Add training code link

README.md

@@ -34,7 +34,7 @@ In this repo, we are open-sourcing NVLM-1.0-D-72B (decoder-only architecture), t
 
 
 ## Reference(s)
-[Paper](https://arxiv.org/abs/2409.11402)   [Inference Code (HF)](https://huggingface.co/nvidia/NVLM-D-72B/tree/main)   [Training Code (Coming soon)]()   [Website](https://research.nvidia.com/labs/adlr/NVLM-1/) 
+[Paper](https://arxiv.org/abs/2409.11402)   [Inference Code (HF)](https://huggingface.co/nvidia/NVLM-D-72B/tree/main)   [Training Code](https://github.com/NVIDIA/Megatron-LM/tree/NVLM-1.0/examples/multimodal/nvlm)   [Website](https://research.nvidia.com/labs/adlr/NVLM-1/) 
 
 ## Benchmark Results
 We train our model with legacy [Megatron-LM](https://github.com/NVIDIA/Megatron-LM/tree/main/megatron/legacy) and adapt the codebase to Huggingface for model hosting, reproducibility, and inference.
@@ -103,7 +103,7 @@ Results (as of September 17th, 2024) in the multimodal benchmarks are as follows
 When converting Megatron checkpoint to Huggingface, we adapt [InternVL codebase](https://huggingface.co/OpenGVLab/InternVL2-Llama3-76B) to support model loading and multi-GPU inference in HF. 
 We also use the tokenizer from [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/tree/main) when adapting the tokenizer to Huggingface, as it contains extra special tokens for vision tasks, e.g., `<|vision_pad|>`. 
 We train NVLM-1.0-D-72B based on the [Qwen2-72B-Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct/tree/main) text-only model and [InternViT-6B-448px-V1-5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5) ViT model with our large-scale high-quality multimodal dataset. 
-For training code, please refer to [Megatron-LM (Coming soon)]().
+For training code, please refer to [Megatron-Core](https://github.com/NVIDIA/Megatron-LM/tree/NVLM-1.0/examples/multimodal/nvlm).
 
 
 ### Prepare the environment