Navdeep⚡ (@dev_navdeep) header
Navdeep⚡ (@dev_navdeep) profile picture

•developer •finetuning LLMs Building All in one AI for you https://xitter.cf/t.co/P94XO6mP9u

Joined April 2018
Re-designed the gear drive to be 16:1 gear reduction, needed the extra kick for small sized robotic leg actuator. Improvements:- 1. Torque peak 5Nm(before 2Nm) 2. Torque nominal 3Nm 3. Weight ~305gm 4. Smooth small scale motion.
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Just finished building complete MIT style QDD actuator module with 4:1 gear drive train. Improvement from last design: - can communicate over CAN bus. - easily programmable with arduino - expected good torque rating (yet to benchmark fully). - handling high current upto 40 amps
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Just finished prototyping cheapest possible foc based position control motor/actuator. - 1: 4 planetry gear based output. Able to do velocity, torque and position control. Can not find a good affordable 3 phase motor driver in india... 🥲
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If anyone is interested in how CPU works, I have created this project that work as 6502 cpu emulator, you can load assembly programs directly onto cpu, also included some references. looking forward to create BIOS for it. github.com/navdhakar/6502cpu
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If you want to use @karpathy 's llm.c gpt2 on low end gpu (in my case MX 330, 2GB memory), you can use unified memory in cuda, just replace cudaMalloc->cudaMallocManaged
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even small LLMs are insanely data hungry, I fine-tuned mistral7B with 20k data points it still overfits in less than 100 iterations
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One important channel I forgot, he gets right into meaty stuff of building real life linux drivers, its easy and its useful, you will never complain why your nvidia card is not working on linux. youtube.com/watch?v=XoYkHUnm…
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every developer should deep dive into such hard core things, it clears your fundamentals. - build OS from scratch (legacy but good foundation) cs.bham.ac.uk/~exr/lectures/… - learning about building bare minimum os from linux kernel youtube.com/watch?v=1gEFYoGU…
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2. model using pytorch - some cores are being used and some not - +1GB memory uses as compared to native these memory save and efficiency can be useful if we want to run these model in offline on consumer hardware.
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