Update README.md

README.md

@@ -11,12 +11,27 @@ Multilingual language models are typically large, requiring significant computat
 
 Can we create multilingual models that maintain performance comparable to their larger models while reducing size, latency and inference speeds?
 
-Potential Techniques:
+Techniques:
 - Pruning
   - SparseGPT | [GitHub](https://github.com/VishnuVardhanSaiLanka/sparsegpt/tree/aya)
-  - ShortGPT
+  - ShortGPT | [Perplexity Sensivities](https://github.com/rsk2327/DistAya/tree/main)
 - Knowledge Distillation
   - DistillKit | [GitHub](https://github.com/ShayekhBinIslam/DistillKit)
+  - Distil-Whisper based method
+  - On policy distillation of language models
+  - Minitron: Compact Language models via Pruning & Knowledge Distillation
+  - DistiLLM: Towards Streamlined Distillation for Large Language Models
 - Quantization
+- Fine-Tuning | [GitHub](https://github.com/rsk2327/DistAya/tree/track/fine-tuning)
+
+Dataset:
+Initial 7 datasets unified, having 6.62M rows which includes the following:
+- Bangla_Alpaca_Orca : Bangle
+- Urdu_Instruct_News_Article_Generation: Urdu
+- Urdu_Instruct_News_Headline_Generation: Urdu
+- Urdu_Instruct_News_Category_Classification: Urdu
+- cidar: Arabic
+- Six_Millions_Instruction_Dataset_For_Arabic_Llm_Ft: Arabic
+- instructv3: English