Initial commit: llamacppctl – llama.cpp Docker server control CLI

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>

llama.cpp.config

@@ -0,0 +1,81 @@
+# llama.cpp.config.example
+#
+# Copy this file to llama.cpp.config and adjust it for your host before
+# running llamacppctl. See docs/SECURITY_AND_OPERATIONS.md for details on
+# every field and on the security model for --system-url/--prompt-url.
+#
+# Section types:
+#   [default]       global defaults, merged over the built-in fallbacks
+#   [model.<name>]  a model profile, selected via --profile <name>
+#   [prompt.<name>] a system-prompt profile, selected via --system-prompt-profile <name>
+#
+# container_name is MANDATORY and must be unique per profile you intend to
+# run concurrently: it is the single identity anchor for Docker naming,
+# locking (--change), and --stop/--check targeting.
+
+[default]
+image = ghcr.io/ggml-org/llama.cpp:server-cuda
+hf_home = ${HF_HOME}
+model_path = models/qwen3/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-Q4_K_M.gguf
+container_name = llama_cpp_server
+host_port = 8001
+container_port = 8000
+model_alias = default_llm
+gpu_device = 1
+restart_policy = unless-stopped
+# Groß-Profil für literarische Texte / durchdachte Reden: volles
+# 256k-Kontextfenster (= n_ctx_train des Modells) und sehr lange Antworten.
+# Empirisch gemessen passt der q4_0-KV-Cache dafür knapp auf EINE 24-GB-3090
+# (~22,6 GB inkl. 21-GB-Modell). Bei Bedarf auf 131072/65536 senken.
+ctx_size = 262144
+n_predict = 32768
+temp = 0.65
+top_p = 0.80
+top_k = 20
+min_p = 0.01
+repeat_penalty = 1.05
+main_gpu = 0
+ngl = 999
+fa = true
+kv_unified = true
+jinja = true
+reasoning = on
+no_context_shift = true
+cache_type_k = q4_0
+cache_type_v = q4_0
+batch_size = 1024
+ubatch_size = 512
+parallel = 1
+cont_batching = true
+health_endpoint = /health
+models_endpoint = /v1/models
+chat_endpoint = /v1/chat/completions
+timeout = 300
+poll_interval = 2
+# Chat-Antwortbudget (Reasoning-Modell braucht viel; für lange Texte hoch):
+max_tokens = 32768
+# chat_temperature leer lassen -> Server-Temp (temp oben) gilt.
+chat_temperature =
+
+# Example second model profile, pinned to the second GPU (e.g. RTX 3090 #2)
+# with its own port and container name so it can run alongside [default].
+[model.qwen35]
+model_path = qwen3/Qwen3-35B-A3B-Q4_K_M.gguf
+model_alias = qwen35
+container_name = llama_cpp_qwen35
+host_port = 8002
+gpu_device = 1
+
+[model.deepseek]
+model_path = deepseek/deepseek-r1-q4.gguf
+model_alias = deepseek
+container_name = llama_cpp_deepseek
+host_port = 8003
+gpu_device = 1
+ctx_size = 131072
+
+[prompt.concise]
+system_prompt = Du antwortest kurz, präzise und technisch.
+
+[prompt.coding]
+system_prompt = Du bist ein erfahrener Linux-, Python- und LLM-Infrastruktur-Engineer.