LLM Projects
Advancing Telugu language processing and creating efficient, accessible language models
Telugu Language Foundation
Building the core infrastructure for Telugu language AI
Telugu Dataset Development
Creating comprehensive Telugu language datasets for NLP tasks
Key Outcomes
- 500 million tokens
- Culture data collection
- Standardized preprocessing pipeline
- Multi-domain coverage
Future Plans
- Expand to 5 billion tokens
- Add domain-specific datasets
- Implement automated cleaning
- Create benchmark sets
Key Metrics
Foundational Telugu Model
Developing a base language model specifically for Telugu
Key Outcomes
- 1B parameter base model
- Efficient architecture
- Domain adaptation capability
- Evaluation framework
Future Plans
- Scale to 3B parameters
- Optimize for inference
- Create model variants
- Develop training tools
Key Metrics
Telugu Tokenization
Building efficient tokenization for Telugu text
Key Outcomes
- Custom tokenizer
- Optimized vocabulary
- Morphological handling
- Subword segmentation
Future Plans
- Reduce vocab size
- Improve rare words
- Add compositionality
- Create tools
Key Metrics
Advanced NLP Research
Pushing boundaries in language understanding
New Sentence Embeddings
Developing improved sentence embedding techniques
Key Outcomes
- Novel embedding architecture
- Cross-lingual mappings
- Evaluation framework
- Visualization tools
Future Plans
- Scale to more languages
- Improve efficiency
- Create embeddings API
- Build demo platform
Key Metrics
Shape of Stories
Analyzing narrative structures in Telugu literature
Key Outcomes
- Story pattern detection
- Cultural preservation
- Analysis toolkit
- Pattern visualization
Future Plans
- Expand story corpus
- Add more languages
- Create visualization
- Build story generator
Key Metrics
Model Optimization
Making models more efficient and accessible
Quantizing/Shortening LLMs
Optimizing model size and performance
Key Outcomes
- 4-bit quantization
- Model pruning
- Performance preservation
- Mobile deployment
Future Plans
- Explore 1-bit models
- Improve speed
- Reduce memory
- Create SDK
Key Metrics
Synthetic Data Generation
Creating synthetic training data for improved model performance
Key Outcomes
- Data generation pipeline
- Quality metrics
- Diversity measures
- Validation tools
Future Plans
- Scale generation
- Improve quality
- Add more domains
- Create GUI
Key Metrics
Telugu Tiny Stories
Creating concise, efficient story generation models
Key Outcomes
- Compact story model
- Generation pipeline
- Quality metrics
- Style controls
Future Plans
- Expand story types
- Add interactivity
- Improve coherence
- Create app
Key Metrics
Projects Summary
Telugu Dataset Development
Q3 2024 - Q4 2025
Creating comprehensive Telugu language datasets for NLP tasks
Foundational Telugu Model
Q1 2025 - Q3 2025
Developing a base language model specifically for Telugu
Telugu Tokenization
Q3 2024 - Q3 2025
Building efficient tokenization for Telugu text
New Sentence Embeddings
Q3 2024 - Q1 2025
Developing improved sentence embedding techniques
Shape of Stories
Q2 2024 - Q4 2024
Analyzing narrative structures in Telugu literature
Quantizing/Shortening LLMs
Q3 2024 - Q2 2025
Optimizing model size and performance
Synthetic Data Generation
Q4 2024 - Q4 2025
Creating synthetic training data for improved model performance
Telugu Tiny Stories
Q2 2024 - Q3 2024
Creating concise, efficient story generation models