--- license: mit base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct library_name: transformers tags: - code - qwen - guidance - sidecar - orchestration --- # Qwen Coder Guidance Sidecar This bundle describes a lightweight local coding guidance component for larger model orchestration. It is upload-ready as a Hugging Face model/runtime adapter package. It does not include training data. It uses `Qwen/Qwen2.5-Coder-1.5B-Instruct` as a fast sidecar that returns compact JSON guidance instead of final user-facing prose. The larger model can call it before or during implementation to get route decisions, repo facts, patch constraints, risks, and tests. ## Output Contract ```json { "route": "plan|patch|critique|test|fix", "confidence": 0.0, "useful_context": ["short facts the outer model should keep"], "plan": ["ordered implementation steps"], "patch_rules": ["constraints for the patch"], "risks": ["likely bugs or failure modes"], "tests": ["commands or cases to run"], "next_action": "one concise next action" } ``` ## Runtime - Default model: `Qwen/Qwen2.5-Coder-1.5B-Instruct` - Included local model path: `base_model/` - Recommended inference: 4-bit NF4, bf16 compute, CUDA - Cache behavior: disk cache by request hash - Memory behavior: unload after call by default - Package entrypoint: `guidance_sidecar.GuidanceEngine` - Deterministic tools: route hint, context stats, risk scan, test suggestions, patch rules, simple calculations, Python syntax probe ## Use Run directly from the uploaded/downloaded folder: ```bash python run_guidance.py --task "Plan a safe config-loader refactor" --context "Python package with tests." ``` Or use the included modeling wrapper: ```python from modeling_guidance_sidecar import GuidanceSidecar guide = GuidanceSidecar("./qwen-coder-guidance-sidecar") result = guide("Refactor the config loader safely", context="Python package with tests.") ``` The repo also includes the full `guidance_sidecar/` runtime package, so it can be used without installing the source project separately. ## Larger-Model Route 1. Outer model gathers repo context. 2. Sidecar runs deterministic tools and Qwen guidance. 3. Sidecar returns compact JSON with `tool_signals`. 4. Outer model treats that JSON as internal control data. 5. Outer model patches code, runs tests, and may call sidecar again for critique/fix routing. The sidecar is designed to help a larger model, not replace it.