🤝 Top Collaborators
.png&w=640&q=75)
🤓 Latest Submissions
Eidolon
Large language models are powerful generalists, but deploying them for specialized domains like legal, medical, or financial applications still requires expensive, time-consuming fine-tuning. Every new task means a new training run, new compute costs, and days of waiting. Eidolon solves this differently. Instead of fine-tuning the base model, Eidolon trains a hypernetwork to generate LoRA adapter weights on demand. Give it a task description and 50 domain examples, and it produces a specialized LoRA matrix set in under a second, injected directly into the frozen base model at inference time. No retraining. No queue. No cost per domain. The architecture centers on a 100M parameter hypernetwork that takes a task embedding from a frozen sentence encoder and outputs pairs of LoRA matrices (A and B) for each targeted attention layer of Llama 3.1 8B. The base model stays completely frozen throughout. Specialization happens entirely through the generated adapter weights. Built natively on AMD ROCm and optimized for the MI300X's HBM3 memory architecture, Eidolon exploits the hardware's massive bandwidth for meta-batch parallelism during hypernetwork training and holds multiple domain LoRAs simultaneously in memory during serving. The full stack runs on PyTorch 2.3+, HuggingFace PEFT, Flash Attention 2, Triton, and vLLM for production inference. The system was trained across 10 diverse domains simultaneously, using a task curriculum and gradient-aware techniques to prevent catastrophic interference between conflicting domain objectives. Generalization is evaluated on a held-out 11th domain the hypernetwork never saw during training. The result is a production-ready, few-shot domain adaptation system that makes LLM specialization as simple as an API call.
10 May 2026
