SwarmDo-A1 is an open, self-hostable, execution-verified, multimodal coding agent — a LoRA adapter on Qwen/Qwen3.6-27B that fixes real software bugs and writes code from images, and verifies its own patches by running the project's tests. Built in Australia; measured, not marketing — the negatives are published too.
SwarmDo-A1 is a coding system, not just a checkpoint: a strong re-based open base + a hardened agentic
harness that verifies patches by running the project's tests, plus a differentiated exec-grounded
visual-coding capability (turning a screenshot or mockup into working code and checking it by rendering
and comparing). This repository holds the fine-tuned LoRA adapter (the "A1" delta); the base weights
are the open Qwen/Qwen3.6-27B. The adapter reaches the architecture's gated-DeltaNet sequence-mixing
layers (in_proj_*, out_proj), not just the standard attention projections.
These are relative, held-out, execution-verified comparisons on our own task sets — not an absolute
public-leaderboard pass rate. We deliberately do not post a single "SWE-bench Verified score" we cannot
stand behind under paired testing.
Honest limitations (published, not buried)
This adapter, as a single model, is ~neutral vs the base. Single-model fine-tuning trades a small
quality gain against a robustness cost (a "churn wall"). A1's value is the system — the base
plus execution-grounded best-of-N selection, where the win above (p = 0.0005) lives.
Specialized for Python-centric agentic SWE. Like all coding agents it can produce plausible-but-wrong
patches — always run the project's tests against its output. Not a general chat assistant.
Usage
Serve the base with the adapter loaded (vLLM ≥ 0.19 — the base's Qwen3_5 architecture supports
adapters on its tower modules):
Serving notes: use --tool-call-parser qwen3_xml (not hermes — it silently drops tool calls);
serve in eager mode if you hit hybrid-attention graph-capture issues; the linear-attention base keeps a
fixed-size recurrent state, so long contexts cost prefill tokens, not growing KV cache.
License
Apache-2.0, consistent with the base model family.
Citation
bibtex
1@software{swarmdo_a1_2026,
2 title = {SwarmDo-A1: An open, execution-verified multimodal coding system},
3 author = {SwarmDo},
4 year = {2026},
5 url = {https://github.com/SwarmDo/models}
6}
Use cases
Automated bug fixing in Python repositories — generate a patch, then verify it against the project's tests.
Agentic software engineering — a coding agent that edits files, runs commands, and self-checks patches.
Image-to-code / visual coding — turn a chart, plot, or UI screenshot into the code that reproduces it (render-and-compare verified).
Self-hosted / private coding assistant — runs entirely on your own hardware (vLLM, or GGUF via Ollama / LM Studio); no data leaves your machine.
Best-of-N patch generation — sample multiple candidate fixes and select the one that passes execution.
Run it locally (GGUF — Ollama / LM Studio / llama.cpp)
For a one-command local run, use the GGUF build — no account needed:
ollama run hf.co/SwarmDo/SwarmDo-A1-GGUF:Q4_K_M
GGUF repo: SwarmDo/SwarmDo-A1-GGUF (Q4_K_M ~16.5 GB, Q8_0 ~28.6 GB; LM Studio auto-indexes it). The GGUF is text-only; for the full multimodal + execution-verified system, self-host with vLLM as shown above.
FAQ
Is SwarmDo-A1 open source? Yes — Apache-2.0, commercial use permitted. Weights, recipe, and full (including negative) results are public.
Can I run it on my own hardware? Yes. Self-host with vLLM (adapter on Qwen3.6-27B), or run the quantized GGUF locally with Ollama / LM Studio / llama.cpp. Q4_K_M fits a single 24 GB GPU or a 32 GB Mac.
What is it based on? A LoRA/PEFT adapter on Qwen/Qwen3.6-27B — a linear-attention hybrid (gated-DeltaNet) base with a vision tower. Apache-2.0.
Does it really do image-to-code? Yes — it writes code from images (charts/plots/UI) and we verify it by rendering the output and comparing to the target image (an objective, reward-hackable-resistant signal), not by eyeballing.
How is it different from other open coding models? SwarmDo-A1 is a system, not just a checkpoint: a strong open base plus an execution-verified best-of-N harness that runs the project's tests, plus an exec-grounded visual-coding capability. We report paired, held-out, execution-verified deltas — and publish the limitations.
Is it good at SWE-bench? We report relative, paired, held-out, execution-verified wins (e.g. base + execution-verified selection solves 36 vs 24, p = 0.0005), not a single absolute leaderboard number we can't stand behind under paired testing.
What languages? Python-centric agentic software engineering. It is a coding agent, not a general chat assistant.
Topics: open source coding model · local LLM for coding · self-hosted AI coding assistant · agentic SWE-bench model · image to code / chart to code / screenshot to code · execution-verified code generation · Qwen3.6-27B LoRA · Ollama coding model · run a coding LLM locally · Apache-2.0 code model.
