diff --git a/README.md b/README.md
index 0e83835..f8d433b 100644
--- a/README.md
+++ b/README.md
@@ -7,7 +7,7 @@ license_link: >-
 
 # Model Overview
 
-Nemotron-4-Minitron-8B-Base is a large language model (LLM) obtained by pruning Nemotron-4 15B; specifically, we prune model embedding size, number of attention heads, and MLP intermediate dimension. Following pruning, we perform continued training with distillation using 94 billion tokens to arrive at the final model; we use the continuous pre-training data corpus used in Nemotron-4 15B for this purpose.
+Minitron-8B-Base is a large language model (LLM) obtained by pruning Nemotron-4 15B; specifically, we prune model embedding size, number of attention heads, and MLP intermediate dimension. Following pruning, we perform continued training with distillation using 94 billion tokens to arrive at the final model; we use the continuous pre-training data corpus used in Nemotron-4 15B for this purpose.
 
 Deriving the Minitron 8B and 4B models from the base 15B model using our approach requires up to **40x fewer training tokens** per model compared to training from scratch; this results in **compute cost savings of 1.8x** for training the full model family (15B, 8B, and 4B). Minitron models exhibit up to a 16% improvement in MMLU scores compared to training from scratch, perform comparably to other community models such as Mistral 7B, Gemma 7B and Llama-3 8B, and outperform state-of-the-art compression techniques from the literature. Please refer to our [arXiv paper](https://arxiv.org/abs/2407.14679) for more details.
 
@@ -15,15 +15,15 @@ This model is for research and development only.
 
 **Model Developer:** NVIDIA 
 
-**Model Dates:** Nemotron-4-Minitron-8B-Base was trained between February 2024 and June 2024.
+**Model Dates:** Minitron-8B-Base was trained between February 2024 and June 2024.
 
 ## License
 
-Nemotron-4-Minitron-8B-Base is released under the [NVIDIA Open Model License Agreement](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf).
+Minitron-8B-Base is released under the [NVIDIA Open Model License Agreement](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf).
 
 ## Model Architecture
 
-Nemotron-4-Minitron-8B-Base uses a model embedding size of 4096, 48 attention heads, and an MLP intermediate dimension of 16384.
+Minitron-8B-Base uses a model embedding size of 4096, 48 attention heads, and an MLP intermediate dimension of 16384.
 It also uses Grouped-Query Attention (GQA) and Rotary Position Embeddings (RoPE). 
 
 **Architecture Type:** Transformer Decoder (auto-regressive language model) 
@@ -55,14 +55,14 @@ $ git clone -b aot/head_dim_rope --single-branch https://github.com/suiyoubi/tra
 $ pip install -e .
 ```
 
-The following code provides an example of how to load the Nemotron-4-Minitron-8B model and use it to perform text generation.
+The following code provides an example of how to load the Minitron-8B model and use it to perform text generation.
 
 ```python
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
 # Load the tokenizer and model
-model_path = "nvidia/Nemotron-4-Minitron-8B-Base"
+model_path = "nvidia/Minitron-8B-Base"
 tokenizer = AutoTokenizer.from_pretrained(model_path)
 
 device='cuda'
@@ -87,7 +87,7 @@ print(output_text)
 
 **Labeling Method:** Not Applicable
 
-**Properties:** The training corpus for Nemotron-4-Minitron-8B-Base consists of English and multilingual text, as well as code. Our sources cover a variety of document types such as: webpages, dialogue, articles, and other written materials. The corpus spans domains including legal, math, science, finance, and more. In our continued training set, we introduce a small portion of question-answering, and alignment style data to improve model performance. 
+**Properties:** The training corpus for Minitron-8B-Base consists of English and multilingual text, as well as code. Our sources cover a variety of document types such as: webpages, dialogue, articles, and other written materials. The corpus spans domains including legal, math, science, finance, and more. In our continued training set, we introduce a small portion of question-answering, and alignment style data to improve model performance. 
 
 **Data Freshness:** The pretraining data has a cutoff of June 2023. 
 
diff --git a/config.json b/config.json
index d99b7af..c455ad9 100644
--- a/config.json
+++ b/config.json
@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "nvidia/Nemotron-4-Minitron-8B-Base",
+  "_name_or_path": "nvidia/Minitron-8B-Base",
   "architectures": [
     "NemotronForCausalLM"
   ],