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8.9 KiB
8.9 KiB
Hugging Face TGI: Guardrails and Fix Patterns
Stabilization guide for Text Generation Inference (TGI), the Hugging Face high-throughput serving stack. Use these checks when local inference works in notebooks but collapses when deployed via TGI servers.
Open these first
- Visual recovery: RAG Architecture & Recovery
- Retrieval tuning knobs: Retrieval Playbook
- Snippet traceability: retrieval-traceability.md
- Ordering & race fixes: bootstrap-ordering.md, deployment-deadlock.md, predeploy-collapse.md
- Embedding issues: embedding-vs-semantic.md
Core acceptance
- ΔS(question, retrieved) ≤ 0.45
- Coverage ≥ 0.70 for target section
- λ remains convergent across 3 paraphrases and 2 seeds
- Responses remain stable across concurrent clients (no flip-flops)
Typical TGI breakpoints and fix
| Symptom | Likely cause | Fix |
|---|---|---|
| Logs show healthy, outputs differ across replicas | Worker desync / weight loading skew | bootstrap-ordering.md, predeploy-collapse.md |
| Citations disappear under concurrent load | Async merge loses trace offsets | retrieval-traceability.md, data-contracts.md |
| ΔS rises >0.60 when concurrency >10 | Context fragmentation across shards | context-drift.md, entropy-collapse.md |
| Errors vanish in dry run, appear in production | Race condition in warm-up path | deployment-deadlock.md |
| JSON outputs invalid at scale | Schema loosening during parallel decode | logic-collapse.md, data-contracts.md |
Fix in 60 seconds
- Batch probe: Run same query at concurrency=1 and concurrency=16. If ΔS only rises at higher concurrency, lock async merging.
- λ probe: Test 3 paraphrases, 2 seeds. If λ flips, apply BBAM variance clamp.
- Contracts: Require
snippet_id,offsets,tokensin every response. - Warm-up fencing: Run dummy batch before live serve to sync workers.
- Verify: Replay test dataset at concurrency=32. Expect stable ΔS ≤ 0.45.
Copy-paste test prompt
I am running Hugging Face TGI for local inference.
Concurrent clients sometimes cause unstable answers.
Question: "{user_question}"
Please return:
1. ΔS at concurrency=1 vs concurrency=16
2. λ across paraphrases
3. Whether citations are preserved
4. Minimal fix module if ΔS ≥ 0.60
🔗 Quick-Start Downloads (60 sec)
| Tool | Link | 3-Step Setup |
|---|---|---|
| WFGY 1.0 PDF | Engine Paper | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>” |
| TXT OS (plain-text OS) | TXTOS.txt | 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly |
🧭 Explore More
| Module | Description | Link |
|---|---|---|
| WFGY Core | WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack | View → |
| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | View → |
| Problem Map 2.0 | RAG-focused failure tree, modular fixes, and pipelines | View → |
| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | View → |
| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | View → |
| Benchmark vs GPT-5 | Stress test GPT-5 with full WFGY reasoning suite | View → |
| 🧙♂️ Starter Village 🏡 | New here? Lost in symbols? Click here and let the wizard guide you through | Start → |
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