DeepReinforce Ornith-1.0 (dense 9B on Qwen 3.5, Q5_K_M, MIT), an
agentic-coding model. Tool-calls + <think> work under --jinja; native
256k so no YaRN. Loads at ~7.7GB VRAM @ 64k.
Benchmark (docs/2026-06-27-ornith-9b-benchmark.md): quality ties
gpt-oss-20b but gen is ~2.5-3x slower (dense 9B active vs gpt-oss MoE
3.6B active on the compute-bound P100). Default stays gpt-oss-20b;
ornith kept as a coding specialist in the menu.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Replace single llama-server with llama-swap so all benchmarked models are
selectable from Hermes' menu and hot-swapped on the one P100. Menu: gpt-oss-20b
(default, ~45s cold start), gemma-4-26b-a4b (MoE), gemma-4-12b, gemma-4-e4b.
qwen3-30b-a3b excluded (OOMs at 64k in 16GB). All 64k, q8/q8 KV, --parallel 1.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
