Update README.md

README.md

@@ -53,12 +53,11 @@ The model is a single shot fine tuned Instruct LLM in Hindi and dialects
 <!-- Provide a longer summary of what this model is/does. -->
 The model is a single shot fine tuned Instruct LLM in Hindi and dialects
 
-- **Developed by:** More information needed
-- **Shared by [Optional]:** More information needed
-- **Model type:** Language model
+- **Developed by:** Nikhil Malhotra, Nilesh Brahme, Satish Mishra, Vinay Sharma (Makers Lab, TechMahindra)
+- **Model type:** Foundational Language model
 - **Language(s) (NLP):** hin, bho, mai, doi
 - **License:** other
-- **Parent Model:** More information needed
+- **Parent Model:** It is the parent model on GPT architecture
 - **Resources for more information:** More information needed
 
 
@@ -95,7 +94,9 @@ The model is a single shot fine tuned Instruct LLM in Hindi and dialects
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
+Significant research has explored bias and fairness issues with language models
+(see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). 
+Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
 
 
 ## Recommendations
@@ -227,6 +228,15 @@ More information needed
 # How to Get Started with the Model
 
 Use the code below to get started with the model.
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+pipe = pipeline("text-generation", model="nickmalhotra/Indus_1.175B")
+
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("nickmalhotra/Indus_1.175B")
+model = AutoModelForCausalLM.from_pretrained("nickmalhotra/Indus_1.175B")
 
 <details>
 <summary> Click to expand </summary>