christians site

ai/ml/ds

i have done a bit of machine learning engineering, infrastructure, fullstack software, ai engineering, and a bunch of cloud stuff. primarily stuff to support ml/ai development and deployment.

i enjoy most things that involve applied ml, but some of my favorite focuses are developer tools and data processing pipelines. i love open source and contributing, contributing myself and supporting others.

blog

• kv cache, post training, and hardware aware algos
• ai from first principles (talk)
• llm evalulations
• audio question answering
• engineer >> scientist
• utilizing vector embeddings
• how to contribute to open source (medium post)
• run a 46B model on your home gpu (medium post)
• train an object detection model on roboflow (medium post)

projects

• kerneltune - training models to write performant triton kernels, dataset and model published
• pip install chatan - use chatan to quickly create synthetic datasets for finetuning with llm generators and class sampling
• realtime omni - omni/multimodal inference server with qwen 2.5 omni or phi 4 multimodal. text-to-text, text-to-speech, audio-to-text, audio-text-to-text, image-text-to-text, audio-text-to-speech
• audio-llama - multimodal adapter for llama to enable paired text+audio inputs
• realtime speech-speech inference - fast stt+chat+tts
• ai notebook - cursor-esque flow in a modern notebook interface
• llm evaluator - prompt+model evals, dual chat comparison, more
• dialstructure - audio transcription, diarization, summarization, classification
• groq-nvim plugin - ai code gen in vim
• lambda labs client - manage gpu clusters with a python client