Update README.md

README.md

@@ -18,7 +18,7 @@ model-index:
       value: 22.7
       name: normalized accuracy
     source:
-      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nickmalhotra/indus_1.175B
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nickmalhotra/ProjectIndus
       name: Open LLM Leaderboard
   - task:
       type: text-generation
@@ -108,7 +108,7 @@ model-index:
 # Model Card for Indus
 
 <!-- Provide a quick summary of what the model is/does. [Optional] -->
-The model is a single shot fine tuned Instruct LLM in Hindi and dialects
+The model is a pretrained model in Hindi and dialects which is instruct tuned .
 
 
 
@@ -157,26 +157,32 @@ The model is a single shot fine tuned Instruct LLM in Hindi and dialects
 ## Model Description
 
 <!-- Provide a longer summary of what this model is/does. -->
-The model is a single shot fine tuned Instruct LLM in Hindi and dialects
+TThe model is a pretrained model in Hindi and dialects which is instruct tuned.
 
 - **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:** It is the parent model on GPT architecture
-- **Resources for more information:** More information needed
+- **Parent Model:** It is a grounds up model built on GPT-2 architecture starting from tokenizer to decoder
+- **Resources for more information:** https://www.techmahindra.com/en-in/innovation/the-indus-project/
+  
 
 
 
 # Uses
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+Uses include question and answeting and conversation in Hindi and Dialects. The model would be reward tuned to be used across various industries
+1. Call center
+2. Healthcare
+3. Automotive
+4. Telecom
 
 ## Direct Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 <!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
-
+Direct use is as a foundationla model on Hindi and dialects
 
 
 
@@ -184,7 +190,12 @@ The model is a single shot fine tuned Instruct LLM in Hindi and dialects
 
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 <!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
- 
+ Uses include question and answeting and conversation in Hindi and Dialects. The model would be reward tuned to be used across various industries
+1. Call center
+2. Healthcare
+3. Automotive
+4. Telecom
+
 
 
 
@@ -192,7 +203,7 @@ The model is a single shot fine tuned Instruct LLM in Hindi and dialects
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 <!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
-
+Cannot be used for fill in the blanks, Multiple Q&A etc. at the moment
 
 
 
@@ -203,12 +214,14 @@ The model is a single shot fine tuned Instruct LLM in Hindi and dialects
 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.
+We have taken care across various biases by trying to remove them from training data. However since the model is a generative model, it would tend to produce hallucinations.
+Any disturbing or harmful sterotype produced by the model is purely un-intentional and coincidental. 
 
 
 ## Recommendations
 
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
+Recommendation is to not use biases and negative connotation for the model