diff --git a/train/to_qwen.py b/train/to_qwen.py
new file mode 100644
index 0000000..02512a8
--- /dev/null
+++ b/train/to_qwen.py
@@ -0,0 +1,108 @@
+"""Convert accepted trajectories to the Qwen 3.8 chat template (thinking off, Qwen tool call format).
+
+  train/.venv/bin/python train/to_qwen.py runs/traj/accepted.jsonl runs/traj/qwen.jsonl
+
+Output per line: id, text (full rendered conversation), assistant_spans ([start, end] character offsets of the
+parts the model must learn: after "<think>\\n\\n</think>\\n\\n" up to and with <|im_end|>), n_tokens, messages,
+tools, metadata. Checks per record (a failing record is written to <out>.rejected.jsonl with the reason):
+  - every tool call parses back from the rendered text with the same name and parameter values;
+  - the text starts like the generation prompt of the same messages (so inference and training match);
+  - no "</parameter>" or "</tool_call>" inside a parameter value (would break the Qwen format).
+"""
+import json
+import os
+import re
+import statistics
+import sys
+
+from transformers import AutoTokenizer
+
+MODEL_DIR = os.path.expanduser("~/models/Qwen3.8-27B-4bit")
+HEAD = "<|im_start|>assistant\n<think>\n\n</think>\n\n"
+CALL = re.compile(r"<tool_call>\n<function=([^>\n]+)>\n(.*?)</function>\n</tool_call>", re.S)
+PARAM = re.compile(r"<parameter=([^>\n]+)>\n(.*?)\n</parameter>\n", re.S)
+
+
+def to_template_messages(messages):
+    out = []
+    for m in messages:
+        m = {k: v for k, v in m.items() if k in ("role", "content", "tool_calls", "tool_call_id")}
+        if m["role"] == "assistant":
+            m["content"] = m.get("content") or ""
+            calls = []
+            for c in m.get("tool_calls") or []:
+                a = c["function"].get("arguments") or "{}"
+                a = json.loads(a) if isinstance(a, str) else a
+                calls.append({"id": c.get("id", ""), "type": "function",
+                              "function": {"name": c["function"]["name"], "arguments": a}})
+            if calls:
+                m["tool_calls"] = calls
+            else:
+                m.pop("tool_calls", None)
+        out.append(m)
+    return out
+
+
+def value_text(v):
+    return v if isinstance(v, str) else json.dumps(v)
+
+
+def check_calls(messages, text):
+    """Rendered tool calls == original calls (name and parameter values), in order."""
+    want = [(c["function"]["name"], {k: value_text(v) for k, v in c["function"]["arguments"].items()})
+            for m in messages if m["role"] == "assistant" for c in m.get("tool_calls") or []]
+    body_text = text[text.find("<|im_end|>"):]  # skip the system turn: its tool format example looks like a call
+    got = [(n, {k: v for k, v in PARAM.findall(body)}) for n, body in CALL.findall(body_text)]
+    return want == got
+
+
+def spans(text):
+    out, pos = [], 0
+    while True:
+        i = text.find(HEAD, pos)
+        if i < 0:
+            return out
+        start = i + len(HEAD)
+        end = text.find("<|im_end|>", start) + len("<|im_end|>")
+        out.append([start, end])
+        pos = end
+
+
+def main():
+    src, dst = sys.argv[1], sys.argv[2]
+    tok = AutoTokenizer.from_pretrained(MODEL_DIR)
+    lengths, bad = [], 0
+    with open(dst, "w") as f, open(dst + ".rejected.jsonl", "w") as rj:
+        for line in open(src):
+            rec = json.loads(line)
+            msgs = to_template_messages(rec["messages"])
+            why = []
+            if any(("</parameter>" in value_text(v) or "</tool_call>" in value_text(v))
+                   for m in msgs for c in m.get("tool_calls") or [] for v in c["function"]["arguments"].values()):
+                why.append("tag_in_parameter")
+            text = tok.apply_chat_template(msgs, tools=rec["tools"], tokenize=False, enable_thinking=False)
+            prefix = tok.apply_chat_template(msgs[:2], tools=rec["tools"], tokenize=False,
+                                             add_generation_prompt=True, enable_thinking=False)
+            if not text.startswith(prefix):
+                why.append("prompt_prefix_differs")
+            if not check_calls(msgs, text):
+                why.append("tool_call_roundtrip")
+            n = len(tok(text, add_special_tokens=False)["input_ids"])
+            if why:
+                bad += 1
+                rj.write(json.dumps({"id": rec["id"], "why": why}) + "\n")
+                continue
+            lengths.append(n)
+            f.write(json.dumps({"id": rec["id"], "task": rec["task"], "teacher": rec["teacher"],
+                                "metadata": rec["metadata"], "repair": rec.get("repair"),
+                                "text": text, "assistant_spans": spans(text), "n_tokens": n,
+                                "messages": msgs, "tools": rec["tools"]}) + "\n")
+    if lengths:
+        print(f"{len(lengths)} converted, {bad} rejected; tokens min {min(lengths)} median "
+              f"{int(statistics.median(lengths))} max {max(lengths)}")
+    else:
+        print("nothing converted;", bad, "rejected")
+
+
+if __name__ == "__main__":
+    main()