Enhance LLM configuration and installation scripts for improved flexibility

- Added new environment variables in `env.example` and `gpu-rent.vars.example` for fine-tuning LLM settings, including CUDA build options and model version pinning.
- Updated `llm.md` documentation to include detailed descriptions of new configuration options and usage cases for LLM setups.
- Enhanced the `provision.py` script to forward new environment variables during remote installations, improving the installation process for LLM components.
- Modified the `install_llamacpp.sh` script to support conditional CUDA builds and asset URL overrides, ensuring better compatibility with various environments.
- Improved logging in the installation scripts to provide clearer feedback during the setup process.

docs/llm.md

@@ -120,7 +120,22 @@ Unit `gpu-rent-ollama` читает `/mnt/swarm_data/.gpu-rent-gpu.json`:
 
 На `up`: скачать GGUF → `/mnt/swarm_data/llamacpp/models` → `llama-server` + systemd. Уже скачанные крупные файлы не трогает.
 
-**Бинарник:** у upstream нет Linux CUDA в GitHub Releases (только Windows). `install_llamacpp.sh` собирает `llama-server` из исходников (`GGML_CUDA=ON`, arch из GPU probe). Если `nvcc` недоступен — fallback на Ubuntu Vulkan/CPU asset (без Windows). Pin: `LLAMACPP_TAG=b10545`. Override: `LLAMACPP_ASSET_URL` + `LLAMACPP_SHA256`.
+**Бинарник (без compile по умолчанию):** официальные Releases **не** дают Linux CUDA — только Windows CUDA + Ubuntu CPU/Vulkan. `install_llamacpp.sh` берёт **Ubuntu Vulkan** prebuilt (~30 MB, GPU через Vulkan). CUDA-сборка из исходников — только крайний случай (prebuilt упал) или явно `LLAMACPP_BUILD_CUDA=1` (5–15 мин, тихий лог + heartbeat).
+
+Переменные (`.env` или `gpu-rent.vars`, пробрасываются на VM при `up`):
+
+| Var | Зачем |
+| --- | --- |
+| `LLAMACPP_TAG` | pin release (`b10545`) |
+| `LLAMACPP_ASSET_URL` + `LLAMACPP_SHA256` | свой архив |
+| `LLAMACPP_BUILD_CUDA=1` | сразу CUDA из исходников |
+| `LLAMACPP_FORCE_REINSTALL=1` | снести бинарь и поставить заново |
+| `LLAMACPP_NGL` / `LLAMACPP_CTX` | override GPU layers / context |
+| `LLAMACPP_HOST` / `LLAMACPP_PORT` | bind (default `127.0.0.1:8080`) |
+| `LLAMACPP_EXTRA_ARGS` | доп. флаги `llama-server` |
+| `OLLAMA_VERSION` / `OLLAMA_SHA256` | pin Ollama |
+
+Готовые кейсы A–H — в `gpu-rent.vars.example`.
 
 ### Пресеты (меню)
 
@@ -158,4 +173,7 @@ OLLAMA_VERSION=0.6.5
 OLLAMA_SHA256=<sha256 of ollama-linux-amd64.tgz>
 LLAMACPP_TAG=b10545
 # или LLAMACPP_ASSET_URL=... + LLAMACPP_SHA256=...
+# CUDA из исходников (медленно): LLAMACPP_BUILD_CUDA=1
+# Переустановка бинаря: LLAMACPP_FORCE_REINSTALL=1
+# VRAM: LLAMACPP_NGL=40  LLAMACPP_CTX=4096  LLAMACPP_EXTRA_ARGS=--flash-attn on
 ```

env.example

@@ -46,6 +46,17 @@ OLLAMA_LOCAL_PORT=17811
 LLAMACPP_LOCAL_PORT=17812
 # OLLAMA_MODELS_MANIFEST=
 # LLAMACPP_MODELS_MANIFEST=
+# Pin / тонкая настройка LLM (несecреты; удобнее в gpu-rent.vars — см. кейсы A–H):
+# LLAMACPP_TAG=b10545
+# LLAMACPP_BUILD_CUDA=1
+# LLAMACPP_FORCE_REINSTALL=1
+# LLAMACPP_ASSET_URL=
+# LLAMACPP_SHA256=
+# LLAMACPP_NGL=40
+# LLAMACPP_CTX=4096
+# LLAMACPP_EXTRA_ARGS=--flash-attn on
+# OLLAMA_VERSION=0.6.5
+# OLLAMA_SHA256=
 # CIVITAI_API_TOKEN=
 # CIVITAI_API_HOST=civitai.red
 # Hugging Face (GGUF / gated HF URLs / capture fallback metadata):

gpu-rent.vars.example

@@ -1,7 +1,7 @@
 # gpu-rent.vars — локальные параметры запуска (не секреты).
 # Скопируй в gpu-rent.vars и правь. Файл в .gitignore.
 # Читают: gpu-rent.ps1 / .bat / .sh и python -m gpu_rent (после .env).
-# Секреты (OS_PASSWORD, CIVITAI_*) — только в .env.
+# Секреты (OS_PASSWORD, CIVITAI_*, HF_TOKEN) — только в .env.
 
 # Если запуск без аргументов (двойной клик / .\gpu-rent.ps1):
 # GPU_RENT_DEFAULT_ARGS=up --yes
@@ -9,24 +9,76 @@
 # Доп. флаги ко ВСЕМ вызовам (после твоих аргументов):
 # GPU_RENT_EXTRA_ARGS=
 
-# Те же имена, что в .env / env.example (несекретные дефолты):
+# ---------------------------------------------------------------------------
+# Базовые (несекретные дефолты; те же имена, что в env.example)
+# ---------------------------------------------------------------------------
 # UPDATE_GIT=true
 # DEFAULT_SPOT=true
 # SCAN_POOLS=ru-6,ru-7
 # FLAVOR_PREFERENCE=4090-24,4090-48,a5000,a100-40
 # SWARMUI_LOCAL_PORT=17801
 # DATA_VOLUME_SIZE_GB=100
-# DEFAULT_FLAVOR_ID=
-# DEFAULT_SPOT=true
 # ENABLE_SWARMUI=true
-# WORKLOAD=llm          # то же что ENABLE_SWARMUI=false (только LLM)
-# LLM_RUNTIME=none
-# OLLAMA_LOCAL_PORT=17811
-# LLAMACPP_LOCAL_PORT=17812
 # IDLE_MINUTES=60
 # IDLE_GRACE_MINUTES=90
-# LOCAL_WATCHDOG_GRACE_MINUTES=10
 # NOTIFY_READY=true
-# PULL_OUTPUT=false
 # Prefer auto /32 (leave unset). Spike/WARP only:
 # GPU_RENT_SSH_CIDR=0.0.0.0/0
+
+# ---------------------------------------------------------------------------
+# LLM — кейсы тонкой настройки (раскомментируй один блок)
+# Подробнее: docs/llm.md
+# ---------------------------------------------------------------------------
+
+# --- A) Быстрый default: SwarmUI + llama.cpp, Ubuntu Vulkan prebuilt ---
+# LLM_RUNTIME=llamacpp
+# ENABLE_SWARMUI=true
+# LLAMACPP_TAG=b10545
+# (GGUF — llamacpp-models.yaml; бинарь без compile)
+
+# --- B) Только LLM, без SwarmUI (дешевле по времени bootstrap / VRAM) ---
+# WORKLOAD=llm
+# LLM_RUNTIME=llamacpp
+# LLAMACPP_TAG=b10545
+
+# --- C) Максимальная скорость инференса: CUDA-сборка (5–15 мин первый раз) ---
+# LLM_RUNTIME=llamacpp
+# LLAMACPP_TAG=b10545
+# LLAMACPP_BUILD_CUDA=1
+# LLAMACPP_FORCE_REINSTALL=1
+# (FORCE — снести старый Vulkan/битый бинарь и переустановить)
+
+# --- D) Свой бинарь / pin URL (supply-chain) ---
+# LLM_RUNTIME=llamacpp
+# LLAMACPP_ASSET_URL=https://github.com/ggml-org/llama.cpp/releases/download/b10545/llama-b10545-bin-ubuntu-vulkan-x64.tar.gz
+# LLAMACPP_SHA256=<sha256 архива>
+# LLAMACPP_FORCE_REINSTALL=1
+
+# --- E) Делим 4090 со SwarmUI: меньше слоёв / короче контекст ---
+# LLM_RUNTIME=llamacpp
+# LLAMACPP_NGL=40
+# LLAMACPP_CTX=4096
+# LLAMACPP_EXTRA_ARGS=--flash-attn on
+
+# --- F) Длинный контекст / почти весь VRAM под LLM (llm-only) ---
+# WORKLOAD=llm
+# LLM_RUNTIME=llamacpp
+# LLAMACPP_NGL=99
+# LLAMACPP_CTX=32768
+# LLAMACPP_EXTRA_ARGS=--parallel 1
+
+# --- G) Ollama вместо llama.cpp + pin версии ---
+# LLM_RUNTIME=ollama
+# OLLAMA_VERSION=0.6.5
+# OLLAMA_SHA256=<sha256 of ollama-linux-amd64.tgz>
+# OLLAMA_LOCAL_PORT=17811
+
+# --- H) Выключить LLM (гасит unit’ы на следующем up) ---
+# LLM_RUNTIME=none
+
+# Порты туннеля (localhost):
+# OLLAMA_LOCAL_PORT=17811
+# LLAMACPP_LOCAL_PORT=17812
+# Свой путь к манифесту GGUF/pull:
+# LLAMACPP_MODELS_MANIFEST=
+# OLLAMA_MODELS_MANIFEST=

src/gpu_rent/provision.py

@@ -29,6 +29,32 @@ from gpu_rent.sync_files import pull_tree, push_tree
 Log = Callable[[str], None]
 DATA = "/mnt/swarm_data"
 
+# Forwarded to remote install_*.sh (from .env / gpu-rent.vars → os.environ).
+_OLLAMA_INSTALL_ENV = ("OLLAMA_VERSION", "OLLAMA_SHA256")
+_LLAMACPP_INSTALL_ENV = (
+    "LLAMACPP_TAG",
+    "LLAMACPP_ASSET_URL",
+    "LLAMACPP_SHA256",
+    "LLAMACPP_BUILD_CUDA",
+    "LLAMACPP_FORCE_REINSTALL",
+    "LLAMACPP_NGL",
+    "LLAMACPP_CTX",
+    "LLAMACPP_HOST",
+    "LLAMACPP_PORT",
+    "LLAMACPP_EXTRA_ARGS",
+)
+
+
+def _remote_llm_env(cfg: Config, *keys: str) -> dict[str, str]:
+    import os
+
+    env: dict[str, str] = {"SWARM_USER": cfg.ssh_user}
+    for key in keys:
+        value = (os.environ.get(key) or "").strip()
+        if value:
+            env[key] = value
+    return env
+
 
 def _pkg_text(name: str) -> str:
     return files("gpu_rent.remote").joinpath(name).read_text(encoding="utf-8")
@@ -442,7 +468,7 @@ def provision_llm(cfg: Config, host: str, log: Log) -> None:
             _pkg_text("install_ollama.sh"),
             remote_path="/tmp/gpu-rent-install_ollama.sh",
             timeout=900,
-            env={"SWARM_USER": cfg.ssh_user},
+            env=_remote_llm_env(cfg, *_OLLAMA_INSTALL_ENV),
             log=log,
         )
         entries = parse_ollama_models(cfg.ollama_models_manifest)
@@ -516,13 +542,16 @@ def provision_llm(cfg: Config, host: str, log: Log) -> None:
         else:
             log("llamacpp-models.yaml пуст — GGUF skip (положи вручную)")
         log("LLM: ставим/запускаем llama.cpp server")
+        import os
+
+        build_cuda = (os.environ.get("LLAMACPP_BUILD_CUDA") or "").strip() == "1"
         run_script_sudo(
             cfg,
             host,
             _pkg_text("install_llamacpp.sh"),
             remote_path="/tmp/gpu-rent-install_llamacpp.sh",
-            timeout=1200,
+            timeout=3600 if build_cuda else 1200,
-            env={"SWARM_USER": cfg.ssh_user},
+            env=_remote_llm_env(cfg, *_LLAMACPP_INSTALL_ENV),
             log=log,
         )
     st = load_state()

src/gpu_rent/remote/install_llamacpp.sh

@@ -1,6 +1,9 @@
 #!/usr/bin/env bash
-# Install llama-server (CUDA) for OpenAI-compatible API on loopback :8080.
+# Install llama-server for OpenAI-compatible API on loopback :8080.
-# Official GitHub releases ship Windows CUDA only — on Linux we build from source.
+#
+# Default: official Linux release asset (Ubuntu Vulkan — GPU without compile).
+# CUDA source build only as last resort (or LLAMACPP_BUILD_CUDA=1).
+# Pin: LLAMACPP_TAG=b10545  LLAMACPP_ASSET_URL=...  LLAMACPP_SHA256=...
 set -euo pipefail
 
 SWARM_USER="${SWARM_USER:-ubuntu}"
@@ -28,8 +31,21 @@ SERVER_BIN="${BIN_DIR}/llama-server"
 LLAMACPP_TAG="${LLAMACPP_TAG:-}"
 LLAMACPP_ASSET_URL="${LLAMACPP_ASSET_URL:-}"
 LLAMACPP_SHA256="${LLAMACPP_SHA256:-}"
+# 1 = force CUDA compile; 0/empty = prebuilt first, build only if prebuilt fails
+LLAMACPP_BUILD_CUDA="${LLAMACPP_BUILD_CUDA:-}"
+LLAMACPP_FORCE_REINSTALL="${LLAMACPP_FORCE_REINSTALL:-}"
+LLAMACPP_NGL="${LLAMACPP_NGL:-}"
+LLAMACPP_CTX="${LLAMACPP_CTX:-}"
+LLAMACPP_HOST="${LLAMACPP_HOST:-127.0.0.1}"
+LLAMACPP_PORT="${LLAMACPP_PORT:-8080}"
+LLAMACPP_EXTRA_ARGS="${LLAMACPP_EXTRA_ARGS:-}"
 
-# Pick a Linux release asset (never Windows/macOS). Prefer ubuntu CUDA → vulkan → cpu.
+if [[ "${LLAMACPP_FORCE_REINSTALL}" == "1" ]]; then
+  log "LLAMACPP_FORCE_REINSTALL=1 — удаляю старый бинарь"
+  rm -f "$SERVER_BIN" "$STAMP"
+fi
+
+# Prefer ubuntu CUDA (rare) → vulkan → cpu. Never Windows/macOS/cudart-only.
 pick_linux_asset_url() {
   python3 -c '
 import json,sys
@@ -107,7 +123,7 @@ ensure_build_deps() {
     export PATH="/usr/local/cuda/bin:${PATH}"
     return 0
   fi
-  log "ставлю nvidia-cuda-toolkit (нужен nvcc для сборки)…"
+  log "ставлю nvidia-cuda-toolkit (нужен nvcc)…"
   apt-get install -y -qq nvidia-cuda-toolkit >/dev/null
   if command -v nvcc >/dev/null 2>&1; then
     return 0
@@ -119,14 +135,24 @@ ensure_build_deps() {
   return 1
 }
 
+ensure_vulkan_runtime() {
+  if ldconfig -p 2>/dev/null | grep -q 'libvulkan\.so'; then
+    return 0
+  fi
+  export DEBIAN_FRONTEND=noninteractive
+  log "ставлю libvulkan1 (для Ubuntu Vulkan prebuilt)…"
+  apt-get install -y -qq libvulkan1 mesa-vulkan-drivers >/dev/null 2>&1 || \
+    apt-get install -y -qq libvulkan1 >/dev/null 2>&1 || true
+}
+
 install_from_archive_url() {
   local url="$1"
   local tmp
   tmp="$(mktemp -d)"
   (
     cd "$tmp"
-    log "asset $url"
+    log "скачиваю prebuilt: $url"
-    curl -fL "$url" -o pkg.bin
+    curl -fL --progress-bar "$url" -o pkg.bin
     if [[ -n "$LLAMACPP_SHA256" ]]; then
       echo "${LLAMACPP_SHA256}  pkg.bin" | sha256sum -c -
     else
@@ -149,58 +175,19 @@ install_from_archive_url() {
       exit 1
     fi
     install -m 755 "$found" "$SERVER_BIN"
-    chown "${SWARM_USER}:${SWARM_USER}" "$SERVER_BIN"
+    # Shared libs next to binary (release tarballs ship .so alongside).
+    find out -type f \( -name '*.so' -o -name '*.so.*' \) -print0 2>/dev/null \
+      | while IFS= read -r -d '' so; do
+          install -m 755 "$so" "${BIN_DIR}/$(basename "$so")"
+        done
+    chown -R "${SWARM_USER}:${SWARM_USER}" "$BIN_DIR"
   )
   local rc=$?
   rm -rf "$tmp"
   return "$rc"
 }
 
-build_cuda_from_source() {
+install_linux_release() {
-  local tag="$1"
-  local arch
-  arch="$(cuda_architectures)"
-  log "собираю llama-server CUDA из исходников (tag=${tag}, arch=${arch})…"
-  if ! ensure_build_deps; then
-    log "нет nvcc — CUDA-сборку пропускаем"
-    return 1
-  fi
-  log "nvcc $(nvcc --version 2>/dev/null | tail -n1 || echo '?')"
-
-  mkdir -p "$SRC_DIR"
-  if [[ -d "${SRC_DIR}/.git" ]]; then
-    git -C "$SRC_DIR" fetch --depth 1 origin tag "$tag" 2>/dev/null || true
-    if ! git -C "$SRC_DIR" checkout -f "$tag" 2>/dev/null; then
-      rm -rf "$SRC_DIR"
-      git clone --depth 1 --branch "$tag" "$REPO" "$SRC_DIR"
-    fi
-  else
-    rm -rf "$SRC_DIR"
-    git clone --depth 1 --branch "$tag" "$REPO" "$SRC_DIR"
-  fi
-
-  cmake -S "$SRC_DIR" -B "${SRC_DIR}/build" \
-    -DCMAKE_BUILD_TYPE=Release \
-    -DGGML_CUDA=ON \
-    -DCMAKE_CUDA_ARCHITECTURES="${arch}" \
-    -DLLAMA_CURL=ON \
-    -DLLAMA_BUILD_SERVER=ON
-  cmake --build "${SRC_DIR}/build" -j"$(nproc)" --target llama-server
-
-  local built="${SRC_DIR}/build/bin/llama-server"
-  if [[ ! -x "$built" ]]; then
-    log "сборка не дала ${built}"
-    return 1
-  fi
-  install -m 755 "$built" "$SERVER_BIN"
-  chown "${SWARM_USER}:${SWARM_USER}" "$SERVER_BIN"
-  echo "cuda:${tag}:${arch}" >"$STAMP"
-  chown "${SWARM_USER}:${SWARM_USER}" "$STAMP"
-  log "CUDA binary → ${SERVER_BIN}"
-  return 0
-}
-
-install_linux_release_fallback() {
   local tag="$1"
   local api url
   api="${API_BASE}/releases/tags/${tag}"
@@ -210,36 +197,134 @@ install_linux_release_fallback() {
     return 1
   fi
   if [[ "$url" == *vulkan* ]]; then
-    log "WARN: официального Linux CUDA нет — беру Ubuntu Vulkan prebuilt"
+    log "беру Ubuntu Vulkan prebuilt (GPU без compile; CUDA-сборка — LLAMACPP_BUILD_CUDA=1)"
-  elif [[ "$url" != *cuda* ]]; then
+    ensure_vulkan_runtime
-    log "WARN: беру Linux prebuilt без CUDA (CPU) — лучше собрать с nvcc"
+  elif [[ "$url" == *cuda* ]]; then
+    log "беру Linux CUDA prebuilt"
+  else
+    log "WARN: Linux prebuilt без GPU backend (CPU) — ${url##*/}"
   fi
   install_from_archive_url "$url"
   echo "asset:${tag}" >"$STAMP"
   chown "${SWARM_USER}:${SWARM_USER}" "$STAMP"
 }
 
+# Quiet CUDA build: no cmake spam; heartbeat every 30s with last %.
+build_cuda_from_source() {
+  local tag="$1"
+  local arch build_log pid pct line
+  arch="$(cuda_architectures)"
+  build_log="${LLAMA_ROOT}/build-cuda.log"
+  log "крайний случай: сборка CUDA из исходников (tag=${tag}, arch=${arch}, 5–15 мин)…"
+  log "полный лог: ${build_log}"
+  if ! ensure_build_deps; then
+    log "нет nvcc — CUDA-сборку пропускаем"
+    return 1
+  fi
+  log "nvcc $(nvcc --version 2>/dev/null | tail -n1 || echo '?')"
+
+  mkdir -p "$SRC_DIR"
+  export GIT_TERMINAL_PROMPT=0
+  if [[ -d "${SRC_DIR}/.git" ]]; then
+    git -C "$SRC_DIR" -c advice.detachedHead=false fetch --depth 1 origin tag "$tag" 2>>"$build_log" || true
+    if ! git -C "$SRC_DIR" -c advice.detachedHead=false checkout -f "$tag" >>"$build_log" 2>&1; then
+      rm -rf "$SRC_DIR"
+      git -c advice.detachedHead=false clone --depth 1 --branch "$tag" "$REPO" "$SRC_DIR" >>"$build_log" 2>&1
+    fi
+  else
+    rm -rf "$SRC_DIR"
+    git -c advice.detachedHead=false clone --depth 1 --branch "$tag" "$REPO" "$SRC_DIR" >>"$build_log" 2>&1
+  fi
+
+  cmake -S "$SRC_DIR" -B "${SRC_DIR}/build" \
+    -DCMAKE_BUILD_TYPE=Release \
+    -DGGML_CUDA=ON \
+    -DCMAKE_CUDA_ARCHITECTURES="${arch}" \
+    -DLLAMA_BUILD_SERVER=ON \
+    -DLLAMA_BUILD_UI=OFF \
+    -DLLAMA_USE_PREBUILT_UI=OFF \
+    -DGGML_CCACHE=OFF \
+    >>"$build_log" 2>&1
+
+  # Background build + heartbeat (keeps SSH stream alive without 200 cmake lines).
+  cmake --build "${SRC_DIR}/build" -j"$(nproc)" --target llama-server \
+    >>"$build_log" 2>&1 &
+  pid=$!
+  while kill -0 "$pid" 2>/dev/null; do
+    pct="$(grep -oE '\[[[:space:]]*[0-9]+%\]' "$build_log" 2>/dev/null | tail -n1 || true)"
+    line="$(grep -E 'Building CUDA|Built target|Linking' "$build_log" 2>/dev/null | tail -n1 || true)"
+    if [[ -n "$pct" ]]; then
+      log "сборка CUDA ещё идёт… ${pct}${line:+ · ${line}}"
+    else
+      log "сборка CUDA ещё идёт… (cmake/nvcc, см. build.log)"
+    fi
+    sleep 30
+  done
+  if ! wait "$pid"; then
+    log "сборка упала — хвост ${build_log}:"
+    tail -n 40 "$build_log" >&2 || true
+    return 1
+  fi
+
+  local built="${SRC_DIR}/build/bin/llama-server"
+  if [[ ! -x "$built" ]]; then
+    log "сборка не дала ${built}"
+    return 1
+  fi
+  install -m 755 "$built" "$SERVER_BIN"
+  # CUDA build may need libs from build/bin
+  find "${SRC_DIR}/build/bin" -maxdepth 1 -type f \( -name '*.so' -o -name '*.so.*' \) -print0 2>/dev/null \
+    | while IFS= read -r -d '' so; do
+        install -m 755 "$so" "${BIN_DIR}/$(basename "$so")"
+      done
+  chown -R "${SWARM_USER}:${SWARM_USER}" "$BIN_DIR"
+  echo "cuda:${tag}:${arch}" >"$STAMP"
+  chown "${SWARM_USER}:${SWARM_USER}" "$STAMP"
+  log "CUDA binary → ${SERVER_BIN}"
+  return 0
+}
+
+normalize_tag() {
+  printf '%s' "$1" | tr -d '\r' | head -n1 | awk 'NF{print; exit}'
+}
+
 if [[ -x "$SERVER_BIN" ]]; then
   log "llama-server уже есть: ${SERVER_BIN}"
 else
   if [[ -n "$LLAMACPP_ASSET_URL" ]]; then
-    log "скачиваю llama-server по LLAMACPP_ASSET_URL…"
+    log "скачиваю по LLAMACPP_ASSET_URL…"
     install_from_archive_url "$LLAMACPP_ASSET_URL"
     echo "asset-url" >"$STAMP"
     chown "${SWARM_USER}:${SWARM_USER}" "$STAMP"
   else
     if [[ -z "$LLAMACPP_TAG" ]]; then
-      log "WARN: LLAMACPP_TAG/ASSET_URL не заданы — берём latest (нет pin). См. docs/llm.md"
+      log "WARN: LLAMACPP_TAG не задан — latest (см. docs/llm.md)"
     fi
-    tag="$(resolve_release_tag)"
+    tag="$(normalize_tag "$(resolve_release_tag)")"
-    tag="$(printf '%s' "$tag" | tr -d '\r' | head -n1 | awk 'NF{print; exit}')"
     if [[ -z "$tag" || "$tag" == *" "* || "$tag" == *"["* ]]; then
       log "не удалось определить release tag (got: ${tag:-empty})"
       exit 1
     fi
-    if ! build_cuda_from_source "$tag"; then
+
-      log "CUDA-сборка не удалась — fallback на Linux release asset"
+    installed=0
-      install_linux_release_fallback "$tag"
+    if [[ "${LLAMACPP_BUILD_CUDA}" == "1" ]]; then
+      log "LLAMACPP_BUILD_CUDA=1 — сразу CUDA-сборка"
+      if build_cuda_from_source "$tag"; then
+        installed=1
+      fi
+    else
+      if install_linux_release "$tag"; then
+        installed=1
+      else
+        log "prebuilt не вышел — крайний случай: CUDA из исходников"
+        if build_cuda_from_source "$tag"; then
+          installed=1
+        fi
+      fi
+    fi
+    if [[ "$installed" != "1" ]]; then
+      log "не удалось поставить llama-server"
+      exit 1
     fi
   fi
 fi
@@ -292,7 +377,14 @@ else:
 PY
 )" || true
 fi
-log "llama.cpp -ngl ${NGL} -c ${CTX}"
+# Explicit overrides from gpu-rent.vars / .env (forwarded by provision).
+if [[ -n "$LLAMACPP_NGL" ]]; then
+  NGL="$LLAMACPP_NGL"
+fi
+if [[ -n "$LLAMACPP_CTX" ]]; then
+  CTX="$LLAMACPP_CTX"
+fi
+log "llama.cpp -ngl ${NGL} -c ${CTX} host=${LLAMACPP_HOST} port=${LLAMACPP_PORT}${LLAMACPP_EXTRA_ARGS:+ extra=${LLAMACPP_EXTRA_ARGS}}"
 
 cat >/etc/systemd/system/${UNIT}.service <<EOF
 [Unit]
@@ -305,7 +397,8 @@ Type=simple
 User=${SWARM_USER}
 Group=${SWARM_USER}
 WorkingDirectory=${LLAMA_ROOT}
-ExecStart=${SERVER_BIN} ${MODEL_ARG} ${MMPROJ_ARG} --host 127.0.0.1 --port 8080 -ngl ${NGL} -c ${CTX}
+Environment=LD_LIBRARY_PATH=${BIN_DIR}
+ExecStart=${SERVER_BIN} ${MODEL_ARG} ${MMPROJ_ARG} --host ${LLAMACPP_HOST} --port ${LLAMACPP_PORT} -ngl ${NGL} -c ${CTX} ${LLAMACPP_EXTRA_ARGS}
 Restart=on-failure
 RestartSec=8
 

src/gpu_rent/ssh_ops.py

@@ -323,7 +323,9 @@ def run_script_sudo(
             fh.write(script)
         sftp.chmod(remote_path, 0o755)
         sftp.close()
-        env_s = " ".join(f"{key}={value}" for key, value in (env or {}).items())
+        env_s = " ".join(
+            f"{key}={shlex.quote(str(value))}" for key, value in (env or {}).items()
+        )
         prefix = f"sudo -n env {env_s} " if env_s else "sudo -n "
         command = f"{prefix}bash {remote_path}"
         _stdin, stdout, stderr = client.exec_command(command, timeout=timeout, get_pty=True)

tests/test_remote_llm_env.py

@@ -0,0 +1,27 @@
+from types import SimpleNamespace
+
+from gpu_rent.provision import _LLAMACPP_INSTALL_ENV, _remote_llm_env
+
+
+def test_remote_llm_env_forwards_llamacpp_vars(monkeypatch):
+    monkeypatch.setenv("LLAMACPP_TAG", "b10545")
+    monkeypatch.setenv("LLAMACPP_BUILD_CUDA", "1")
+    monkeypatch.setenv("LLAMACPP_NGL", "40")
+    monkeypatch.setenv("LLAMACPP_ASSET_URL", "https://example/a.tar.gz")
+    monkeypatch.delenv("LLAMACPP_SHA256", raising=False)
+    cfg = SimpleNamespace(ssh_user="ubuntu")
+    env = _remote_llm_env(cfg, *_LLAMACPP_INSTALL_ENV)
+    assert env["SWARM_USER"] == "ubuntu"
+    assert env["LLAMACPP_TAG"] == "b10545"
+    assert env["LLAMACPP_BUILD_CUDA"] == "1"
+    assert env["LLAMACPP_NGL"] == "40"
+    assert env["LLAMACPP_ASSET_URL"] == "https://example/a.tar.gz"
+    assert "LLAMACPP_SHA256" not in env
+
+
+def test_remote_llm_env_skips_empty(monkeypatch):
+    for key in _LLAMACPP_INSTALL_ENV:
+        monkeypatch.delenv(key, raising=False)
+    cfg = SimpleNamespace(ssh_user="ubuntu")
+    env = _remote_llm_env(cfg, *_LLAMACPP_INSTALL_ENV)
+    assert env == {"SWARM_USER": "ubuntu"}