Qwen 2.5

unsloth/kernels/cross_entropy_loss.py

@@ -15,7 +15,7 @@
 import triton
 import triton.language as tl
 import torch
-from .utils import calculate_settings, MAX_FUSED_SIZE, triton_tanh
+from .utils import calculate_settings, MAX_FUSED_SIZE, triton_tanh, triton_cast
 from transformers.models.llama.modeling_llama import logger
 from packaging.version import Version
 
@@ -64,7 +64,7 @@ def _cross_entropy_forward(
         This ensures exp(x - max(x))'s maximum is 1 as exp(0) = 1.
     """
     row_idx = tl.program_id(0)
-    logits_ptr    += row_idx * tl.cast(logits_row_stride, tl.int64)
+    logits_ptr    += row_idx * triton_cast(logits_row_stride, tl.int64)
     loss_ptr      += row_idx
     logsumexp_ptr += row_idx
     labels_ptr    += row_idx
@@ -142,7 +142,7 @@ def _chunked_cross_entropy_forward(
     """
     row_idx   = tl.program_id(0)
     chunk_idx = tl.program_id(1)
-    logits_ptr    += row_idx * tl.cast(logits_row_stride, tl.int64)
+    logits_ptr    += row_idx * triton_cast(logits_row_stride, tl.int64)
     loss_ptr      += row_idx
     logsumexp_ptr += row_idx * N_CHUNKS + chunk_idx
     labels_ptr    += row_idx
@@ -216,7 +216,7 @@ def _cross_entropy_backward(
     row_idx   = tl.program_id(0)
     block_idx = tl.program_id(1)
 
-    logits_ptr += row_idx * tl.cast(logits_row_stride, tl.int64)
+    logits_ptr += row_idx * triton_cast(logits_row_stride, tl.int64)
     dloss_ptr  += row_idx *  dloss_row_stride
     col_offsets = block_idx*BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
     mask = col_offsets < VOCAB_SIZE
@@ -400,6 +400,6 @@ def fast_cross_entropy_loss(
 pass
 
 # Patch CE Losses in transformers
-def patch_loss_functions():
-    _patch_loss_functions(fast_cross_entropy_loss)
+def patch_loss_functions(torch_compile = True):
+    _patch_loss_functions(fast_cross_entropy_loss, torch_compile = torch_compile)
 pass

unsloth/kernels/utils.py

@@ -31,12 +31,18 @@
 # tl.math.tanh now is libdevice.tanh
 from packaging.version import Version
 import triton
+import triton.language as tl
 if Version(triton.__version__) >= Version("3.0.0"):
     from triton.language.extra import libdevice
     triton_tanh = libdevice.tanh
+    triton_cast = tl.cast
 else:
-    import triton.language as tl
     triton_tanh = tl.math.tanh
+    # No casting in old Triton versions
+    @triton.jit
+    def triton_cast(x, dtype):
+        return x.to(dtype)
+    pass
 pass
 
 

unsloth/models/_utils.py

@@ -104,14 +104,22 @@
 # Ignore logging messages
 class HideLoggingMessage(logging.Filter):
     def __init__(self, text): self.text = text
-    def filter(self, x): return not x.getMessage().startswith(self.text)
+    def filter(self, x): return not (self.text in x.getMessage())
 pass
 
 # The speedups for torchdynamo mostly come wih 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"))
 del transformers_training_args_logger
 
+# Using the default loss: `ForCausalLMLoss`.
+try:
+    from transformers.modeling_utils import logger as transformers_modeling_utils_logger
+    transformers_modeling_utils_logger.addFilter(HideLoggingMessage("ForCausalLMLoss"))
+    del transformers_modeling_utils_logger
+except:
+    pass
+
 # =============================================
 
 # =============================================

unsloth/models/llama.py

@@ -2317,7 +2317,8 @@ def patch_peft_model(
                     layer.self_attn.apply_qkv = apply_lora_qkv
                     n_qkv += 1
                 else:
-                    if model_type != "qwen2":
+                    if model_type == "qwen2": n_qkv += 1
+                    else:
                         logger.warning_once(
                             "Not an error, but Unsloth cannot patch Attention layers with our manual autograd engine since either LoRA adapters\n"\
                             "are not enabled or a bias term (like in Qwen) is used."

unsloth/models/mapper.py

@@ -384,22 +384,54 @@
         "unsloth/Qwen2.5-Math-72B-Instruct",
         "Qwen/Qwen2.5-Math-72B-Instruct",
     ),
+    "unsloth/Qwen2.5-Coder-0.5B-bnb-4bit" : (
+        "unsloth/Qwen2.5-Coder-0.5B",
+        "Qwen/Qwen2.5-Coder-0.5B",
+    ),
     "unsloth/Qwen2.5-Coder-1.5B-bnb-4bit" : (
         "unsloth/Qwen2.5-Coder-1.5B",
         "Qwen/Qwen2.5-Coder-1.5B",
     ),
+    "unsloth/Qwen2.5-Coder-3B-bnb-4bit" : (
+        "unsloth/Qwen2.5-Coder-3B",
+        "Qwen/Qwen2.5-Coder-3B",
+    ),
     "unsloth/Qwen2.5-Coder-7B-bnb-4bit" : (
         "unsloth/Qwen2.5-Coder-7B",
         "Qwen/Qwen2.5-Coder-7B",
     ),
+    "unsloth/Qwen2.5-Coder-14B-bnb-4bit" : (
+        "unsloth/Qwen2.5-Coder-14B",
+        "Qwen/Qwen2.5-Coder-14B",
+    ),
+    "unsloth/Qwen2.5-Coder-32B-bnb-4bit" : (
+        "unsloth/Qwen2.5-Coder-32B",
+        "Qwen/Qwen2.5-Coder-32B",
+    ),
+    "unsloth/Qwen2.5-Coder-0.5B-Instruct-bnb-4bit" : (
+        "unsloth/Qwen2.5-Coder-Instruct-0.5B",
+        "Qwen/Qwen2.5-Coder-Instruct-0.5B",
+    ),
     "unsloth/Qwen2.5-Coder-1.5B-Instruct-bnb-4bit" : (
         "unsloth/Qwen2.5-Coder-Instruct-1.5B",
         "Qwen/Qwen2.5-Coder-Instruct-1.5B",
     ),
+    "unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit" : (
+        "unsloth/Qwen2.5-Coder-3B-Instruct",
+        "Qwen/Qwen2.5-Coder-3B-Instruct",
+    ),
     "unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit" : (
         "unsloth/Qwen2.5-Coder-7B-Instruct",
         "Qwen/Qwen2.5-Coder-7B-Instruct",
     ),
+    "unsloth/Qwen2.5-Coder-14B-Instruct-bnb-4bit" : (
+        "unsloth/Qwen2.5-Coder-14B-Instruct",
+        "Qwen/Qwen2.5-Coder-14B-Instruct",
+    ),
+    "unsloth/Qwen2.5-Coder-32B-Instruct-bnb-4bit" : (
+        "unsloth/Qwen2.5-Coder-32B-Instruct",
+        "Qwen/Qwen2.5-Coder-32B-Instruct",
+    ),
     "unsloth/Llama-3.2-1B-bnb-4bit" : (
         "unsloth/Llama-3.2-1B",
         "meta-llama/Llama-3.2-1B",

unsloth/tokenizer_utils.py

@@ -588,15 +588,21 @@ def load_correct_tokenizer(
 def _fix_chat_template(chat_template):
     endfor = "{% endfor %}"
     where = chat_template.find(endfor)
-    if where == -1: return chat_template
+    if where == -1:
+        endfor = "{%- endfor %}"
+        where = chat_template.find(endfor)
+    if where == -1:
+        return chat_template
 
     after_endfor = chat_template[where + len(endfor):]
 
-    if "{% if" not in after_endfor and "{% set " not in after_endfor and \
+    dash = "-" if endfor.startswith("{%-") else ""
+
+    if "{%" + dash + " if" not in after_endfor and "{%" + dash + " set " not in after_endfor and \
         after_endfor.startswith("{{") and after_endfor.endswith("}}") and \
         after_endfor.count("{{") == 1 and after_endfor.count("}}") == 1:
 
-        after_endfor = "{% if add_generation_prompt %}" + after_endfor + "{% endif %}"
+        after_endfor = "{%" + dash + " if add_generation_prompt %}" + after_endfor + endfor
 
         chat_template = chat_template[:where + len(endfor)] + after_endfor
     pass
@@ -643,10 +649,12 @@ def fix_chat_template(tokenizer):
 
     if no == yes:
         # SAME?! That's not good! We check for add_generation_prompt
-        if "{% if add_generation_prompt %}" not in chat_template:
+        if   "{% if add_generation_prompt %}" not in chat_template and \
+            "{%- if add_generation_prompt %}" not in chat_template:
             # Try fixing it by adding it
             new_chat_template = _fix_chat_template(chat_template)
-            if "{% if add_generation_prompt %}" not in new_chat_template:
+            if   "{% if add_generation_prompt %}" not in new_chat_template and \
+                "{%- if add_generation_prompt %}" not in new_chat_template:
                 raise RuntimeError(
                     f"Unsloth: The tokenizer `{tokenizer.name_or_path}`\n"\
                     "does not have a {% if add_generation_prompt %} for generation purposes.\n"\
@@ -1001,13 +1009,14 @@ def patch_sft_trainer_tokenizer():
         # Also DPO weirdly tokenizes non numeric columns? Delete them!
         check_text += \
         "\n"\
-        "column_names = set(self.train_dataset.column_names)\n"\
-        "check = ['chosen', 'rejected', 'prompt', 'chosen_input_ids', 'chosen_attention_mask',\n"\
-        " 'chosen_labels', 'rejected_input_ids', 'rejected_attention_mask', 'rejected_labels',\n"\
-        " 'prompt_input_ids', 'prompt_attention_mask']\n"\
-        "if all(x in column_names for x in check):\n"\
-        "    self.train_dataset = self.train_dataset.remove_columns(['chosen', 'rejected', 'prompt'])\n"\
-        "del check, column_names\n"\
+        "if hasattr(self.train_dataset, 'column_names'):\n"\
+        "    column_names = set(self.train_dataset.column_names)\n"\
+        "    check = ['chosen', 'rejected', 'prompt', 'chosen_input_ids', 'chosen_attention_mask',\n"\
+        "        'chosen_labels', 'rejected_input_ids', 'rejected_attention_mask', 'rejected_labels',\n"\
+        "        'prompt_input_ids', 'prompt_attention_mask']\n"\
+        "    if all(x in column_names for x in check):\n"\
+        "        self.train_dataset = self.train_dataset.remove_columns(['chosen', 'rejected', 'prompt'])\n"\
+        "    del check, column_names\n"\
         "\n"
 
         check_text = check_text.split("\n")