dschlueter
3158d16f9b
Steuert einen llama.cpp-Server als Docker-Container: --start/--check/--stop/
--change/--chat, INI-Konfiguration (builtin defaults -> [default] ->
[model.<profile>] -> CLI), SSRF-gehärtete Prompt-Eingabe (Datei/HTTPS-URL),
File-Locking für --start/--change und ein OpenAI-kompatibler HTTP-Layer.
Enthält u. a.:
- Env-Var-Expansion in hf_home (hf_home = ${HF_HOME})
- konfigurierbares Chat-Antwortbudget (max_tokens/chat_temperature,
CLI: --max-tokens/--chat-temp); temperature defer an Server-Default
- DNS-Pinning gegen DNS-Rebinding bei URL-Quellen
- dry-run als nebenwirkungsfreie Vorschau (kein Lock/Removal/Modell-Check)
- 98 Tests (pytest)
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
81 lines
2.5 KiB
Text
81 lines
2.5 KiB
Text
# llama.cpp.config.example
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#
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# Copy this file to llama.cpp.config and adjust it for your host before
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# running llamacppctl. See docs/SECURITY_AND_OPERATIONS.md for details on
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# every field and on the security model for --system-url/--prompt-url.
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#
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# Section types:
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# [default] global defaults, merged over the built-in fallbacks
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# [model.<name>] a model profile, selected via --profile <name>
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# [prompt.<name>] a system-prompt profile, selected via --system-prompt-profile <name>
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#
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# container_name is MANDATORY and must be unique per profile you intend to
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# run concurrently: it is the single identity anchor for Docker naming,
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# locking (--change), and --stop/--check targeting.
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[default]
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image = ghcr.io/ggml-org/llama.cpp:server-cuda
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hf_home = ${HF_HOME}
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model_path = models/qwen3/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-Q4_K_M.gguf
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container_name = llama_cpp_server
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host_port = 8001
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container_port = 8000
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model_alias = default_llm
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gpu_device = 1
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restart_policy = unless-stopped
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# Groß-Profil für literarische Texte / durchdachte Reden: volles
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# 256k-Kontextfenster (= n_ctx_train des Modells) und sehr lange Antworten.
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# Empirisch gemessen passt der q4_0-KV-Cache dafür knapp auf EINE 24-GB-3090
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# (~22,6 GB inkl. 21-GB-Modell). Bei Bedarf auf 131072/65536 senken.
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ctx_size = 262144
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n_predict = 32768
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temp = 0.65
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top_p = 0.80
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top_k = 20
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min_p = 0.01
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repeat_penalty = 1.05
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main_gpu = 0
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ngl = 999
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fa = true
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kv_unified = true
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jinja = true
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reasoning = on
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no_context_shift = true
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cache_type_k = q4_0
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cache_type_v = q4_0
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batch_size = 1024
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ubatch_size = 512
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parallel = 1
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cont_batching = true
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health_endpoint = /health
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models_endpoint = /v1/models
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chat_endpoint = /v1/chat/completions
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timeout = 300
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poll_interval = 2
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# Chat-Antwortbudget (Reasoning-Modell braucht viel; für lange Texte hoch):
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max_tokens = 32768
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# chat_temperature leer lassen -> Server-Temp (temp oben) gilt.
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chat_temperature =
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# Example second model profile, pinned to the second GPU (e.g. RTX 3090 #2)
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# with its own port and container name so it can run alongside [default].
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[model.qwen35]
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model_path = qwen3/Qwen3-35B-A3B-Q4_K_M.gguf
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model_alias = qwen35
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container_name = llama_cpp_qwen35
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host_port = 8002
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gpu_device = 1
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[model.deepseek]
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model_path = deepseek/deepseek-r1-q4.gguf
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model_alias = deepseek
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container_name = llama_cpp_deepseek
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host_port = 8003
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gpu_device = 1
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ctx_size = 131072
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[prompt.concise]
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system_prompt = Du antwortest kurz, präzise und technisch.
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[prompt.coding]
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system_prompt = Du bist ein erfahrener Linux-, Python- und LLM-Infrastruktur-Engineer.
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