Set LFM2.5 response_template directly (chat template not recognized by add_response_schema)

train_lora.py

@@ -91,6 +91,14 @@ def make_rollout_worker(dataset, tokenizer):
 
 def main():
     tokenizer = AutoTokenizer.from_pretrained(MODEL)
+    # LFM2.5-2.6B's chat_template.jinja doesn't byte-match any template TRL's
+    # add_response_schema recognizes, and the rollout-loop ctor requires a
+    # response parser. TRL ships one purpose-built for LFM2.5's Python-style
+    # tool calls; set it directly. (Only the ctor needs it: in loop-owning mode
+    # the harness, not TRL, parses responses.)
+    from trl.chat_template_utils import lfm2_2_5_template
+
+    tokenizer.response_template = lfm2_2_5_template
     # The trainer dataset is discovered from the env server, not loaded from the
     # Hub: HarborSessionFactory.create() only accepts instructions the server
     # itself serves, and prompt_rows() is its blessed export (one row per task,