Update README.md

README.md

@@ -18,24 +18,7 @@ Students & Academic Teams: Showcase research on model adaptation, data augmentat
 Industry & Startups: Demonstrate practical performance in real-world pipelines; optimise inference speed, resource usage.
 
 2. Allowed Base Models
-Participants must choose one of the following (or any other fully open-source LLM ≤ 8 B params):
-Model Name
-Parameters
-Notes
-Llama 3
-1B, 3B, 7B
-Meta's Llama series, particularly the smaller versions, is designed for efficiency and multilingual text generation. While the larger Llama models are more widely known, the 1B and 3B models offer a compact solution. Meta has also shown interest in addressing the linguistic diversity gap, which includes support for languages like Sinhala and Tamil.
-Gemma
-2B, 4B
-Developed by Google DeepMind, Gemma models are known for being lightweight yet powerful, with strong multilingual capabilities. Google has a strong focus on linguistic diversity, and Gemma's architecture makes it a good candidate for adapting to less-resourced languages.
-Qwen-2 
-0.5B, 1.5B, 7B
-This family of models from Alibaba is designed for efficiency and versatility. Their strong multilingual pretraining makes them good candidates for adaptation to Sinhala and Tamil through fine-tuning.
-Microsoft Phi-3-Mini 
-3.8B
-This model from Microsoft is highlighted for its strong reasoning and code generation capabilities within a compact size. While its primary focus isn't explicitly on a wide range of South Asian languages, its efficient design and good general language understanding could make it a suitable base for fine-tuning with Sinhala and Tamil data.
-Or … any other open-source checkpoint ≤ 8 B params
-
+Participants must choose one of the following (or any other fully open-source LLM ≤ 8 B params)
 Note: Proprietary or closed-license models (e.g., GPT-3 series, Claude) are not allowed.
 
 3. Data Resources and Evaluation