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contracts: sync to ground truth — ornith-1.0-35b→strix-moe, 256K all GPUs, real LiteLLM timeouts

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Verified on ground 2026-07-16 against CT 116 litellm_config.yaml + GPU hosts:
- AMD host serves qwen3.6-35B-udq4 (LiteLLM alias strix-moe); ornith-1.0-35b does NOT exist
- All 3 GPUs at 256K ctx, parallel 2 (RTX 3090 was listed 128K/parallel 1)
- LiteLLM timeouts: qwen 300s, gemma 120s, strix 300s (were stale 90s/120s)
- Added LiteLLM model surface + key scoping to litellm-self-heal
- Patched health-check script path ref

Files: litellm-self-heal, litellm-health, gpu-fleet, gpu-self-heal,
zulip-adapter-lessons, abiba-zulip-restore, hermes-agent-baseline,
delegation-prose-contract, mumuni-delegation-prose-contract
This commit is contained in:
Abiba
2026-07-16 17:02:44 +00:00
parent d0feb7881e
commit dc572889f8
9 changed files with 49 additions and 38 deletions
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litellm-self-heal.prose.md
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@@ -6,6 +6,7 @@ note: >
DEPLOYED 2026-07-12 on CT 116 cron: 0 */6 * * *
Auto-remediation code was removed from the pi Zulip extension (retired 2026-07-04),
now reimplemented as `litellm-health-check.sh` on CT 116.
Script: `/opt/inference-harness/scripts/litellm-health-check.sh` on CT 116 (cron `0 */6 * * *`).
Reports to /var/log/litellm/health-*.json and RA-H OS knowledge graph.
GPU monitoring integrated from gpu-monitor on .24:9100.
@@ -56,18 +57,28 @@ Request → nginx:80 → LiteLLM:4000 → GPU(llama-server, parallel 2)
| Host | IP | Hardware | Models Served | Engine | Context | Parallel |
|------|-----|----------|---------------|--------|---------|----------|
| llm-gpu | 192.168.68.8 | NVIDIA RTX 3090 (24 GB) | qwen3.6-27B-code | llama-server systemd | **256K** | 1 |
| ocu-llm | 192.168.68.110 | NVIDIA RTX 5070 (12 GB) | gemma-4-12b | llama-server systemd | 131K | 2 |
| amdpve | 192.168.68.15 | AMD Strix Halo 64GB UMA | ornith-1.0-35b | llama-server systemd (Vulkan) | 256K | 2 |
| llm-gpu | 192.168.68.8 | NVIDIA RTX 3090 (24 GB) | qwen3.6-27B-code | llama-server systemd (`/home/llmuser/llama-wrapper.sh`, `-c 262144 --parallel 2 --ngl 99`) | **256K** | 2 |
| ocu-llm | 192.168.68.110 | NVIDIA RTX 5070 (12 GB) | gemma-4-12b | llama-server systemd (`/home/llmuser/llama-wrapper.sh`, `--ctx-size 262144 --parallel 2`, IQ4_NL + MTP draft) | **256K** | 2 |
| amdpve | 192.168.68.15 | AMD Strix Halo 64GB UMA | qwen3.6-35B-udq4 (LiteLLM alias: `strix-moe`) | llama-server systemd (Vulkan) | 256K | 2 |
> Verified on ground 2026-07-16 via `curl /v1/models` on each host + `llama-wrapper.sh`. The AMD host's underlying model is `qwen3.6-35B-udq4`; LiteLLM exposes it under two `model_name`s: `qwen3.6-35B-udq4` and `strix-moe` (rpm 40). The legacy name `ornith-1.0-35b` does NOT exist in LiteLLM and must not be referenced.
## LiteLLM Model Surface (ground truth — `/opt/inference-harness/litellm_config.yaml` on CT 116)
`model_name`s served: `qwen3.6-27B-code`, `gemma-4-12b`, `qwen3.6-35B-udq4`, `strix-moe`, `gpu-dense`, `gpu-light`, `syslog-auto`.
- `syslog-auto` is a weighted router model: qwen3.6-27B-code (0.55, rpm 500) + qwen3.6-35B-udq4 (0.30, rpm 60) + gemma-4-12b (0.15, rpm 200).
- `gpu-dense` / `gpu-light` are high-rpm aliases (rpm 500) onto qwen3.6-27B-code / gemma-4-12b respectively.
- Key scoping: agent keys are restricted to `['syslog-auto','qwen3.6-27B-code','gemma-4-12b','strix-moe','gpu-dense','gpu-light']`. Note `qwen3.6-35B-udq4` is NOT in the agent-key allowlist — agents must use `strix-moe` to reach the AMD model.
## Model Fallback Chains (LiteLLM)
| Primary | Timeout | Fallback | Timeout |
|---------|---------|----------|---------|
| qwen3.6-27B-code | 90s | gemma-4-12b | 120s |
| gemma-4-12b | 120s | qwen3.6-27B-code | 90s |
| ornith-1.0-35b | 120s | qwen → gemma | — |
| syslog-auto (balanced) | 90s | qwen → gemma | — |
| qwen3.6-27B-code | 300s | gemma-4-12b | 120s |
| gemma-4-12b | 120s | qwen3.6-27B-code | 300s |
| qwen3.6-35B-udq4 / strix-moe | 300s | qwen → gemma | — |
| syslog-auto (balanced) | 300s | qwen → gemma | — |
> Global: request_timeout=300s, nginx proxy_read_timeout=600s
@@ -128,7 +139,7 @@ Run this first on every cycle. Results feed into remediation rules below.
### 5. Check model inference via LiteLLM — test each model
- POST /v1/chat/completions model=gemma-4-12b → expect 200
- POST /v1/chat/completions model=qwen3.6-27B-code → expect 200
- POST /v1/chat/completions model=ornith-1.0-35b → expect 200
- POST /v1/chat/completions model=strix-moe → expect 200
- Use master key for auth
### 6. Check agent keys