Update README.md

README.md

@@ -109,24 +109,20 @@ model-index:
 
 # Qwen-14B-Hindi
 
-Qwen-14B-Hindi is a 14.7B parameter pre-trained and instruction-tuned bilingual large language model for both Hindi and English, 
-trained on a mixed language dataset containing < >. It features Rotary Position Embeddings (RoPE), SwiGLU activation, RMSNorm normalization, and Attention QKV bias, optimizing performance and efficiency. 
-
+Phi-4-Hindi is a 14.7B parameter pre-trained and instruction-tuned bilingual large language model for both Hindi and English, 
+trained on a mixed language dataset.
 
 ### Model Details:
 
 - **Developed by:** [Traversaal.ai](https://huggingface.co/large-traversaal)
-- **Language(s) (NLP):** Hindi and English
+- **Language(s) (NLP):** Optimized for Hindi and English
 - **License:** MIT
-- **Input:** Text-only data
-- **Output:** Model generates text
-- **Paper :** [Improving Multilingual Capabilities with Cultural and Local Knowledge in Large Language Models While Enhancing Native Performance]( )
-
+- **Paper :** TBA April 15
 
 ## Intended Use
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-We release Qwen-14B-Hindi under the MIT and Apache-2.0 licenses, encouraging researchers, developers, and enterprises to experiment with and build upon the model, particularly for bilingual, multilingual and non-English applications.
+We release Phi-4-Hindi under the MIT license, encouraging researchers, developers, and enterprises to experiment with and build upon the model, particularly for bilingual, multilingual and non-English applications.
 At the time of release, the model demonstrated state-of-the-art performance across an extensive English and Hindi evaluation suite.  
 
 Some potential downstream applications are as follows:  
@@ -148,7 +144,7 @@ Target audiences who may benefit from our model:
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-While Qwen-14B-Hindi is a powerful bilingual model designed for Hindi and English, it is crucial to acknowledge its limitations and the potential for misuse. The model must not be used in ways that violate any applicable laws or regulations. Below are specific scenarios where its use is restricted:  
+While Phi-4-Hindi is a powerful bilingual model designed for Hindi and English, it is crucial to acknowledge its limitations and the potential for misuse. The model must not be used in ways that violate any applicable laws or regulations. Below are specific scenarios where its use is restricted:  
 
 - *Harmful or Malicious Use*: The model should not be employed to create or distribute harmful, misleading, or inappropriate content, including but not limited to:  
   - Encouraging hate speech, violence, or discrimination  
@@ -166,33 +162,15 @@ While Qwen-14B-Hindi is a powerful bilingual model designed for Hindi and Englis
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
 <!-- The model is trained on publicly available data which was in part curated by Inception. -->
-~~We have employed different techniqes to reduce bias in the model. While efforts have been made to minimize biases, it is likely that the model, as with all LLM models, will exhibit some bias. 
+~~While efforts have been made to minimize biases, it is likely that the model, as with all LLM models, will exhibit some bias. 
 
 The model is trained as an AI assistant for Hindi and English speakers. The model is limited to produce responses for queries in these two languages
 and may not produce appropriate responses to other language queries.
 
-By using Llama-3-Nanda-10B-Chat, you acknowledge and accept that, as with any large language model, it may generate incorrect, misleading and/or offensive information or content. 
+By using this model, you acknowledge and accept that, as with any large language model, it may generate incorrect, misleading and/or offensive information or content. 
 The information is not intended as advice and should not be relied upon in any way, nor are we responsible for any of the content or consequences resulting from its use. 
 We are continuously working to develop models with greater capabilities, and as such, welcome any feedback on the model~~
 
-
-## Training Details:
-
-### Training Data:
-
-
-<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-~~For the pre-training of Llama-3-Nanda-10B-Chat, we used a diverse bilingual corpus sourced from the Web and other sources. We also used publicly available English and code datasets.
-To collect Hindi data, we used multiple sources including web pages, Wikipedia articles, news articles, Hindi books, etc.~~
-
-
-### Training Procedure:
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-~~We performed continuous pre-training followed by instruction tuning, both on Cerebras supercomputer.~~
-
 ## Evaluation:
 We evaluated our models on multiple well-known benchmarks to measure their effectiveness against other leading models, and the results are as follows: