Initial commit

.gitignore

@@ -0,0 +1,4 @@
+# machine-specific files generated by ./setup.sh
+Dockerfile
+RUN_AIDER.md
+.ai-config

Dockerfile.template

@@ -0,0 +1,17 @@
+# Template - do not build directly. ./setup.sh renders this to ./Dockerfile
+# by substituting the @@...@@ placeholders for the model chosen for this machine.
+FROM ollama/ollama:latest
+
+ENV OLLAMA_HOST=0.0.0.0:11434 \
+    OLLAMA_CONTEXT_LENGTH=@@CONTEXT@@ \
+    OLLAMA_KEEP_ALIVE=30m
+
+# Bake the model into the image so the container works offline after the build.
+RUN set -e; \
+    ollama serve > /tmp/ollama-build.log 2>&1 & \
+    pid=$!; \
+    for i in $(seq 1 60); do ollama list >/dev/null 2>&1 && break; sleep 1; done; \
+    ollama pull @@MODEL@@; \
+    kill $pid; wait $pid || true
+
+EXPOSE 11434

README.md

@@ -0,0 +1,16 @@
+# local_ai
+
+Local coding AI for [aider](https://aider.chat): an Ollama container with a model
+chosen for *this* machine. Aider runs on the host and talks to the container.
+
+```bash
+./setup.sh            # analyse machine, pick model, generate Dockerfile + RUN_AIDER.md
+./build-ai-image.sh   # build image (downloads the model)
+./run-ai-image.sh     # start container on 127.0.0.1:11434
+# then follow RUN_AIDER.md to start aider
+```
+
+- `models.tsv` – model catalogue (edit to add models)
+- `Dockerfile.template` – container template; `setup.sh` fills in the model
+- `Dockerfile`, `.ai-config`, `RUN_AIDER.md` – generated per machine (git-ignored)
+- `./setup.sh --model <tag> --yes` for non-interactive use

build-ai-image.sh

@@ -0,0 +1,13 @@
+#!/usr/bin/env bash
+# Build the local AI container image (model is downloaded into the image).
+set -euo pipefail
+cd "$(dirname "$0")"
+
+[ -f .ai-config ] && [ -f Dockerfile ] || { echo "No Dockerfile/.ai-config yet - run ./setup.sh first." >&2; exit 1; }
+# shellcheck disable=SC1091
+source .ai-config
+
+echo "Building $IMAGE_NAME (model: $MODEL) - this downloads the model, may take a while..."
+docker build -t "$IMAGE_NAME" .
+echo
+echo "Done. Next: ./run-ai-image.sh"

models.tsv

@@ -0,0 +1,12 @@
+# ollama_tag	disk_gb	mem_gb	cpu_ok	rank	description
+# disk_gb: download / image size. mem_gb: RAM (or VRAM) needed incl. context cache.
+# cpu_ok: yes = usable speed without a GPU (small dense or MoE with few active params).
+# rank: higher = better quality for coding; the recommender picks the highest rank that fits.
+qwen2.5-coder:1.5b	1.0	2.0	yes	10	Tiny; very fast, weak edits. For very small machines.
+qwen2.5-coder:3b	1.9	3.0	yes	20	Small and fast; OK for simple edits.
+qwen2.5-coder:7b	4.7	6.5	yes	30	Best all-round choice for laptops / CPU-only.
+deepseek-coder-v2:16b	8.9	11.5	yes	35	MoE (2.4B active): fast on CPU, needs ~12 GB RAM.
+qwen2.5-coder:14b	9.0	11.5	no	40	Noticeably smarter than 7B; wants a GPU (~12 GB VRAM).
+gpt-oss:20b	14	17	yes	50	MoE reasoning model; needs ~17 GB RAM or VRAM.
+qwen3-coder:30b	19	23	yes	60	MoE (3B active): strong coder, fast on CPU with 24+ GB RAM.
+qwen2.5-coder:32b	20	24	no	70	Top-quality dense coder; needs a 24 GB GPU.

run-ai-image.sh

@@ -0,0 +1,47 @@
+#!/usr/bin/env bash
+# Start / stop the local AI container.
+#   ./run-ai-image.sh          start (default)
+#   ./run-ai-image.sh stop     stop and remove
+#   ./run-ai-image.sh logs     follow container logs
+#   ./run-ai-image.sh status   show state
+set -euo pipefail
+cd "$(dirname "$0")"
+
+[ -f .ai-config ] || { echo "No .ai-config yet - run ./setup.sh first." >&2; exit 1; }
+# shellcheck disable=SC1091
+source .ai-config
+
+running() { [ "$(docker inspect -f '{{.State.Running}}' "$CONTAINER_NAME" 2>/dev/null)" = "true" ]; }
+exists()  { docker inspect "$CONTAINER_NAME" >/dev/null 2>&1; }
+
+case "${1:-start}" in
+  stop)   exists && docker rm -f "$CONTAINER_NAME" >/dev/null && echo "Stopped." || echo "Not running."; exit 0 ;;
+  logs)   exec docker logs -f "$CONTAINER_NAME" ;;
+  status) running && echo "Running: http://127.0.0.1:$PORT (model $MODEL)" || echo "Not running."; exit 0 ;;
+  start)  ;;
+  *) echo "Usage: $0 [start|stop|logs|status]" >&2; exit 1 ;;
+esac
+
+docker image inspect "$IMAGE_NAME" >/dev/null 2>&1 || { echo "Image missing - run ./build-ai-image.sh first." >&2; exit 1; }
+
+if running; then
+  echo "Already running on http://127.0.0.1:$PORT"
+else
+  exists && docker rm -f "$CONTAINER_NAME" >/dev/null
+  args=(-d --name "$CONTAINER_NAME" -p "127.0.0.1:$PORT:11434" --restart unless-stopped)
+  [ "$USE_GPU" = "1" ] && args+=(--gpus all)
+  docker run "${args[@]}" "$IMAGE_NAME" >/dev/null
+  printf "Waiting for the model server"
+  for _ in $(seq 1 60); do
+    curl -fs "http://127.0.0.1:$PORT/api/tags" >/dev/null 2>&1 && break
+    printf "."; sleep 1
+  done
+  echo
+  curl -fs "http://127.0.0.1:$PORT/api/tags" >/dev/null 2>&1 || { echo "Server did not come up; see: ./run-ai-image.sh logs" >&2; exit 1; }
+  echo "Running on http://127.0.0.1:$PORT"
+fi
+
+echo
+echo "Now, in your project directory:"
+echo "  OLLAMA_API_BASE=http://127.0.0.1:$PORT aider --model ollama_chat/$MODEL"
+echo "(see RUN_AIDER.md for details)"

setup.sh

@@ -0,0 +1,243 @@
+#!/usr/bin/env bash
+# Analyse this machine, recommend a local coding model, and generate:
+#   Dockerfile   (from Dockerfile.template, with the chosen model)
+#   .ai-config   (settings used by build-ai-image.sh / run-ai-image.sh)
+#   RUN_AIDER.md (how to run aider against the container on this machine)
+#
+# Usage: ./setup.sh [--model <ollama-tag>] [--gpu|--no-gpu] [--yes]
+set -euo pipefail
+cd "$(dirname "$0")"
+
+FORCE_MODEL=""; FORCE_GPU=""; ASSUME_YES=0
+while [ $# -gt 0 ]; do
+  case "$1" in
+    --model) FORCE_MODEL="${2:?--model needs a value}"; shift ;;
+    --gpu) FORCE_GPU=1 ;;
+    --no-gpu) FORCE_GPU=0 ;;
+    --yes|-y) ASSUME_YES=1 ;;
+    -h|--help) sed -n '2,8p' "$0" | sed 's/^# \?//'; exit 0 ;;
+    *) echo "Unknown option: $1" >&2; exit 1 ;;
+  esac
+  shift
+done
+
+IMAGE_NAME="local-ai"; CONTAINER_NAME="local-ai"; PORT=11434
+NOTES=()   # machine-specific remarks, echoed to the terminal and RUN_AIDER.md
+
+# ---------------------------------------------------------------- detection
+cpu_model=$(grep -m1 'model name' /proc/cpuinfo | cut -d: -f2- | xargs || true)
+cpu_threads=$(nproc)
+ram_gb=$(awk '/MemTotal/ {printf "%.1f", $2/1024/1024}' /proc/meminfo)
+disk_gb=$(df -Pk . | awk 'NR==2 {printf "%.0f", $4/1024/1024}')
+grep -qw avx2 /proc/cpuinfo && avx2=yes || avx2=no
+
+gpu_name=""; vram_gb=0; gpu_usable=0; nodriver_gpu=0; est_vram=0
+if command -v nvidia-smi >/dev/null 2>&1 && nvidia-smi -L >/dev/null 2>&1; then
+  gpu_name=$(nvidia-smi --query-gpu=name --format=csv,noheader | head -1)
+  vram_mb=$(nvidia-smi --query-gpu=memory.total --format=csv,noheader,nounits | head -1)
+  vram_gb=$(awk -v m="$vram_mb" 'BEGIN {printf "%.1f", m/1024}')
+  gpu_usable=1
+elif command -v lspci >/dev/null 2>&1 && lspci | grep -iE 'vga|3d' | grep -qi nvidia; then
+  gpu_name=$(lspci | grep -iE 'vga|3d' | grep -i nvidia | head -1 | sed 's/.*: //')
+  nodriver_gpu=1
+  # VRAM can't be read without a driver; estimate from the card name (unknown -> 0)
+  case "$gpu_name" in
+    *"1650"*|*"1050 Ti"*|*"3050 Mobile"*|*"3050 Laptop"*) est_vram=4 ;;
+    *"1660"*|*"2060"*|*"1060"*|*"2070"*|*"2080"*|*"3060 Mobile"*|*"3060 Laptop"*|*"3070"*|*"3060 Ti"*) est_vram=6 ;;
+    *"3060"*|*"4060"*|*"2080 Ti"*|*"3080"*|*"4070"*) est_vram=12 ;;
+    *"3090"*|*"4090"*) est_vram=24 ;;
+  esac
+fi
+
+have_docker=no; docker_ok=no; docker_nvidia=no
+if command -v docker >/dev/null 2>&1; then
+  have_docker=yes
+  if docker info >/dev/null 2>&1; then
+    docker_ok=yes
+    docker info 2>/dev/null | grep -qi nvidia && docker_nvidia=yes
+  fi
+fi
+if [ "$gpu_usable" = 1 ] && [ "$docker_nvidia" = no ]; then
+  NOTES+=("GPU $gpu_name found but Docker has no NVIDIA runtime (install nvidia-container-toolkit). Running on CPU.")
+  gpu_usable=0
+fi
+[ "$FORCE_GPU" = 1 ] && gpu_usable=1
+[ "$FORCE_GPU" = 0 ] && gpu_usable=0
+[ "$have_docker" = no ] && NOTES+=("Docker not found - install it before running ./build-ai-image.sh.")
+[ "$have_docker" = yes ] && [ "$docker_ok" = no ] && NOTES+=("Docker is installed but not usable by this user (daemon down, or add yourself to the 'docker' group).")
+[ "$avx2" = no ] && NOTES+=("CPU has no AVX2; CPU inference will be slow.")
+command -v aider >/dev/null 2>&1 && aider_ok=yes || aider_ok=no
+
+echo "=== This machine ==="
+printf "  CPU    : %s (%s threads, AVX2: %s)\n" "$cpu_model" "$cpu_threads" "$avx2"
+printf "  RAM    : %s GB\n" "$ram_gb"
+if [ "$gpu_usable" = 1 ]; then printf "  GPU    : %s (%s GB VRAM) - will be used\n" "$gpu_name" "$vram_gb"
+elif [ -n "$gpu_name" ]; then printf "  GPU    : %s (not usable)\n" "$gpu_name"
+else printf "  GPU    : none\n"; fi
+printf "  Disk   : %s GB free here\n" "$disk_gb"
+printf "  Docker : %s   aider: %s\n" "$docker_ok" "$aider_ok"
+echo
+
+# ------------------------------------------------ memory budget / context
+# What a model must fit into: VRAM when a GPU is used, else RAM minus headroom
+# for the OS, aider and the editor.
+budget=$(awk -v r="$ram_gb" 'BEGIN {b=r-4; if (b<1) b=1; printf "%.1f", b}')
+gpu_budget=$(awk -v v="$vram_gb" 'BEGIN {printf "%.1f", v*0.9}')
+
+if   awk -v b="$budget" 'BEGIN{exit !(b<6)}';  then CONTEXT=4096
+elif awk -v b="$budget" 'BEGIN{exit !(b>=40)}'; then CONTEXT=32768
+elif awk -v b="$budget" 'BEGIN{exit !(b>=20)}'; then CONTEXT=16384
+else CONTEXT=8192; fi
+
+# ---------------------------------------------------------------- catalogue
+mapfile -t rows < <(grep -v '^#' models.tsv | grep -v '^[[:space:]]*$' | sort -t$'\t' -k5,5n)
+tags=(); status=(); speed=(); rec=-1
+for i in "${!rows[@]}"; do
+  IFS=$'\t' read -r tag disk mem cpu_ok rank desc <<<"${rows[$i]}"
+  tags[i]=$tag
+  fits_ram=$(awk -v m="$mem" -v b="$budget" 'BEGIN{print (m<=b)}')
+  fits_gpu=$(awk -v m="$mem" -v b="$gpu_budget" -v u="$gpu_usable" 'BEGIN{print (u && m<=b)}')
+  if [ "$fits_gpu" = 1 ]; then status[i]="fits"; speed[i]="fast (GPU)"
+  elif [ "$fits_ram" = 1 ] && [ "$cpu_ok" = yes ]; then status[i]="fits"; speed[i]="ok (CPU)"
+  elif [ "$fits_ram" = 1 ]; then status[i]="slow"; speed[i]="slow (CPU)"
+  else status[i]="too big"; speed[i]="-"; fi
+  [ "${status[$i]}" = fits ] && rec=$i     # sorted by rank, so last "fits" wins
+done
+[ "$rec" -ge 0 ] || rec=0
+
+# ------------------------------------------- would a GPU driver help?
+if [ "$nodriver_gpu" = 1 ]; then
+  best=-1
+  if awk -v v="$est_vram" 'BEGIN{exit !(v>0)}'; then
+    for i in "${!rows[@]}"; do
+      mem=$(cut -f3 <<<"${rows[$i]}")
+      awk -v m="$mem" -v v="$est_vram" 'BEGIN{exit !(m<=v*0.9)}' && best=$i
+    done
+  fi
+  drv="install the NVIDIA driver and nvidia-container-toolkit, then re-run ./setup.sh"
+  if [ "$est_vram" = 0 ]; then
+    NOTES+=("NVIDIA GPU ($gpu_name) found but not usable (no driver). Installing the driver may speed things up a lot if it has enough VRAM: $drv.")
+  elif [ "$best" -gt "$rec" ]; then
+    NOTES+=("Worth installing the NVIDIA driver: with ~${est_vram} GB VRAM, ${tags[$best]} would run fully on the GPU (better than the current CPU pick ${tags[$rec]}). $drv.")
+  elif [ "$best" -ge 0 ]; then
+    NOTES+=("Optional: an NVIDIA driver would make models up to ${tags[$best]} run fully on the ~${est_vram} GB GPU (much faster) and partly speed up larger ones such as ${tags[$rec]} via partial offload, but ${est_vram} GB VRAM does not unlock bigger models than ${tags[$rec]}. Worth it for speed: $drv.")
+  else
+    NOTES+=("A driver would not help much: ~${est_vram} GB VRAM is too small to hold even the smallest model.")
+  fi
+fi
+
+# --------------------------------------------------------------------- menu
+if [ -z "$FORCE_MODEL" ]; then
+  echo "=== Models (budget: ${budget} GB RAM$([ "$gpu_usable" = 1 ] && echo ", ${gpu_budget} GB VRAM")) ==="
+  printf "  %-3s %-24s %7s %7s  %-9s %-11s %s\n" "#" "model" "disk" "memory" "fit" "speed" ""
+  for i in "${!rows[@]}"; do
+    IFS=$'\t' read -r tag disk mem cpu_ok rank desc <<<"${rows[$i]}"
+    mark=" "; [ "$i" = "$rec" ] && mark="*"
+    printf " %s%-3s %-24s %5s GB %5s GB  %-9s %-11s %s\n" "$mark" "$((i+1))" "$tag" "$disk" "$mem" "${status[$i]}" "${speed[$i]}" "$desc"
+  done
+  echo
+  echo "  * = recommended for this machine: ${tags[$rec]}"
+  echo
+  if [ "$ASSUME_YES" = 1 ]; then choice=""; else read -r -p "Select model number [$((rec+1))]: " choice; fi
+  choice=${choice:-$((rec+1))}
+  [[ "$choice" =~ ^[0-9]+$ ]] && [ "$choice" -ge 1 ] && [ "$choice" -le "${#rows[@]}" ] || { echo "Invalid choice." >&2; exit 1; }
+  sel=$((choice-1))
+  MODEL=${tags[$sel]}
+  if [ "${status[$sel]}" = "too big" ] || [ "${status[$sel]}" = "slow" ]; then
+    echo "Warning: $MODEL is '${status[$sel]}' on this machine (may swap, crawl, or crash)."
+    if [ "$ASSUME_YES" != 1 ]; then read -r -p "Continue anyway? [y/N] " a; [[ "$a" =~ ^[Yy] ]] || exit 1; fi
+  fi
+  sel_disk=$(cut -f2 <<<"${rows[$sel]}")
+else
+  MODEL=$FORCE_MODEL; sel_disk=0
+  for r in "${rows[@]}"; do [ "$(cut -f1 <<<"$r")" = "$MODEL" ] && sel_disk=$(cut -f2 <<<"$r"); done
+fi
+if awk -v d="$sel_disk" -v f="$disk_gb" 'BEGIN{exit !(d*2>f)}'; then
+  NOTES+=("Low disk space: ${disk_gb} GB free, the build needs roughly 2x the model size (${sel_disk} GB).")
+fi
+
+# ------------------------------------------------------------------- output
+USE_GPU=$gpu_usable
+sed -e "s|@@MODEL@@|$MODEL|g" -e "s|@@CONTEXT@@|$CONTEXT|g" Dockerfile.template > Dockerfile
+cat > .ai-config <<EOF
+# generated by ./setup.sh - machine specific, not committed
+MODEL="$MODEL"
+CONTEXT=$CONTEXT
+USE_GPU=$USE_GPU
+IMAGE_NAME="$IMAGE_NAME"
+CONTAINER_NAME="$CONTAINER_NAME"
+PORT=$PORT
+EOF
+
+if [ "$USE_GPU" = 1 ]; then where="on your GPU ($gpu_name)"; else where="on your CPU ($cpu_threads threads, no GPU)"; fi
+case "$MODEL" in
+  *:1.5b|*:3b) EDIT_HINT=$'\nSmall models struggle with diff-style edits; add `--edit-format whole`.\n' ;;
+  *) EDIT_HINT="" ;;
+esac
+if [ "$USE_GPU" = 1 ]; then SPEED="Expect fast responses."
+else SPEED="CPU-only: expect a few tokens/second, and a slow first response while the model loads into RAM. Keep prompts and added files small."; fi
+
+{
+cat <<EOF
+# Running aider with the local AI
+
+Generated by \`./setup.sh\` for this machine:
+
+- CPU: $cpu_model ($cpu_threads threads)
+- RAM: $ram_gb GB, GPU: ${gpu_name:-none}$([ "$USE_GPU" = 1 ] && echo " ($vram_gb GB VRAM)" || echo " (not used)")
+- Model: \`$MODEL\` running $where, context window $CONTEXT tokens
+
+$SPEED
+
+## 1. Build and start the container
+
+\`\`\`bash
+./build-ai-image.sh     # once; downloads the model into the image
+./run-ai-image.sh       # starts the server on http://127.0.0.1:$PORT
+./run-ai-image.sh stop  # when you are done
+\`\`\`
+
+## 2. Install aider (once)
+
+\`\`\`bash
+python3 -m pip install --user pipx && pipx install aider-chat
+# or: python3 -m pip install aider-install && aider-install
+\`\`\`
+
+## 3. Run aider in your project (a git repo)
+
+\`\`\`bash
+cd /path/to/your/project
+export OLLAMA_API_BASE=http://127.0.0.1:$PORT
+aider --model ollama_chat/$MODEL
+\`\`\`
+$EDIT_HINT
+Tip: put those in \`~/.aider.conf.yml\` to skip the flags:
+
+\`\`\`yaml
+model: ollama_chat/$MODEL
+\`\`\`
+
+(and keep \`export OLLAMA_API_BASE=...\` in your shell profile).
+
+## Troubleshooting
+
+- \`./run-ai-image.sh logs\` shows the server log; \`./run-ai-image.sh status\` shows its state.
+- Nothing is sent off the machine once the image is built.
+- To switch model or after changing hardware, re-run \`./setup.sh\` then \`./build-ai-image.sh\`.
+EOF
+if [ "${#NOTES[@]}" -gt 0 ]; then
+  echo; echo "## Notes about this machine"; echo
+  for n in "${NOTES[@]}"; do echo "- $n"; done
+fi
+} > RUN_AIDER.md
+
+echo
+echo "Selected: $MODEL (context $CONTEXT, GPU: $([ "$USE_GPU" = 1 ] && echo yes || echo no))"
+for n in "${NOTES[@]}"; do echo "Note: $n"; done
+echo "Generated: Dockerfile  .ai-config  RUN_AIDER.md"
+echo
+echo "Next steps:"
+echo "  ./build-ai-image.sh"
+echo "  ./run-ai-image.sh"
+echo "  then follow RUN_AIDER.md to start aider"