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ashioyajotham/README.md

๐Ÿ”ญ Currently Working On

  • Building AI Agents for research, information synthesis and coding
  • Algorithmic Trading bots
  • ML Systems Design & Implementation
  • Data Science Projects

๐ŸŒฑ Research Interests

  • MLOps & System Design
  • AI Safety (red-teaming, mech interp and alignment)
  • Foundation Models (confabulations ["hallucinations"], reasoning, knowledge-encoding)
  • Video generation models (physical world modeling)

๐Ÿ› ๏ธ Skills & Technologies

python pytorch docker

Web Development & Deployment

Streamlit Flask FastAPI Docker

Featured Deployments

Streamlit App

๐Ÿ“ Latest Blog Posts

โ–ถ Read more on Substack


GitHub followers GitHub User's stars

Typing SVG

๐Ÿ“ˆ GitHub Analytics

โšก Recent GitHub Activity
Ashioya's Activity Graph

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  1. fingpt_trader fingpt_trader Public template

    A quant trading system platform based on FinGPT, demonstrating new applications of large pre-trained Language Models in quantitative finance.

    Python 16 6

  2. web_research_agent web_research_agent Public

    Research implementation exploring dynamic task analysis and multi-strategy synthesis in the ReAct paradigm. Demonstrates how systems can adapt their reasoning approach based on question structure wโ€ฆ

    Python 1 1

  3. cot-faithfulness-mech-interp cot-faithfulness-mech-interp Public

    Mechanistic analysis of Chain-of-Thought faithfulness using GPT-2 Small

    Jupyter Notebook 1

  4. Value-Aligned-Confabulation-VAC-Research Value-Aligned-Confabulation-VAC-Research Public

    Research framework for evaluating value-aligned confabulation in LLMs - distinguishing beneficial speculation from harmful hallucination.

    Python

  5. greater-than-circuit greater-than-circuit Public

    Reverse engineering the circuit responsible for the "greater than" capability in a language model

    Python

  6. weather_forecasting_lora weather_forecasting_lora Public

    How close can LoRA get to full fine-tuning (FullFT) in terms of learning speed, performance, and compute tradeoffs? And under what conditions?

    Python