Update README.md

README.md

@@ -21,7 +21,7 @@ We introduce Bamba-9B, a decoder-only language model based on the [Mamba-2](http
 The current release includes the following models:
 | **Stage**            | **Bamba 9B**                                                         | **Quantized**                                                           | **Note**                                                          |
 |----------------------|----------------------------------------------------------------------|-------------------------------------------------------------------------|-------------------------------------------------------------------|
-| **Base Model**       | [ibm-fms/Bamba-9B](https://huggingface.co/ibm-fms/Bamba-9B)          | [ibm-fms/Bamba-9B-fp8](https://huggingface.co/ibm-fms/Bamba-9B-fp8)     | Stage 2 pretraining                                               |
+| **Base Model**       | [ibm-fms/Bamba-9B-v1](https://huggingface.co/ibm-fms/Bamba-9B-v1)    | [ibm-fms/Bamba-9B-fp8](https://huggingface.co/ibm-fms/Bamba-9B-fp8)     | Stage 2 pretraining                                               |
 | **Base Model**       | [ibm-fms/Bamba-9B-2T](https://huggingface.co/ibm-fms/Bamba-9B-2T)    | [ibm-fms/Bamba-9B-fp8](https://huggingface.co/ibm-fms/Bamba-9B-fp8)     | Stage 1 pretraining                                               |
 | **Base Model**       | [ibm-fms/Bamba-9B-1.8T](https://huggingface.co/ibm-fms/Bamba-9B-1.8T)| [ibm-fms/Bamba-9B-fp8](https://huggingface.co/ibm-fms/Bamba-9B-fp8)     | Intermediate checkpoints during Stage 1, more to come             |
 | **SFT**              | coming soon                                                          | coming soon                                                             | to be released in the next drop                                   | 
@@ -64,8 +64,8 @@ You can utilize our newly contributed HF integration to run inference on our Bam
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model = AutoModelForCausalLM.from_pretrained("ibm-fms/Bamba-9B")
-tokenizer = AutoTokenizer.from_pretrained("ibm-fms/Bamba-9B")
+model = AutoModelForCausalLM.from_pretrained("ibm-fms/Bamba-9B-v1")
+tokenizer = AutoTokenizer.from_pretrained("ibm-fms/Bamba-9B-v1")
 
 message = ["Mamba is a snake with following properties  "]
 inputs = tokenizer(message, return_tensors='pt', return_token_type_ids=False)