gemma4-comfyui-mcp

gemma4-comfyui-mcp

Local, offline LLMs fine-tuned to be ComfyUI experts that drive the full comfyui-mcp tool surface — 178 tools (113 MCP server tools + 65 live-canvas panel_* tools) — in Gemma 4's native tool-call format. These are the free, no-API-key backends for the comfyui-mcp Agent Panel: pick Ollama (local) in the panel and the agent edits your live graph, generates images/video/audio, manages models and custom nodes, and runs your workflows — entirely on your GPU.

Quickstart (Ollama, recommended):

ollama pull artokun/gemma4-comfyui-mcp:e4b   # default — ~3.5 GB VRAM at q4
# also:  :e2b (~2 GB VRAM)   :12b (~8 GB VRAM)

Size ladder

Pick by VRAM budget. Every rung is the same data, same recipe — only the base size changes.

Each folder ships the GGUF quant(s), the LoRA adapter (r=32 — apply to the base or re-export any quant yourself), the trained chat template, and tokenizer/trainer configs.

What makes these different

A stock model meets comfyui-mcp's ~200-schema catalog cold and burns its context (and your patience) learning it. These models were trained on the tool suite itself:

• Data: 1,055 server-verified tool-use trajectories (e4b/e2b; the earlier 12b rung trained on the initial 772) — synthesized TOUCAN-style against a live ComfyUI + comfyui-mcp server: task synthesis → real trajectory generation → server-verified filtering (every outcome checked against the actual server state, never the model's claims). Grounded in real pack workflows (krea2 / ideogram / qwen-edit / wan / ltxv) and skills. Fully open: artokun/comfyui-mcp-trajectories.
• Trimmed-context tool-menu training: each example is rendered with the tools it actually calls plus random distractors (≤24 of the 178), in random order — the model learns to navigate a menu, not memorize positions, and generalizes to the full surface at inference (the xLAM/Toucan/Hermes recipe).
• Teachers: open-weight only. No Anthropic/OpenAI/Google outputs anywhere in the training data (ToS), enforced by a blocklist in the dataset assembly.
• Bases: the Heretic-abliterated Gemma 4 family (Optuna-optimized directional ablation — cleanest abliteration lineage), chosen by a server-verified bake-off over other abliterated variants.
• Recipe: QLoRA r=32 / α=32 on q/k/v/o + MLP projections, 2 epochs, cosine LR 2e-4, seq len 12–16K, loss masked to assistant turns.

Validation

Every rung is validated in llama-server --jinja before release: native tool-call emission (finish_reason=tool_calls), correct tool selection, well-formed JSON arguments, and dimension/parameter extraction from natural-language requests. Final train loss: 0.022 (e4b), 0.021 (e2b); the 12b rung additionally passed live end-to-end panel runs.

Known behavior: :e2b reasons verbosely before calling — give it max_tokens ≥ 512 or the call gets truncated mid-thought. The current :e2b tag is the v2 retrain (see Arena results below).

Usage

ComfyUI Agent Panel (the intended home): install comfyui-mcp, pull a tag, pick Ollama (local) in the panel's backend picker. :e4b is the built-in default — zero further config.

Plain Ollama / any OpenAI-compatible client:

ollama pull artokun/gemma4-comfyui-mcp:e4b
# tags ship num_ctx 65536 + temperature 0 + the ComfyUI agent system prompt

llama.cpp:

llama-server -m e4b/model-q4_k_m.gguf --jinja -c 16384

From the adapter (transformers + PEFT): load the base (AutoModelForImageTextToText — Gemma 4 is the unified multimodal arch), attach <size>/adapter_model.safetensors, merge_and_unload(). A clean re-export path (sharded 16-bit + GGUF) ships in finetune/train/reexport.py.

Repo layout

12b/ e4b/ e2b/        model-q4_k_m.gguf (+q5/q8 where built), LoRA adapter,
                      chat_template.jinja, tokenizer/trainer configs
finetune/             the COMPLETE pipeline: datagen (task synth + tool-surface
                      export), train (QLoRA + dataset prep + re-export),
                      arena (server-verified eval harness), package (Modelfile)
finetune/data/        train/val JSONL (1,055 trajectories) + the 178-tool surface

Limits

Tool calling is the trained strength. Vision is limited at these sizes — the agent generates but can't visually critique its own outputs — and hard multi-stage graph composition (multi-output pipelines built in one shot) remains frontier-model territory (see the measured arena table below; the harness is in finetune/arena/).

Arena results (measured, server-verified)

Scores on the comfyui-mcp LLM Arena — a 10-scenario ladder (health → generation → break/fix → multi-stage pipelines) where every result is verified against a live ComfyUI server, not the model's claims. Best of 3 full runs per model, RTX 4090, PASS=2/PARTIAL=1/FAIL=0:

Every rung beats its stock base. For calibration, the same ladder scores frontier hosted models 19–20/20 and the best small hosted models 17–19/20 — these are honest local-tier numbers, not marketing.

e2b v2 note: the initial e2b release regressed below stock (4/20) — the smallest rung couldn't bridge between the direct-call training format and the deployed 6-tool router envelope, dropping call_tool's name field. v2 retrains on a dual-view mix (direct calls + the exact runtime router format + fresh server-verified arena trajectories) — zero malformed envelopes across all verdict runs. Details: comfyui-mcp#183.

Credits & license

Bases by p-e-w (Heretic) and coder3101; Gemma 4 by Google DeepMind (Gemma license). Fine-tune, data, and pipeline by @artokun — part of the comfyui-mcp project.