Update README.md

README.md

@@ -54,7 +54,7 @@ Here we provide a code snippet illustrating the process of loading both the proc
 from datasets import load_dataset
 from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
 
-repo_id = "MERaLiON/AudioLLM"
+repo_id = "MERaLiON/MERaLiON-AudioLLM-Whisper-SEA-LION"
 
 processor = AutoProcessor.from_pretrained(
     repo_id, 
@@ -95,7 +95,7 @@ MERaLiON-AudioLLM also supports batch inference.
 from datasets import load_dataset
 from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
 
-repo_id = "MERaLiON/AudioLLM"
+repo_id = "MERaLiON/MERaLiON-AudioLLM-Whisper-SEA-LION"
 
 processor = AutoProcessor.from_pretrained(
     repo_id, 
@@ -135,6 +135,7 @@ response = processor.batch_decode(generated_ids, skip_special_tokens=True)
 
 The current MERaLiON-AudioLLM has not been aligned for safety. Developers and users should perform their own safety fine-tuning and related security measures. In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights and codes.
 
+This research is supported by the National Research Foundation, Singapore and Infocomm Media Development Authority, Singapore under its National Large Language Models Funding Initiative. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore and Infocomm Media Development Authority, Singapore.
 
 ## Technical Specifications 
 
@@ -144,7 +145,7 @@ MERaLiON-AudioLLM is trained on a diverse collection of publicly available datas
 
 ### Compute and Infrastructure
 
-MERaLiON-AudioLLM is trained on the **ASPIRE 2A+** Supercomputer Cluster, provided by the **National Supercomputing Centre (NSCC)**. ASPIRE 2A+ cluster provides multiple H100 nodes, with each compute node equipped with 8 Nvidia H100 GPUs, 2 TB of RAM, and 30 TB of locally attached NVMe storage. These nodes are interconnected via a rail-optimised, full fat-tree topology, utilising 400 Gb/s NDR InfiniBand cables. Additionally, the cluster incorporates a 2.5 PB SSD-based Lustre file system, linked to the H100 nodes through high-speed InfiniBand connections. 
+MERaLiON-AudioLLM is trained on the **ASPIRE 2A+** Supercomputer Cluster, provided by **National Supercomputing Centre (NSCC)**, Singapore. ASPIRE 2A+ cluster provides multiple H100 nodes, with each compute node equipped with 8 Nvidia H100 GPUs, 2 TB of RAM, and 30 TB of locally attached NVMe storage. These nodes are interconnected via a rail-optimised, full fat-tree topology, utilising 400 Gb/s NDR InfiniBand cables. Additionally, the cluster incorporates a 2.5 PB SSD-based Lustre file system, linked to the H100 nodes through high-speed InfiniBand connections. 
 
 With a global batch size of 640, we train the current release of MERaLiON-AudioLLM for around 200k steps, which took 2 days to complete using 16 nodes, 128 H100 GPUs.