diff --git a/unsloth/models/rl.py b/unsloth/models/rl.py
index 22189f459c..88aeeda8a1 100644
--- a/unsloth/models/rl.py
+++ b/unsloth/models/rl.py
@@ -199,15 +199,15 @@ def PatchRL(FastLanguageModel):
     unwrap = "unwrap_model_for_generation"
     for trainer in trainers:
         try:
-            current_trainer = eval(f"trl.trainer.{trainer}")
+            current_trainer = getattr(trl.trainer, trainer)
         except:
             continue
         if hasattr(current_trainer, unwrap):
             try:
-                exec(f"trl.trainer.{trainer}.{unwrap} = unsloth_{unwrap}")
+                setattr(current_trainer, unwrap, unsloth_unwrap_model_for_generation)
             except:
                 continue
-    exec(f"Trainer.prediction_step=unsloth_prediction_step")
+    Trainer.prediction_step = unsloth_prediction_step
 
 
 selective_log_softmax = RL_REPLACEMENTS["selective_log_softmax"]
@@ -234,6 +234,10 @@ from transformers.training_args import ParallelMode
 # Also patches W&B since multiple runs must use wandb.finish()
 import functools
 from types import MethodType
+try:
+    from unsloth_zoo.gradient_checkpointing import reset_unsloth_gradient_checkpointing_buffers
+except:
+    def reset_unsloth_gradient_checkpointing_buffers(): pass
 def prepare_for_training_mode(f):
     @functools.wraps(f)
     def wrapper(self, *args, **kwargs):
@@ -244,6 +248,11 @@ def prepare_for_training_mode(f):
         # Return inference mode
         if hasattr(self, 'model') and hasattr(self.model, "for_inference"):
             self.model.for_inference()
+        # Reset gradient checkpointing buffers to free memory while staying ready for next run
+        try:
+            reset_unsloth_gradient_checkpointing_buffers()
+        except:
+            pass
         # Patch W&B to enable logging on future runs, otherwise it'll overwrite the first run
         try:
             import wandb
@@ -817,7 +826,7 @@ def _patch_trl_rl_trainers(trainer_file = "grpo_trainer"):
         num_proc_check = (
             "if dataset_num_proc is None:\n"
             "    import psutil\n"
-            "    dataset_num_proc = min(max(psutil.cpu_count()+4, 2), 64)\n"
+            "    dataset_num_proc = min(max((psutil.cpu_count() or 1)+4, 2), 64)\n"
             "    memory_gb_left = psutil.virtual_memory().available / (1024**3)\n"
             "    if   memory_gb_left <=  4: dataset_num_proc = 1 # Too risky, so set to 1\n"
             "    elif memory_gb_left <=  6: dataset_num_proc = min(2, dataset_num_proc)\n"
@@ -994,10 +1003,10 @@ def _patch_trl_rl_trainers(trainer_file = "grpo_trainer"):
 
         # Temporary patch _is_vlm to False
         # as of 0.22 it only exists in sfttrainer
-        oriignal_is_vlm_text = "self._is_vlm = True"
+        original_is_vlm_text = "self._is_vlm = True"
         new_is_vlm_text = "self._is_vlm = False"
         RLTrainer_source = RLTrainer_source.replace(
-            oriignal_is_vlm_text, new_is_vlm_text
+            original_is_vlm_text, new_is_vlm_text
         )
 
     # Remove multiple doc strings
diff --git a/unsloth/save.py b/unsloth/save.py
index 714df7682a..071e032c53 100644
--- a/unsloth/save.py
+++ b/unsloth/save.py
@@ -879,12 +879,12 @@ def install_llama_cpp_make_non_blocking():
     IS_CMAKE = False
     if check == 0:
         # Uses old MAKE
-        n_jobs = max(int(psutil.cpu_count() * 1.5), 1)
+        n_jobs = max(int((psutil.cpu_count() or 1) * 1.5), 1)
         full_command = ["make", "all", "-j" + str(n_jobs), "-C", "llama.cpp"]
         IS_CMAKE = False
     else:
         # Uses new CMAKE
-        n_jobs = max(int(psutil.cpu_count()), 1)  # Use less CPUs since 1.5x faster
+        n_jobs = max(int(psutil.cpu_count() or 1), 1)  # Use less CPUs since 1.5x faster
         check = os.system(
             f"cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=OFF {CURL_FLAG}"
         )
@@ -994,13 +994,13 @@ def install_llama_cpp_old(version = -10):
     # Try using MAKE
     commands = [
         "make clean -C llama.cpp",
-        f"make all -j{psutil.cpu_count()*2} -C llama.cpp",
+        f"make all -j{(psutil.cpu_count() or 1)*2} -C llama.cpp",
     ]
     if try_execute(commands) == "CMAKE":
         # Instead use CMAKE
         commands = [
             f"cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=OFF {CURL_FLAG}",
-            f"cmake --build llama.cpp/build --config Release -j{psutil.cpu_count()*2} --clean-first --target {' '.join(LLAMA_CPP_TARGETS)}",
+            f"cmake --build llama.cpp/build --config Release -j{(psutil.cpu_count() or 1)*2} --clean-first --target {' '.join(LLAMA_CPP_TARGETS)}",
             "cp llama.cpp/build/bin/llama-* llama.cpp",
             "rm -rf llama.cpp/build",
         ]
@@ -1040,14 +1040,14 @@ def install_llama_cpp_blocking(use_cuda = False):
         "make clean -C llama.cpp",
         # https://github.com/ggerganov/llama.cpp/issues/7062
         # Weirdly GPU conversion for GGUF breaks??
-        # f"{use_cuda} make all -j{psutil.cpu_count()*2} -C llama.cpp",
-        f"make all -j{psutil.cpu_count()*2} -C llama.cpp",
+        # f"{use_cuda} make all -j{(psutil.cpu_count() or 1)*2} -C llama.cpp",
+        f"make all -j{(psutil.cpu_count() or 1)*2} -C llama.cpp",
     ]
     if try_execute(commands) == "CMAKE":
         # Instead use CMAKE
         commands = [
             f"cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=OFF {CURL_FLAG}",
-            f"cmake --build llama.cpp/build --config Release -j{psutil.cpu_count()*2} --clean-first --target {' '.join(LLAMA_CPP_TARGETS)}",
+            f"cmake --build llama.cpp/build --config Release -j{(psutil.cpu_count() or 1)*2} --clean-first --target {' '.join(LLAMA_CPP_TARGETS)}",
             "cp llama.cpp/build/bin/llama-* llama.cpp",
             "rm -rf llama.cpp/build",
         ]