Safety and Claims

This page states plainly what this model does and does not claim, so you can weigh it without reading between the lines.

What we claim, and the evidence

The artifact is what it says it is. The build is retraceable hash by hash: corpus content, packed training shards, adapter checkpoint, the LoRA GGUF, and the final quantized GGUF are each recorded in provenance.json, and checksums.sha256 ties the chain to the exact file you downloaded.
Reruns are reproducible. Served at temperature 0 with a fixed seed, generations are byte-identical across runs (recorded generation hash prefix 403b2e8b21df9f55).

What we do not claim

No capability uplift over the base model. We have not measured one, so we do not assert one.
No benchmark standing at all, yet. Unlike the 14B, this model carries no executed benchmark artifacts. HumanEval, MBPP, hard-set, and similar suites have not been run. See BENCHMARKS.md.
No safety tuning beyond the base model. Refusal behavior, bias, and content boundaries follow Qwen2.5-Coder-32B-Instruct. We have not measured or modified them, so treat them as inherited and unaudited here.

Sensible boundaries for use

Treat generated code the way you would treat code from any assistant: run your tests, review before shipping, and never execute generated code against production systems unreviewed.
The model runs entirely locally and sends nothing anywhere. Network behavior is a property of the runtime you choose (Ollama, llama.cpp), not the weights.
Keep secrets out of prompts as a habit. Nothing in this release requires secrets, keys, or private files to use.

If you find a problem

Open an issue on the model repo with the prompt, the runtime and version, and the observed output. A reproducible report at temperature 0 is the fastest path to a fix, because we can replay it exactly.