abiba
221f9f79f3
audit-hermes-config.py Rule 8 required auxiliary.vision.model and auxiliary.web_extract.model to equal the retired 'gpu-light', so a config adopting the live canonical 'gpu-vision' FAILED our own audit - the audit was enforcing a dead alias (400 Invalid model name). Rule 8 now requires gpu-vision; retired names gpu-light/crew-auto join the raw-name rejection set; the guidance message names the live aliases. Sweep of the remaining references: gpu-self-heal stops canonicalizing gpu-light; hermes-config-template, hermes-agent-baseline, hermes-key-enforcement, inference-optimization, litellm-client-timeouts and gpu-fleet now use the live gpu-vision alias. Where a file restated model/rpm/weight/fallback state it now points at CT 116 /opt/inference-harness/litellm_config.yaml instead of duplicating it. koby's .129 config is report-only and recorded, not edited. Adds tests/test_audit_hermes_config_alias.py: executes the audit CLI and asserts gpu-vision passes while gpu-light and gemma-4-12b fail.
104 lines
3.6 KiB
Markdown
104 lines
3.6 KiB
Markdown
---
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name: inference-optimization
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kind: responsibility
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description: >
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Optimizes the full Syslog inference stack — LiteLLM routing weights, GPU model
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assignments, agent context management, and prompt caching — to reduce response
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times to sub-15s average. All GPUs now at 128K context (stable ceiling).
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id: 067NC6KP02RG60S50M40E30928
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---
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### Goal
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Syslog inference response times reduced to sub-15s average by optimizing the full
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stack: LiteLLM routing weights, GPU model assignments, Hermes agent context
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management, and prompt caching — without sacrificing agent capability.
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### Requires
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- `inference-metrics`: current SpendLogs from CT116 LiteLLM Postgres — avg
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request_duration_ms, prompt_tokens, completion_tokens, model_group breakdown,
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cache_hit rate over the last 3 hours
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- `agent-configs`: current config.yaml from each active Hermes agent (Mumuni
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.123, any others on .129/.122) including compression, model, context_window,
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prompt_caching, memory settings
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- `gpu-health`: health check response from all 3 GPU backends (strix-moe .15:8080,
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qwen .8:8080, gemma .110:8080)
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### Maintains
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The optimized inference stack configuration — every change is applied and
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verified end-to-end. Postcondition: avg request_duration_ms ≤ 15000 for 90% of
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inference calls.
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#### liteLLM-routing
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The syslog-auto routing weights, model-specific timeouts, RPM limits, and
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model_list entries on CT116 `/opt/inference-harness/litellm_config.yaml`.
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#### agent-compression
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Each Hermes agent's `~/.hermes/config.yaml` compression, context_window,
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prompt_caching, and model sections.
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#### prompt-caching
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LiteLLM cache configuration and llama.cpp `--cache-prompt` flag on GPU hosts.
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#### verification
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End-to-end latency measurements after changes applied — at least 3 test
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inference calls per model path measuring ttft (time-to-first-token) and total
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duration.
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### Continuity
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- input-driven
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### Strategies
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**Context is the root cause.** Every ~46K prompt token costs ~87s of
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prefill time at 532 tok/s. Fix context first, routing second.
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- **Route by task**: qwen for code/standard queries; gemma for
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compression/auxiliary; strix-moe for compression tasks.
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- **Compress aggressively**: threshold at 40% (not 65%) — a 128K window should
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compact at 51K, not 85K. Target 15% tail (not 30%).
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- **Cache everything repeated**: system prompts, skill docs, AGENTS.md — these
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never change between turns. Single-digit cache hit rate is unacceptable.
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- **Lower context ceiling**: 128K window is the stable ceiling for agent conversations.
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GPUs reduced from 256K to 128K (2026-07-17). 128K window should compact at 85K (0.65 threshold). For larger contexts, route to external providers.
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### Shape
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- `self`: analyze metrics, compute optimal configs, apply changes, verify
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- `delegates`:
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- `apply-liteLLM`: update litellm_config.yaml and reload
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- `apply-agent-config`: update hermes config.yaml per agent
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- `verify-latency`: run test inference calls and measure response
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### Execution
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```prose
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-- Phase 1: Analyze current state (already complete)
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-- Phase 2: Apply LiteLLM routing optimization
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call apply-liteLLM-routing
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config_path: /opt/inference-harness/litellm_config.yaml
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host: 192.168.68.116
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-- Phase 3: Apply agent context compression optimization
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call apply-agent-compression
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agent: mumuni
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host: 192.168.68.14
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config_path: /home/hermes/.hermes/config.yaml
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-- Phase 4: Enable llama.cpp prompt caching on GPU hosts
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call enable-prompt-caching
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hosts: [192.168.68.15, 192.168.68.8, 192.168.68.110]
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-- Phase 5: Verify end-to-end latency
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call verify-latency
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host: 192.168.68.116
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models: [syslog-auto, qwen3.6-27B-code, gpu-vision]
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```
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