Nightly

pyproject.toml

@@ -7,7 +7,7 @@ name = "unsloth"
 dynamic = ["version"]
 description = "2-5X faster LLM finetuning"
 readme = "README.md"
-requires-python = ">=3.9,<3.13"
+requires-python = ">=3.9,<=3.13"
 license = {text = "Apache-2.0"}
 keywords = ["ai", "llm",]
 authors = [
@@ -37,7 +37,7 @@ triton = [
 ]
 
 huggingface = [
-    "unsloth_zoo>=2025.8.3",
+    "unsloth_zoo>=2025.8.4",
     "packaging",
     "tyro",
     "transformers>=4.51.3,!=4.47.0,!=4.52.0,!=4.52.1,!=4.52.2,!=4.52.3,!=4.53.0",
@@ -384,7 +384,7 @@ colab-ampere-torch220 = [
     "flash-attn>=2.6.3",
 ]
 colab-new = [
-    "unsloth_zoo>=2025.8.3",
+    "unsloth_zoo>=2025.8.4",
     "packaging",
     "tyro",
     "transformers>=4.51.3,!=4.47.0,!=4.52.0,!=4.52.1,!=4.52.2,!=4.52.3,!=4.53.0",

unsloth/models/_utils.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-__version__ = "2025.8.4"
+__version__ = "2025.8.5"
 
 __all__ = [
     "SUPPORTS_BFLOAT16",
@@ -58,6 +58,7 @@
     "HAS_CUT_CROSS_ENTROPY",
     "EMPTY_LOGITS",
     "fused_linear_cross_entropy",
+    "unsloth_fused_ce_loss",
     "patch_unsloth_smart_gradient_checkpointing",
     "unpatch_unsloth_smart_gradient_checkpointing",
 
@@ -109,6 +110,7 @@
     HAS_CUT_CROSS_ENTROPY,
     fused_linear_cross_entropy,
     _unsloth_get_batch_samples,
+    unsloth_fused_ce_loss,
 )
 from unsloth_zoo.vision_utils import (
     process_vision_info,
@@ -152,6 +154,41 @@ def __init__(self, text): self.text = text
     def filter(self, x): return not (self.text in x.getMessage())
 pass
 
+# Stop vLLM messages
+if os.environ.get('UNSLOTH_ENABLE_LOGGING', '0') != '1':
+    try:
+        from vllm.worker.worker import logger as vllm_worker_logger
+        vllm_worker_logger.addFilter(HideLoggingMessage("Sleep mode freed"))
+        del vllm_worker_logger
+    except:
+        pass
+    try:
+        from vllm.v1.worker.gpu_worker import logger as vllm_gpu_worker_logger
+        vllm_gpu_worker_logger.addFilter(HideLoggingMessage("Sleep mode freed"))
+        del vllm_gpu_worker_logger
+    except:
+        pass
+    try:
+        from vllm.executor.executor_base import logger as vllm_executor_logger
+        vllm_executor_logger.addFilter(HideLoggingMessage("to fall asleep"))
+        vllm_executor_logger.addFilter(HideLoggingMessage("to wake up"))
+        del vllm_executor_logger
+    except:
+        pass
+    try:
+        from vllm.core.block.prefix_caching_block import logger as vllm_prefix_caching_logger
+        vllm_prefix_caching_logger.addFilter(HideLoggingMessage("reset prefix cache"))
+        del vllm_prefix_caching_logger
+    except:
+        pass
+    try:
+        from vllm.v1.core.block_pool import logger as vllm_block_pool_logger
+        vllm_block_pool_logger.addFilter(HideLoggingMessage("reset prefix cache"))
+        del vllm_block_pool_logger
+    except:
+        pass
+pass
+
 # The speedups for torchdynamo mostly come with GPU Ampere or higher and which is not detected here.
 from transformers.training_args import logger as transformers_training_args_logger
 transformers_training_args_logger.addFilter(HideLoggingMessage("The speedups"))
@@ -224,6 +261,17 @@ def filter(self, x): return not (self.text in x.getMessage())
 except:
     pass
 
+# You passed `quantization_config` or equivalent parameters
+try:
+    warnings.filterwarnings(
+        action = "ignore",
+        message = r".*quantization_config.*",
+        category = UserWarning,
+        append = True,
+    )
+except:
+    pass
+
 # Errors out on
 # Some weights of Gemma3nForConditionalGeneration were not initialized from the model checkpoint
 from transformers.modeling_utils import logger as transformers_logger

unsloth/models/llama.py

@@ -1197,12 +1197,25 @@ def _CausalLM_fast_forward(
                 if self.config.model_type == "falcon_h1":
                     hidden_states = hidden_states * self.config.lm_head_multiplier
 
-                loss = fused_linear_cross_entropy(
-                    hidden_states      = hidden_states,
-                    lm_weight          = lm_head,
-                    labels             = labels,
-                    num_items_in_batch = n_items,
-                    logit_softcapping  = logit_softcapping,
+                # loss = fused_linear_cross_entropy(
+                #     hidden_states      = hidden_states,
+                #     lm_weight          = lm_head,
+                #     labels             = labels,
+                #     num_items_in_batch = n_items,
+                #     logit_softcapping  = logit_softcapping,
+                # )
+                loss = unsloth_fused_ce_loss(
+                    trainer              = None,
+                    hidden_states        = hidden_states,
+                    lm_head_weight       = lm_head,
+                    lm_head_bias         = None,
+                    labels               = labels,
+                    mask                 = None,
+                    n_items              = n_items,
+                    scaling              = getattr(self, "accelerator_scaler", None),
+                    target_gb            = 1,
+                    torch_compile        = True,
+                    logit_softcapping    = logit_softcapping,
                 )
                 if not return_dict:
                     output = (logits,) + outputs[1:]

unsloth/models/loader.py

@@ -111,6 +111,14 @@ def from_pretrained(
         disable_log_stats          = True,
         *args, **kwargs,
     ):
+        # Login to allow private models
+        if token is None: token = get_token()
+        if token is not None:
+            try:
+                from huggingface_hub import login
+                login(token = token)
+            except:
+                pass
         if load_in_8bit or full_finetuning:
             return FastModel.from_pretrained(
                 model_name                 = model_name,
@@ -513,6 +521,13 @@ def from_pretrained(
         *args, **kwargs,
     ):
         if token is None: token = get_token()
+        # Login to allow private models
+        if token is not None:
+            try:
+                from huggingface_hub import login
+                login(token = token)
+            except:
+                pass
         if whisper_language is not None: assert(type(whisper_language) is str)
         if whisper_task is not None: assert(type(whisper_task) is str)
         SUPPORTS_BFLOAT16 = is_bfloat16_supported()
@@ -587,10 +602,12 @@ def from_pretrained(
             if transformers_version < Version("4.53.0"):
                 raise RuntimeError("Unsloth: Gemma 3N only works on transformers >= 4.53.0" + LATEST)
         elif "falcon-h1" in lowered_model_name:
+            # Falcon must use float32 Triton ie TRITON_F32_DEFAULT = 'ieee'
+            # since Mamba kernels error out on using lower precision
             os.environ["UNSLOTH_FORCE_CUSTOM_DTYPE"] = \
                 "float16;torch.float32;torch.float16;"\
-                "if name.endswith(('q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj', 'head')): module.to(torch.float16); "\
-                "os.environ['TRITON_F32_DEFAULT'] = 'ieee';"
+                "if name.endswith(('q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj', 'head')): module.to(torch.float16);"\
+                "os.environ['TRITON_F32_DEFAULT'] = 'ieee'"
         elif "gpt-oss" in lowered_model_name:
             os.environ["UNSLOTH_DISABLE_STATIC_GENERATION"] = "1"
             # CCE fails on Tesla T4

unsloth/models/rl.py

@@ -421,6 +421,20 @@ def _patch_trl_rl_trainers(trainer_file = "grpo_trainer"):
         RLTrainer_post += neftune_check
     pass
 
+    # Add accelerator scaler to model
+    if "model" in call_args:
+        neftune_check = \
+        "if hasattr(self, 'accelerator'):\n"\
+        "    scaler = self.accelerator.scaler\n"\
+        "    current_model = model\n"\
+        "    while hasattr(current_model, 'model'):\n"\
+        "        current_model.accelerator_scaler = scaler\n"\
+        "        current_model = current_model.model\n"\
+        "    current_model.accelerator_scaler = scaler\n"\
+        "pass\n"
+        RLTrainer_post += neftune_check
+    pass
+
     # Edit optional metrics
     other_metrics_processor = ""
     if trainer_file in RL_METRICS_CHANGES: