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README.md

@@ -41,6 +41,16 @@ This is the **`-term`** sibling of [**GLM-5.2-NVFP4-REAP-504B**](https://hugging
 
 GLM-5.2 exposes **two** reasoning levels: **`high`** and **`max`**. The chat template **defaults to `max`** and treats anything else as `max`; there is no low/medium/minimal. Pass `reasoning_effort: "high"` (e.g. via `chat_template_kwargs`) for shorter, faster thinking; leave it default for `max`. This is a **heavy thinker** — at `max` it can reason for tens of thousands of tokens before answering, so give it a generous `max_tokens` (≈80–120k) or it will hit the cap mid-thought.
 
+## What it's calibrated on (and what that means)
+
+Calibrated **narrow, on purpose:** code + tool-calling/agentic data (evol-codealpaca, Magicoder, xLAM function-calling, SWE-smith) **plus the model's own terminating reasoning traces**. We did **not** calibrate on broad/general, multilingual, or long-document data.
+
+- **Stronger at:** coding, tool use, and ending its own reasoning (the termination traces are the whole point of this variant).
+- **Weaker at (expected):** general knowledge, other languages, niche domains — those experts scored low on a code-heavy calibration, so they got dropped.
+- **Long context still works** — that lives in the attention, which isn't pruned (an internal 177k-token task scores 30/30); we just didn't add long-document calibration.
+
+Want a broad general-purpose model instead? Calibrate on a wider mix (general + multilingual + long-context).
+
 ## Honest limitations / which sibling to pick — READ THIS
 
 - **Not A/B-validated** against the code-calibrated `-504B`. Whether the trace-recalibration actually *helps your workload* is unmeasured; for pure coding the `-504B` may be more decisive/less verbose. **Benchmark both on your own tasks.**