Fine-tuning a Go expert: does it actually work? (Part 2)
The v2 adapter trained overnight on 41k samples. Loss 0.918, accuracy 82.7%. I loaded it into vLLM and ran the same prompts. Here’s what came out.
Gpu
Fine-tuning a Go expert: LoRA on a $300 GPU (Part 1)
I trained a LoRA adapter on 41k Go code examples from the Kubernetes and etcd source trees. The first run produced 600 tab characters. Here’s what I learned.
Disaggregated Prefill/Decode on Consumer GPUs
Running llm-d’s disaggregated prefill/decode architecture across an RTX 3060 and a Tesla T4 connected by 25GbE RDMA. What worked, what broke, and what I learned about KV cache transfer at the edge of what consumer hardware can do.