Add helpful error messages for fast_generate when fast_inference=False

When users load a model with fast_inference=False but then try to use
vLLM-style arguments with fast_generate, they previously got confusing
errors. This adds a wrapper that detects common mistakes and provides
helpful guidance:

- Using sampling_params: explains to use HF generate args instead
- Using lora_request: explains LoRA weights are already merged
- Passing text strings: shows how to tokenize input first

Changes:
- Add make_fast_generate_wrapper to _utils.py
- Apply wrapper in llama.py when fast_inference=False
- Apply wrapper in vision.py when fast_inference=False

unsloth/models/_utils.py

@@ -73,6 +73,7 @@ __all__ = [
     "verify_fp8_support_if_applicable",
     "_get_inference_mode_context_manager",
     "hf_login",
+    "make_fast_generate_wrapper",
 ]
 
 import torch
@@ -2378,3 +2379,58 @@ def hf_login(token: Optional[str] = None) -> Optional[str]:
     except Exception as e:
         logger.info(f"Failed to login to huggingface using token with error: {e}")
     return token
+
+
+def make_fast_generate_wrapper(original_generate):
+    """
+    Creates a wrapper around model.generate that checks for incorrect
+    vLLM-style usage when fast_inference=False.
+    """
+    @functools.wraps(original_generate)
+    def _fast_generate_wrapper(*args, **kwargs):
+        # Check for vLLM-specific arguments
+        if "sampling_params" in kwargs:
+            raise ValueError(
+                "Unsloth: `sampling_params` is only supported when `fast_inference=True` (vLLM). "
+                "Since `fast_inference=False`, use HuggingFace generate arguments instead:\n"
+                "  model.fast_generate(**tokens.to('cuda'), max_new_tokens=64, temperature=1.0, top_p=0.95)"
+            )
+
+        if "lora_request" in kwargs:
+            raise ValueError(
+                "Unsloth: `lora_request` is only supported when `fast_inference=True` (vLLM). "
+                "Since `fast_inference=False`, LoRA weights are already merged into the model."
+            )
+
+        # Check if first positional argument is a string or list of strings
+        if len(args) > 0:
+            first_arg = args[0]
+            is_string_input = False
+
+            if isinstance(first_arg, str):
+                is_string_input = True
+            elif isinstance(first_arg, (list, tuple)) and len(first_arg) > 0:
+                if isinstance(first_arg[0], str):
+                    is_string_input = True
+
+            if is_string_input:
+                raise ValueError(
+                    "Unsloth: Passing text strings to `fast_generate` is only supported "
+                    "when `fast_inference=True` (vLLM). Since `fast_inference=False`, you must "
+                    "tokenize the input first:\n\n"
+                    "  messages = tokenizer.apply_chat_template(\n"
+                    "      [{\"role\": \"user\", \"content\": \"Your prompt here\"}],\n"
+                    "      tokenize=True, add_generation_prompt=True,\n"
+                    "      return_tensors=\"pt\", return_dict=True\n"
+                    "  )\n"
+                    "  output = model.fast_generate(\n"
+                    "      **messages.to('cuda'),\n"
+                    "      max_new_tokens=64,\n"
+                    "      temperature=1.0,\n"
+                    "  )"
+                )
+
+        # Call original generate
+        return original_generate(*args, **kwargs)
+
+    return _fast_generate_wrapper

unsloth/models/llama.py

@@ -2326,7 +2326,7 @@ class FastLlamaModel:
                 attn_implementation = "eager",
                 **kwargs,
             )
-            model.fast_generate = model.generate
+            model.fast_generate = make_fast_generate_wrapper(model.generate)
             model.fast_generate_batches = None
         else:
             from unsloth_zoo.vllm_utils import (

unsloth/models/vision.py

@@ -673,7 +673,7 @@ class FastBaseModel:
                 **kwargs,
             )
             if hasattr(model, "generate"):
-                model.fast_generate = model.generate
+                model.fast_generate = make_fast_generate_wrapper(model.generate)
                 model.fast_generate_batches = error_out_no_vllm
             if offload_embedding:
                 if bool(