diff --git a/src/ctx_to_lora/modeling/generative_adapter.py b/src/ctx_to_lora/modeling/generative_adapter.py
new file mode 100644
index 0000000..91fdd27
--- /dev/null
+++ b/src/ctx_to_lora/modeling/generative_adapter.py
@@ -0,0 +1,70 @@
+import requests
+import torch
+
+
+def call_generate(
+    input_txt: str,
+    context_txt: str,
+    window_size: int | None = None,
+    max_new_tokens: int | None = None,
+    host: str = "http://127.0.0.1:8989",
+    timeout: int = 120,
+) -> torch.Tensor:
+    """Send the prompt to the API server and return the generated token tensor."""
+    payload: dict[str, object] = {
+        "input_txt": input_txt,
+        "context_txt": context_txt,
+    }
+    if window_size is not None:
+        payload["window_size"] = int(window_size)
+    if max_new_tokens is not None:
+        payload["max_new_tokens"] = int(max_new_tokens)
+
+    response = requests.post(f"{host}/generate", json=payload, timeout=timeout)
+    response.raise_for_status()
+    data = response.json()
+    if "output" not in data:
+        raise ValueError(f"Unexpected response payload: {data}")
+    return torch.tensor([data["output"]])
+
+
+def check_server_health(host: str = "http://127.0.0.1:8989", timeout: int = 60) -> None:
+    """Check if the API server is healthy and responding."""
+    try:
+        response = requests.get(f"{host}/health", timeout=timeout)
+        response.raise_for_status()
+        data = response.json()
+        if data.get("status") != "ok":
+            raise RuntimeError(f"Server is not healthy: {data}")
+    except requests.exceptions.ConnectionError as e:
+        raise RuntimeError(
+            f"Cannot connect to server at {host}. Is the server running?"
+        ) from e
+    except requests.exceptions.Timeout as e:
+        raise RuntimeError(f"Server health check timed out after {timeout}s") from e
+    except requests.exceptions.RequestException as e:
+        raise RuntimeError(f"Server health check failed: {e}") from e
+    print("Server is healthy.")
+
+
+class GenerativeAdapter(torch.nn.Module):
+    def __init__(self, model, tokenizer):
+        super().__init__()
+        self.base_model = model  # placeholder
+        self.tokenizer = tokenizer
+        check_server_health()
+
+    @property
+    def generation_config(self):
+        return self.base_model.generation_config
+
+    def generate(self, *args, **kwargs):
+        ctx_ids = kwargs["ctx_ids"]
+        input_ids = kwargs["input_ids"]
+        assert ctx_ids.shape[0] == 1
+        assert input_ids.shape[0] == 1
+
+        context_txt = self.tokenizer.decode(ctx_ids[0])
+        input_txt = self.tokenizer.decode(input_ids[0])
+        outputs = call_generate(input_txt, context_txt)
+        return outputs