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docs(admin): add ops documentation and fix hermes dashboard service
- Remove admin/ from .gitignore (repo is now private) - Add --tui flag to hermes-dashboard.service template - Document hermes-tui wrapper, dashboard access, TUI usage Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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admin/llama_server.md
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admin/llama_server.md
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# llama-server — Inférence LLM (gpu-01)
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llama-server (llama.cpp compilé with ROCm 7.x) tourne en service systemd sur **gpu-01** (`192.168.10.20:1234`).
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API 100% compatible OpenAI — remplace LM Studio pour l'inférence.
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---
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## Service systemd
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```bash
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# Depuis gpu-01
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sudo systemctl status llama-server
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sudo systemctl restart llama-server
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sudo journalctl -u llama-server -f
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sudo journalctl -u llama-server -n 50
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```
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---
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## Modèle actuel
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| Paramètre | Valeur |
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|---|---|
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| Modèle | Qwen2.5-14B-Instruct Q4_K_M |
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| Alias API | `qwen2.5-14b-instruct` |
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| Chemin | `/mnt/models/bartowski/Qwen2.5-14B-Instruct-GGUF/Qwen2.5-14B-Instruct-Q4_K_M.gguf` |
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| Contexte | 16384 tokens (limité par VRAM — voir ci-dessous) |
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| GPU offload | 99 layers (tout sur GPU) |
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| Parallélisme | 1 slot (requis pour Hermes — voir bug parallel) |
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| Embeddings | activés (`--embeddings --pooling mean`) |
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### Contrainte VRAM (12 GB RX 6700XT)
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| Élément | VRAM |
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|---|---|
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| Modèle Q4_K_M 14B | ~8 GB |
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| KV cache à 16384 ctx | ~3 GB |
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| **Total** | **~11 GB** ✅ |
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À 32768 tokens de contexte, le KV cache monte à ~6 GB → total ~14 GB → segfault.
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**Ne pas augmenter `llama_ctx_size` au-delà de 16384 pour le 14B.**
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---
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## Embeddings
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llama-server expose `/v1/embeddings` avec `--embeddings --pooling mean`.
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Le flag `--pooling mean` est obligatoire pour les LLM causaux (sinon : erreur 400
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`Pooling type 'none' is not OAI compatible`).
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```bash
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# Test embeddings depuis storage-01
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curl -s -X POST http://192.168.10.20:1234/v1/embeddings \
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-H "Content-Type: application/json" \
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-d '{"model":"qwen2.5-14b-instruct","input":["test"]}' | jq '.data[0].embedding | length'
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# → 5120 (dimension Qwen2.5-14B)
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```
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---
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## Performances (RX 6700XT, ROCm 7.x)
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| Métrique | Qwen2.5-7B | Qwen2.5-14B |
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|---|---|---|
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| Prefill | ~400 tok/s | ~200 tok/s |
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| Génération | ~70 tok/s | ~35 tok/s |
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| TTFT Hermes (15k tokens) | ~35s | ~75s |
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| VRAM modèle | ~4 GB | ~8 GB |
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---
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## Validation API
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```bash
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# Depuis storage-01 ou n'importe quelle machine du LAN cluster
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curl -s http://192.168.10.20:1234/v1/models | jq '.data[].id'
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# Test inférence rapide
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curl http://192.168.10.20:1234/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "qwen2.5-14b-instruct",
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"messages": [{"role": "user", "content": "Dis bonjour"}],
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"max_tokens": 50
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}'
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```
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---
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## Changer de modèle
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1. Modifier `host_vars/gpu-01/vars.yml` :
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```yaml
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llama_model_path: "/mnt/models/bartowski/NouveauModele.gguf"
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llama_model_alias: "nouveau-alias"
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llama_ctx_size: 16384 # ajuster selon VRAM disponible
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```
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2. Redéployer :
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```bash
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ansible-playbook -i inventory.yml playbooks/gpu-01.yml --tags llama_server
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```
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3. Mettre à jour LiteLLM (alias `hermes-default`) :
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```bash
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ansible-playbook -i inventory.yml playbooks/storage-01.yml --tags litellm
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```
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---
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## Build (source)
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```bash
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# Sur gpu-01 — rebuild si besoin
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cd /opt/llama.cpp
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git pull
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cmake -B build \
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-DGGML_HIP=ON \
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-DAMDGPU_TARGETS=gfx1030 \
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-DCMAKE_BUILD_TYPE=Release \
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-DCMAKE_HIP_COMPILER=/opt/rocm/llvm/bin/clang++ \
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-DCMAKE_PREFIX_PATH=/opt/rocm
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cmake --build build --target llama-server -j$(nproc)
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sudo systemctl restart llama-server
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```
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---
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## Points d'attention
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| Sujet | Détail |
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|---|---|
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| GPU non officiel | RX 6700XT (gfx1031) — `HSA_OVERRIDE_GFX_VERSION=10.3.0` dans le service |
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| ROCm version | 7.2.3 — libs dans `/opt/rocm` |
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| VRAM limite | 14B + 16k ctx = ~11 GB — ne pas dépasser 16384 tokens |
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| parallel=1 obligatoire | `--parallel 4` divise le ctx en slots → contexte insuffisant pour Hermes |
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| pooling mean obligatoire | sans `--pooling mean` l'endpoint `/v1/embeddings` retourne erreur 400 |
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| Gemma 4 interdit | Crash ROCm sur prompts >800 tokens |
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| Modèles sur NFS | `/mnt/models` monté depuis storage-01 (NFS) |
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