Projects

  1. Distributed LLM Inference Service

    An efficient cloud-based inference service for LLMs using Kubernetes. Leveraging vLLM, an open-source library for optimizing LLMs, the service addresses challenges in memory consumption and latency.

  2. RLHF on Microsoft/Phi-2 for dialogues

    Fine-tuning Phi-2 using Direct Preference Optimization (DPO) on the Anthropic Helpful and Harmless dialogue dataset, leveraging LoRA adapters and 8-bit quantization for efficient training, showing improved reward margins.

  3. Conditional Diffusion Model for Next Frame Generation

    A diffusion model conditioned on past 11 frames for auto-regressive generation of next 11 frames. Achieved an MSE of 0.004 for next frame prediction and Jaccard score of 30.89 for semantic segmentation.

  4. transformerkp: A transformers based library for keyphrase identification from text documents.

    transformerkp allows you to train and apply state-of-the-art deep learning models for keyphrase extraction and generation from text documents. It supports several benchmark datasets and evaluation metrics for keyphrase extraction and generation.

  5. t-CRF: CRF head on top of Transformer for sequence tagging.

    t-CRF enables users to use Conditional Random Field layer on top of any Transformer based sequence tagger like POS tagger, entity recognition, etc.

  6. SpanElectra: A language model with accuracy of spanBERT and efficiency of ELECTRA.

    SpanElectra is an efficient language model that uses span boundary objective from SpanBert LM to capture span-level context but uses the discriminator-generator training method inspired by Electra LM for efficient low-resource training.

  7. Question-Generation using Language Model.

    Given a paragraph, generate all the possible questions related to this paragraph. If a context is provided, create those questions from that paragraph whose answer should be that context.