVIDIA_B200_GPUs.md b/docs/source/blogs/tech_blog/blog1_Pushing_Latency_Boundaries_Optimizing_DeepSeek-R1_Performance_on_NVIDIA_B200_GPUs.md
@@ -67,7 +67,7 @@ We have explored a mixed precision recipe, which provides a better tradeoff betw
 
 *TensorRT LLM already supports [FP8 Attention](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/models/core/deepseek_v3#fp8-kv-cache-and-mla) while for this latency scenario low-precision attention computation doesn't help with performance so we choose to use bf16 precision for the Attention Modules.
 
-** nvfp4 model checkpoint is generated by the [NVIDIA TensorRT Model Optimizer toolkit](https://github.com/NVIDIA/TensorRT-Model-Optimizer).
+** nvfp4 model checkpoint is generated by the [NVIDIA Model Optimizer toolkit](https://github.com/NVIDIA/Model-Optimizer).
 
 *** RouterGEMM uses bf16 inputs/weights with fp32 outputs for numerical stability
 
diff --git a/docs/source/blogs/tech_blog/blog3_Optimizing_DeepSeek_R1_Throughput_on_NVIDIA_Blackwell_GPUs.md b/docs/source/blogs/tech_blog/blog3_Optimizing_DeepSeek_R1_Throughput_on_NVIDIA_Blackwell_GPUs.md
@@ -29,7 +29,7 @@ The mixed precision recipe for DeepSeek R1 throughput scenario is almost the sam
 * FP8 KV cache and FP8 attention, rather than BF16 precision.
 * FP4 Allgather for better communication bandwidth utilization.
 
-The checkpoint used in this blog is hosted in [nvidia/DeepSeek-R1-FP4](https://huggingface.co/nvidia/DeepSeek-R1-FP4), generated by [NVIDIA Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer). The accuracy score of common dataset on this FP4 checkpoint and TensorRT LLM implementations are:
+The checkpoint used in this blog is hosted in [nvidia/DeepSeek-R1-FP4](https://huggingface.co/nvidia/DeepSeek-R1-FP4), generated by [NVIDIA Model Optimizer](https://github.com/NVIDIA/Model-Optimizer). The accuracy score of common dataset on this FP4 checkpoint and TensorRT LLM implementations are:
 
 | Precision | GPQA Diamond | MATH-500
 | :-- | :-- | :-- |
diff --git a/docs/source/developer-guide/perf-benchmarking.md b/docs/source/developer-guide/perf-benchmarking.md
@@ -423,10 +423,10 @@ checkpoint. For the Llama-3.1 models, TensorRT LLM provides the following checkp
 - [`nvidia/Llama-3.1-70B-Instruct-FP8`](https://huggingface.co/nvidia/Llama-3.1-70B-Instruct-FP8)
 - [`nvidia/Llama-3.1-405B-Instruct-FP8`](https://huggingface.co/nvidia/Llama-3.1-405B-Instruct-FP8)
 
-To understand more about how to quantize your own checkpoints, refer to ModelOpt [documentation](https://nvidia.github.io/TensorRT-Model-Optimizer/deployment/1_tensorrt_llm.html).
+To understand more about how to quantize your own checkpoints, refer to ModelOpt [documentation](https://nvidia.github.io/Model-Optimizer/deployment/1_tensorrt_llm.html).
 
 `trtllm-bench` utilizes the `hf_quant_config.json` file present in the pre-quantized checkpoints above. The configuration
-file is present in checkpoints quantized with [TensorRT Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer)
+file is present in checkpoints quantized with [Model Optimizer](https://github.com/NVIDIA/Model-Optimizer)
 and describes the compute and KV cache quantization that checkpoint was compiled with. For example, from the checkpoints
 above:
 
diff --git a/docs/source/developer-guide/perf-overview.md b/docs/source/developer-guide/perf-overview.md
@@ -21,7 +21,7 @@ and shows the throughput scenario under maximum load. The reported metric is `To
 
 The performance numbers below were collected using the steps described in this document.
 
-Testing was performed on models with weights quantized using [ModelOpt](https://nvidia.github.io/TensorRT-Model-Optimizer/#) and published by NVIDIA on the [Model Optimizer HuggingFace Collection](https://huggingface.co/collections/nvidia/model-optimizer-66aa84f7966b3150262481a4).
+Testing was performed on models with weights quantized using [ModelOpt](https://nvidia.github.io/Model-Optimizer/#) and published by NVIDIA on the [Model Optimizer HuggingFace Collection](https://huggingface.co/collections/nvidia/model-optimizer-66aa84f7966b3150262481a4).
 
 *(NEW for v1.0) RTX 6000 Pro Blackwell Server Edition Benchmarks:*
 
diff --git a/docs/source/features/auto_deploy/support_matrix.md b/docs/source/features/auto_deploy/support_matrix.md
@@ -120,7 +120,7 @@ Optimize attention operations with different attention kernel implementations:
 
 ### Precision Support
 
-AutoDeploy supports models with various precision formats, including quantized checkpoints generated by [`TensorRT-Model-Optimizer`](https://github.com/NVIDIA/TensorRT-Model-Optimizer).
+AutoDeploy supports models with various precision formats, including quantized checkpoints generated by [`Model-Optimizer`](https://github.com/NVIDIA/Model-Optimizer).
 
 **Supported precision types include:**
 
diff --git a/docs/source/features/quantization.md b/docs/source/features/quantization.md
@@ -23,7 +23,7 @@ The default PyTorch backend supports FP4 and FP8 quantization on the latest Blac
 
 ### Running Pre-quantized Models
 
-TensorRT LLM can directly run [pre-quantized models](https://huggingface.co/collections/nvidia/model-optimizer-66aa84f7966b3150262481a4) generated with the [NVIDIA TensorRT Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer).
+TensorRT LLM can directly run [pre-quantized models](https://huggingface.co/collections/nvidia/model-optimizer-66aa84f7966b3150262481a4) generated with the [NVIDIA Model Optimizer](https://github.com/NVIDIA/Model-Optimizer).
 
 ```python
 from tensorrt_llm import LLM
@@ -54,8 +54,8 @@ If a pre-quantized model is not available on the [Hugging Face Hub](https://hugg
 Follow this step-by-step guide to quantize a model:
 
 ```bash
-git clone https://github.com/NVIDIA/TensorRT-Model-Optimizer.git
-cd TensorRT-Model-Optimizer/examples/llm_ptq
+git clone https://github.com/NVIDIA/Model-Optimizer.git
+cd Model-Optimizer/examples/llm_ptq
 scripts/huggingface_example.sh --model <huggingface_model_card> --quant fp8 --export_fmt hf
 ```
 
@@ -108,4 +108,4 @@ FP8 block wise scaling GEMM kernels for sm100 are using MXFP8 recipe (E4M3 act/w
 ## Quick Links
 
 - [Pre-quantized Models by ModelOpt](https://huggingface.co/collections/nvidia/model-optimizer-66aa84f7966b3150262481a4)
-- [ModelOpt Support Matrix](https://nvidia.github.io/TensorRT-Model-Optimizer/guides/0_support_matrix.html)
+- [ModelOpt Support Matrix](https://nvidia.github.io/Model-Optimizer/guides/0_support_matrix.html)
diff --git a/docs/source/legacy/performance/perf-benchmarking.md b/docs/source/legacy/performance/perf-benchmarking.md
@@ -662,7 +662,7 @@ checkpoint. For the Llama-3.1 models, TensorRT-LLM provides the following checkp
 - [`nvidia/Llama-3.1-405B-Instruct-FP8`](https://huggingface.co/nvidia/Llama-3.1-405B-Instruct-FP8)
 
 `trtllm-bench` utilizes the `hf_quant_config.json` file present in the pre-quantized checkpoints above. The configuration
-file is present in checkpoints quantized with [TensorRT Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer)
+file is present in checkpoints quantized with [Model Optimizer](https://github.com/NVIDIA/Model-Optimizer)
 and describes the compute and KV cache quantization that checkpoint was compiled with. For example, from the checkpoints
 above:
 
diff --git a/docs/source/torch/auto_deploy/support_matrix.md b/docs/source/torch/auto_deploy/support_matrix.md
@@ -118,7 +118,7 @@ Optimize attention operations with different attention kernel implementations:
 
 ### Precision Support
 
-AutoDeploy supports models with various precision formats, including quantized checkpoints generated by [`TensorRT-Model-Optimizer`](https://github.com/NVIDIA/TensorRT-Model-Optimizer).
+AutoDeploy supports models with various precision formats, including quantized checkpoints generated by [`Model-Optimizer`](https://github.com/NVIDIA/Model-Optimizer).
 
 **Supported precision types include:**
 
diff --git a/docs/source/torch/features/quantization.md b/docs/source/torch/features/quantization.md
@@ -1,7 +1,7 @@
 # Quantization
 
 The PyTorch backend supports FP8 and NVFP4 quantization. You can pass quantized models in HF model hub,
-which are generated by [TensorRT Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer).
+which are generated by [Model Optimizer](https://github.com/NVIDIA/Model-Optimizer).
 
 ```python
 from tensorrt_llm._torch import LLM
@@ -12,7 +12,7 @@ llm.generate("Hello, my name is")
 Or you can try the following commands to get a quantized model by yourself:
 
 ```bash
-git clone https://github.com/NVIDIA/TensorRT-Model-Optimizer.git
-cd TensorRT-Model-Optimizer/examples/llm_ptq
+git clone https://github.com/NVIDIA/Model-Optimizer.git
+cd Model-Optimizer/examples/llm_ptq
 scripts/huggingface_example.sh --model <huggingface_model_card> --quant fp8 --export_fmt hf
 ```
diff --git a/examples/auto_deploy/README.md b/examples/auto_deploy/README.md
@@ -90,16 +90,16 @@ python lm_eval_ad.py \
 --model autodeploy --model_args model=meta-llama/Meta-Llama-3.1-8B-Instruct,world_size=2 --tasks mmlu
 ```
 
-### Mixed-precision Quantization using TensorRT Model Optimizer
+### Mixed-precision Quantization using Model Optimizer
 
-TensorRT Model Optimizer [AutoQuantize](https://nvidia.github.io/TensorRT-Model-Optimizer/reference/generated/modelopt.torch.quantization.model_quant.html#modelopt.torch.quantization.model_quant.auto_quantize) algorithm is a PTQ algorithm from ModelOpt which quantizes a model by searching for the best quantization format per-layer while meeting the performance constraint specified by the user. This way, `AutoQuantize` enables to trade-off model accuracy for performance.
+Model Optimizer [AutoQuantize](https://nvidia.github.io/Model-Optimizer/reference/generated/modelopt.torch.quantization.model_quant.html#modelopt.torch.quantization.model_quant.auto_quantize) algorithm is a PTQ algorithm from ModelOpt which quantizes a model by searching for the best quantization format per-layer while meeting the performance constraint specified by the user. This way, `AutoQuantize` enables to trade-off model accuracy for performance.
 
 Currently `AutoQuantize` supports only `effective_bits` as the performance constraint (for both weight-only quantization and weight & activation quantization). See
-[AutoQuantize documentation](https://nvidia.github.io/TensorRT-Model-Optimizer/reference/generated/modelopt.torch.quantization.model_quant.html#modelopt.torch.quantization.model_quant.auto_quantize) for more details.
+[AutoQuantize documentation](https://nvidia.github.io/Model-Optimizer/reference/generated/modelopt.torch.quantization.model_quant.html#modelopt.torch.quantization.model_quant.auto_quantize) for more details.
 
 #### 1. Quantize a model with ModelOpt
 
-Refer to [NVIDIA TensorRT Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/examples/llm_autodeploy/README.md) for generating quantized model checkpoint.
+Refer to [NVIDIA Model Optimizer](https://github.com/NVIDIA/Model-Optimizer/blob/main/examples/llm_autodeploy/README.md) for generating quantized model checkpoint.
 
 #### 2. Deploy the quantized model with AutoDeploy
 
diff --git a/examples/disaggregated/README.md b/examples/disaggregated/README.md
@@ -212,7 +212,7 @@ In disaggregated serving, the context workers and generation workers have differ
 ### Prerequisites
 
 To enable mixed precision serving, you will need:
-1. A quantized checkpoint created with [TensorRT Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer)
+1. A quantized checkpoint created with [Model Optimizer](https://github.com/NVIDIA/Model-Optimizer)
 2. The original unquantized checkpoint (Can also be quantized)
 3. Both checkpoints must use the same KV cache dtype to ensure compatibility during transfer
 
diff --git a/examples/llm-api/_tensorrt_engine/llm_medusa_decoding.py b/examples/llm-api/_tensorrt_engine/llm_medusa_decoding.py
@@ -29,7 +29,7 @@ def run_medusa_decoding(use_modelopt_ckpt=False, model_dir=None):
     llm_kwargs = {}
 
     if use_modelopt_ckpt:
-        # This is a Llama-3.1-8B combined with Medusa heads provided by TensorRT Model Optimizer.
+        # This is a Llama-3.1-8B combined with Medusa heads provided by Model Optimizer.
         # Both the base model (except lm_head) and Medusa heads have been quantized in FP8.
         model = model_dir or "nvidia/Llama-3.1-8B-Medusa-FP8"
 
@@ -85,7 +85,7 @@ def run_medusa_decoding(use_modelopt_ckpt=False, model_dir=None):
     parser.add_argument(
         '--use_modelopt_ckpt',
         action='store_true',
-        help="Use FP8-quantized checkpoint from TensorRT Model Optimizer.")
+        help="Use FP8-quantized checkpoint from Model Optimizer.")
     # TODO: remove this arg after ModelOpt ckpt is public on HF
     parser.add_argument('--model_dir', type=Path, default=None)
     args = parser.parse_args()
diff --git a/examples/llm-api/_tensorrt_engine/quickstart_example.py b/examples/llm-api/_tensorrt_engine/quickstart_example.py
@@ -9,7 +9,7 @@ def main():
     build_config.max_num_tokens = 1024
 
     # Model could accept HF model name, a path to local HF model,
-    # or TensorRT Model Optimizer's quantized checkpoints like nvidia/Llama-3.1-8B-Instruct-FP8 on HF.
+    # or Model Optimizer's quantized checkpoints like nvidia/Llama-3.1-8B-Instruct-FP8 on HF.
     llm = LLM(model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
               build_config=build_config)
 
diff --git a/examples/llm-api/llm_inference.py b/examples/llm-api/llm_inference.py
@@ -7,7 +7,7 @@
 def main():
 
     # Model could accept HF model name, a path to local HF model,
-    # or TensorRT Model Optimizer's quantized checkpoints like nvidia/Llama-3.1-8B-Instruct-FP8 on HF.
+    # or Model Optimizer's quantized checkpoints like nvidia/Llama-3.1-8B-Instruct-FP8 on HF.
     llm = LLM(model="TinyLlama/TinyLlama-1.1B-Chat-v1.0")
 
     # Sample prompts.
diff --git a/examples/llm-api/quickstart_example.py b/examples/llm-api/quickstart_example.py
@@ -4,7 +4,7 @@
 def main():
 
     # Model could accept HF model name, a path to local HF model,
-    # or TensorRT Model Optimizer's quantized checkpoints like nvidia/Llama-3.1-8B-Instruct-FP8 on HF.
+    # or Model Optimizer's quantized checkpoints like nvidia/Llama-3.1-8B-Instruct-FP8 on HF.
     llm = LLM(model="TinyLlama/TinyLlama-1.1B-Chat-v1.0")
 
     # Sample prompts.
diff --git a/examples/medusa/README.md b/examples/medusa/README.md
@@ -19,7 +19,7 @@ For more info about Medusa visit [speculative decoding documentation](https://nv
 The TensorRT LLM Medusa example code is located in [`examples/medusa`](./). There is one [`convert_checkpoint.py`](./convert_checkpoint.py) file to convert and build the [TensorRT](https://developer.nvidia.com/tensorrt) engine(s) needed to run models with Medusa decoding support.
 In this example, we demonstrate the usage of two models:
 1. The Vucuna 7B model from Hugging Face [`FasterDecoding/medusa-vicuna-7b-v1.3`](https://huggingface.co/FasterDecoding/medusa-vicuna-7b-v1.3) with its Medusa heads [`medusa-vicuna-7b-v1.3`](https://huggingface.co/FasterDecoding/medusa-vicuna-7b-v1.3).
-2. The quantized checkpoint [`nvidia/Llama-3.1-8B-Medusa-FP8`](https://huggingface.co/nvidia/Llama-3.1-8B-Medusa-FP8) on Hugging Face by [TensorRT Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer) (ModelOpt). This model is based on [Llama-3.1 8B](https://huggingface.co/meta-llama/Llama-3.1-8B) and enhanced with Medusa heads, with both the base model (except lm_head) and Medusa heads already quantized in FP8.
+2. The quantized checkpoint [`nvidia/Llama-3.1-8B-Medusa-FP8`](https://huggingface.co/nvidia/Llama-3.1-8B-Medusa-FP8) on Hugging Face by [Model Optimizer](https://github.com/NVIDIA/Model-Optimizer) (ModelOpt). This model is based on [Llama-3.1 8B](https://huggingface.co/meta-llama/Llama-3.1-8B) and enhanced with Medusa heads, with both the base model (except lm_head) and Medusa heads already quantized in FP8.
 
 ### Build TensorRT engine(s)
 Get the weights by downloading base model [`vicuna-7b-v1.3`](https://huggingface.co/lmsys/vicuna-7b-v1.3) and Medusa Heads [`medusa-vicuna-7b-v1.3`](https://huggingface.co/FasterDecoding/medusa-vicuna-7b-v1.3) from HF.
diff --git a/examples/models/core/deepseek_v3/README.md b/examples/models/core/deepseek_v3/README.md
diff --git a/examples/models/core/exaone/README.md b/examples/models/core/exaone/README.md
diff --git a/examples/models/core/llama/README.md b/examples/models/core/llama/README.md
diff --git a/examples/models/core/llama4/README.md b/examples/models/core/llama4/README.md
diff --git a/examples/models/core/qwen/README.md b/examples/models/core/qwen/README.md
diff --git a/examples/quantization/README.md b/examples/quantization/README.md
diff --git a/security_scanning/examples/models/core/mllama/poetry.lock b/security_scanning/examples/models/core/mllama/poetry.lock
diff --git a/security_scanning/poetry.lock b/security_scanning/poetry.lock
