57_WolveandClaude Opus 4.7
20d4bd5b72
The abliterated 9B was the source of the tool-call format mangling
(both Native XML leaks and Default Python-syntax leaks). Standard
qwen3.5:9b is the same family, same 9B size (6.6 GB), vision-capable
and native tool calling actually works.
The image content uncensored-ness was always going to come from the
SDXL checkpoints in ComfyUI — the LLM is just a dispatcher. Picking
a well-behaved tool-caller for that role doesn't compromise output
content.
Updated:
- image_studio.json base_model_id → qwen3.5:9b
- init-models.sh: pulls qwen3.5:9b as a standard registry pull,
in addition to the existing abliterated 9B (which stays for
other chat models)
- image_studio.md setup table + vision section explaining why
we chose standard over abliterated for the dispatcher role
function_calling stays as 'default' and tool_choice as 'required'
for now — they don't hurt with a reliable tool-caller and operators
can flip back to native + drop tool_choice once they verify it
works for them (which also removes the need for a separate Task
Model for title generation).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
69 lines
2.6 KiB
Bash
69 lines
2.6 KiB
Bash
#!/bin/sh
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# Preseed Ollama with the models the stack should have available at startup.
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# Runs once via the model-init service (see docker-compose.yml). Safe to
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# re-run — already-present models are skipped.
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#
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# Two pull paths:
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# - s3_pull — fetches a tarball from $S3_OLLAMA_BASE (your own mirror,
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# created by mirror-ollama-model.sh) and extracts into
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# Ollama's data dir. Faster + immune to upstream changes.
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# Falls back to ollama pull if S3_OLLAMA_BASE is unset.
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# - pull — standard `ollama pull` against registry.ollama.ai.
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set -e
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# Make sure curl is available — ollama/ollama:latest doesn't always include
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# it, and s3_pull needs it. tar is in the base image.
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if ! command -v curl >/dev/null 2>&1; then
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apt-get update -qq && apt-get install -y -qq curl ca-certificates >/dev/null
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fi
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S3_OLLAMA_BASE="${S3_OLLAMA_BASE:-}"
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OLLAMA_DATA="/root/.ollama"
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s3_pull() {
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name="$1"; archive="$2"
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if ollama list 2>/dev/null | awk 'NR>1 {print $1}' | grep -qx "$name"; then
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echo "✓ $name already present"
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return
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fi
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if [ -z "$S3_OLLAMA_BASE" ]; then
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echo "→ $name: S3_OLLAMA_BASE unset, falling back to ollama pull"
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ollama pull "$name"
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return
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fi
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url="${S3_OLLAMA_BASE%/}/$archive"
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echo "→ Downloading $name from $url…"
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curl -fL -C - --retry 3 -o "/tmp/$archive" "$url"
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tar -xzf "/tmp/$archive" -C "$OLLAMA_DATA/models/"
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rm -f "/tmp/$archive"
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echo "✓ $name installed (mirror)"
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}
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pull() {
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name="$1"
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if ollama list 2>/dev/null | awk 'NR>1 {print $1}' | grep -qx "$name"; then
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echo "✓ $name already present"
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else
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echo "→ Pulling $name from registry.ollama.ai…"
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ollama pull "$name"
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fi
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}
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# ─── S3-mirrored models ─────────────────────────────────────────────────────
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# These live in your own bucket. Create the tarballs once with
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# mirror-ollama-model.sh, upload to S3, then list them here.
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s3_pull "huihui_ai/qwen3.5-abliterated:9b" "qwen3.5-abliterated-9b.tgz"
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# Standard non-abliterated Qwen 3.5 9B — 6.6 GB, vision + native tool
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# calling. Used as the Image Studio dispatcher (the abliterated 9B above
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# is fine for chat but mangles native tool-call formatting).
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pull "qwen3.5:9b"
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# ─── Direct registry pulls ──────────────────────────────────────────────────
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for model in dolphin3:8b llama3.1:8b ministral-3:8b mistral-nemo:12b qwen3.6:latest; do
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pull "$model"
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done
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echo "Done."
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