adding in disclaimer for RDU tutorial

README.md

@@ -86,6 +86,10 @@ model = AutoModelForCausalLM.from_pretrained("sambanovasystems/BLOOMChat-176B-v1
 
 ### Tutorial on using the model for text generation
 
+As this model was trained on SambaNova's Reconfigurable Dataflow Unit (RDU) which is not accessible by everyone, we provided a tutorial on how to use this model on GPU.
+
+For those interested in running models on RDUs, [please contact us](https://sambanova.ai/getstarted).
+
 [This tutorial](https://github.com/huggingface/transformers-bloom-inference) from Huggingface will be the base layer for running our model. The tutorial is intended for BLOOM; however, since our model is based off of BLOOM we can repurpose it.
 
 For setup instructions follow the Huggingface tutorial.
@@ -135,7 +139,7 @@ Running command for int8 (sub optimal performance, but fast inference time):
 ```
 python -m inference_server.cli --model_name sambanovasystems/BLOOMChat-176B-v1 --model_class AutoModelForCausalLM --dtype int8 --deployment_framework hf_accelerate --generate_kwargs '{"do_sample": false, "temperature": 0.8, "repetition_penalty": 1.2, "top_p": 0.9, "max_new_tokens": 512}'
 ```
-**DISCLAIMER:** When using int8, the results will be subpar compared to bf16 as the model is being [quantized](https://huggingface.co/blog/hf-bitsandbytes-integration#introduction-to-model-quantization). 
+**DISCLAIMER:** When using int8, the results will be subpar compared to bf16 as the model is being [quantized](https://huggingface.co/blog/hf-bitsandbytes-integration#introduction-to-model-quantization).
 
 ### Suggested Inference Parameters
 - Temperature: 0.8