50 lines
1.9 KiB
YAML
50 lines
1.9 KiB
YAML
# llama-swap model menu for the Hermes backend (single P100, 16GB).
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# llama-swap presents every model below via /v1/models and hot-swaps on demand —
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# only one fits in VRAM at a time, so selecting a different model triggers a
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# brief reload. Default is chosen by Hermes (model.default = gpt-oss-20b).
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#
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# All four serve 64k context (Hermes' minimum). Args are the validated Pascal
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# config: q8_0 KV (q4_0 V-cache is pathological on GP100), flash-attn on,
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# --parallel 1 so one sequence gets the full 64k. gpt-oss/gemma4 are natively
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# >=128k so no YaRN/override-kv needed.
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#
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# Excluded: qwen3-30b-a3b-2507 (Q3) — OOMs at 64k in 16GB, so it can't meet
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# Hermes' 64k minimum on this GPU.
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healthCheckTimeout: 300
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logLevel: info
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macros:
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# --jinja applies each model's embedded chat template (REQUIRED for gpt-oss'
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# harmony format, else content comes back empty; harmless/correct for gemma4).
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common: "-ngl 99 --parallel 1 --ctx-size 65536 --flash-attn on --cache-type-k q8_0 --cache-type-v q8_0 --jinja"
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models:
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"gpt-oss-20b":
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# MoE ~3.6B active. Fastest prefill (~365 tok/s) -> ~45s cold start on Hermes' 16k prompt.
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cmd: >
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/app/llama-server --port ${PORT} --host 0.0.0.0
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-m /models/gpt-oss-20b-mxfp4.gguf --alias gpt-oss-20b
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${common}
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"gemma-4-26b-a4b":
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# gemma4 MoE, 4B active / 26B total (UD-Q3_K_M). Quality-leaning; ~147 tok/s prefill.
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cmd: >
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/app/llama-server --port ${PORT} --host 0.0.0.0
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-m /models/gemma-4-26B-A4B-it-UD-Q3_K_M.gguf --alias gemma-4-26b-a4b
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${common}
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"gemma-4-12b":
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# Dense 12B (Q4_K_M). ~85 tok/s prefill.
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cmd: >
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/app/llama-server --port ${PORT} --host 0.0.0.0
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-m /models/gemma-4-12b-it-Q4_K_M.gguf --alias gemma-4-12b
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${common}
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"gemma-4-e4b":
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# Small dense ~4B (Q4_K_M). Lots of VRAM headroom; ~173 tok/s prefill.
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cmd: >
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/app/llama-server --port ${PORT} --host 0.0.0.0
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-m /models/gemma-4-E4B-it-Q4_K_M.gguf --alias gemma-4-e4b
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${common}
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