[model] update kt code (#9406)

README.md

@@ -332,6 +332,8 @@ Choose your path:
 > [!NOTE]
 > For the "base" models, the `template` argument can be chosen from `default`, `alpaca`, `vicuna` etc. But make sure to use the **corresponding template** for the "instruct/chat" models.
 >
+> If the model has both reasoning and non-reasoning versions, please use the `_nothink` suffix to distinguish between them. For example, `qwen3` and `qwen3_nothink`.
+>
 > Remember to use the **SAME** template in training and inference.
 >
 > \*: You should install the `transformers` from main branch and use `DISABLE_VERSION_CHECK=1` to skip version check.

README_zh.md

@@ -334,6 +334,8 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 > [!NOTE]
 > 对于所有“基座”（Base）模型，`template` 参数可以是 `default`, `alpaca`, `vicuna` 等任意值。但“对话”（Instruct/Chat）模型请务必使用**对应的模板**。
 >
+> 如果模型有推理 / 非推理两个版本，请使用 `_nothink` 后缀来区分不同的模板。例如 `qwen3` 和 `qwen3_nothink`。
+>
 > 请务必在训练和推理时采用**完全一致**的模板。
 >
 > \*：您需要从 main 分支安装 `transformers` 并使用 `DISABLE_VERSION_CHECK=1` 来跳过版本检查。

examples/inference/deepseek2_lora_sft_kt.yaml

@@ -7,4 +7,4 @@ trust_remote_code: true
 use_kt: true # use KTransformers as LoRA sft backend to inference
 kt_optimize_rule: examples/kt_optimize_rules/DeepSeek-V2-Lite-Chat-sft-amx.yaml
 cpu_infer: 32
-chunk_size: 8192
+chunk_size: 8192

examples/inference/deepseek3_kt.yaml

@@ -6,4 +6,4 @@ trust_remote_code: true
 use_kt: true # use KTransformers as LoRA sft backend to inference
 kt_optimize_rule: examples/kt_optimize_rules/DeepSeek-V3-Chat-sft-amx-multi-gpu.yaml
 cpu_infer: 32
-chunk_size: 8192
+chunk_size: 8192

examples/inference/deepseek3_lora_sft_kt.yaml

@@ -7,4 +7,4 @@ trust_remote_code: true
 use_kt: true # use KTransformers as LoRA sft backend to inference
 kt_optimize_rule: examples/kt_optimize_rules/DeepSeek-V3-Chat-sft-amx-multi-gpu.yaml
 cpu_infer: 32
-chunk_size: 8192
+chunk_size: 8192

examples/kt_optimize_rules/DeepSeek-V2-Chat-sft-amx.yaml

@@ -6,7 +6,7 @@
       generate_device: "cuda"
       prefill_device: "cuda"
 - match:
-    name: "^model\\.layers\\.(?!.*self_attn\\.kv_b_proj).*$"  # regular expression 
+    name: "^model\\.layers\\.(?!.*self_attn\\.kv_b_proj).*$"  # regular expression
     class: torch.nn.Linear  # only match modules matching name and class simultaneously
   replace:
     class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
@@ -66,4 +66,4 @@
     class: "default"
     kwargs:
       generate_device: "cpu"
-      prefill_device: "cpu"
+      prefill_device: "cpu"

examples/kt_optimize_rules/DeepSeek-V2-Chat.yaml

@@ -6,7 +6,7 @@
       generate_device: "cuda"
       prefill_device: "cuda"
 - match:
-    name: "^model\\.layers\\.(?!.*self_attn\\.kv_b_proj).*$"  # regular expression 
+    name: "^model\\.layers\\.(?!.*self_attn\\.kv_b_proj).*$"  # regular expression
     class: torch.nn.Linear  # only match modules matching name and class simultaneously
   replace:
     class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
@@ -65,4 +65,4 @@
     class: "default"
     kwargs:
       generate_device: "cpu"
-      prefill_device: "cpu"
+      prefill_device: "cpu"

examples/kt_optimize_rules/DeepSeek-V2-Lite-Chat-sft-amx-multi-gpu.yaml

@@ -24,7 +24,7 @@
       prefill_device: "cuda:1"
 
 - match:
-    name: "^model\\.layers\\.(0|[1-9])\\.(?!.*self_attn\\.kv_b_proj).*$"  # regular expression 
+    name: "^model\\.layers\\.(0|[1-9])\\.(?!.*self_attn\\.kv_b_proj).*$"  # regular expression
     class: torch.nn.Linear  # only match modules matching name and class simultaneously
   replace:
     class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
@@ -35,7 +35,7 @@
       prefill_op: "KLinearTorch"
 
 - match:
-    name: "^model\\.layers\\.([12][0-9])\\.(?!.*self_attn\\.kv_b_proj).*$"  # regular expression 
+    name: "^model\\.layers\\.([12][0-9])\\.(?!.*self_attn\\.kv_b_proj).*$"  # regular expression
     class: torch.nn.Linear  # only match modules matching name and class simultaneously
   replace:
     class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
@@ -44,7 +44,7 @@
       prefill_device: "cuda:1"
       generate_op: "KLinearTorch"
       prefill_op: "KLinearTorch"
-  
+
 - match:
     name: "^model\\.layers\\.(0|[1-9])\\.mlp$"
     class: ktransformers.models.modeling_deepseek.DeepseekV2MoE
@@ -108,7 +108,7 @@
     class: "ktransformers.operators.models.KDeepseekV2Model"
     kwargs:
       per_layer_prefill_intput_threshold: 0 # 0 is close layer wise prefill
-      transfer_map: 
+      transfer_map:
         10: "cuda:1"
 
 - match:

examples/kt_optimize_rules/DeepSeek-V2-Lite-Chat-sft-amx.yaml

@@ -6,7 +6,7 @@
       generate_device: "cuda"
       prefill_device: "cuda"
 - match:
-    name: "^model\\.layers\\.(?!.*self_attn\\.kv_b_proj).*$"  # regular expression 
+    name: "^model\\.layers\\.(?!.*self_attn\\.kv_b_proj).*$"  # regular expression
     class: torch.nn.Linear  # only match modules matching name and class simultaneously
   replace:
     class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
@@ -66,4 +66,4 @@
     class: "default"
     kwargs:
       generate_device: "cpu"
-      prefill_device: "cpu"
+      prefill_device: "cpu"

examples/kt_optimize_rules/DeepSeek-V2-Lite-Chat-sft.yaml

@@ -6,7 +6,7 @@
       generate_device: "cuda"
       prefill_device: "cuda"
 - match:
-    name: "^model\\.layers\\.(?!.*self_attn\\.kv_b_proj).*$"  # regular expression 
+    name: "^model\\.layers\\.(?!.*self_attn\\.kv_b_proj).*$"  # regular expression
     class: torch.nn.Linear  # only match modules matching name and class simultaneously
   replace:
     class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
@@ -65,4 +65,4 @@
     class: "default"
     kwargs:
       generate_device: "cpu"
-      prefill_device: "cpu"
+      prefill_device: "cpu"

examples/kt_optimize_rules/DeepSeek-V2-Lite-Chat.yaml

@@ -6,7 +6,7 @@
       generate_device: "cuda"
       prefill_device: "cuda"
 - match:
-    name: "^model\\.layers\\.(?!.*self_attn\\.kv_b_proj).*$"  # regular expression 
+    name: "^model\\.layers\\.(?!.*self_attn\\.kv_b_proj).*$"  # regular expression
     class: torch.nn.Linear  # only match modules matching name and class simultaneously
   replace:
     class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
@@ -65,4 +65,4 @@
     class: "default"
     kwargs:
       generate_device: "cpu"
-      prefill_device: "cpu"
+      prefill_device: "cpu"

examples/kt_optimize_rules/DeepSeek-V3-Chat-amx.yaml

@@ -7,7 +7,7 @@
       prefill_device: "cuda"
 
 - match:
-    name: "^lm_head$"  # regular expression 
+    name: "^lm_head$"  # regular expression
     class: torch.nn.Linear  # only match modules matching name and class simultaneously
   replace:
     class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
@@ -18,7 +18,7 @@
       prefill_op: "KLinearTorch"
 
 - match:
-    name: "^model\\.layers\\.(?!.*self_attn\\.kv_b_proj).*$"  # regular expression 
+    name: "^model\\.layers\\.(?!.*self_attn\\.kv_b_proj).*$"  # regular expression
     class: torch.nn.Linear  # only match modules matching name and class simultaneously
   replace:
     class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
@@ -74,4 +74,4 @@
     class: "default"
     kwargs:
       generate_device: "cpu"
-      prefill_device: "cpu"
+      prefill_device: "cpu"

examples/kt_optimize_rules/DeepSeek-V3-Chat-sft-amx-multi-gpu.yaml

@@ -24,7 +24,7 @@
       prefill_device: "cuda:1"
 
 - match:
-    name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.(?!self_attn\\.kv_b_proj).*$"  # regular expression 
+    name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.(?!self_attn\\.kv_b_proj).*$"  # regular expression
     class: torch.nn.Linear  # only match modules matching name and class simultaneously
   replace:
     class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
@@ -35,7 +35,7 @@
       prefill_op: "KLinearTorch"
 
 - match:
-    name: "^model\\.layers\\.([3456][0-9])\\.(?!self_attn\\.kv_b_proj).*$"  # regular expression 
+    name: "^model\\.layers\\.([3456][0-9])\\.(?!self_attn\\.kv_b_proj).*$"  # regular expression
     class: torch.nn.Linear  # only match modules matching name and class simultaneously
   replace:
     class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
@@ -44,7 +44,7 @@
       prefill_device: "cuda:1"
       generate_op: "KLinearTorch"
       prefill_op: "KLinearTorch"
-  
+
 - match:
     name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.mlp$"
     class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
@@ -125,7 +125,7 @@
     class: "ktransformers.operators.models.KDeepseekV2Model"
     kwargs:
       per_layer_prefill_intput_threshold: 0 # 0 is close layer wise prefill
-      transfer_map: 
+      transfer_map:
         30: "cuda:1"
 
 - match:

examples/kt_optimize_rules/DeepSeek-V3-Chat-sft-amx.yaml

@@ -7,7 +7,7 @@
       prefill_device: "cuda"
 
 - match:
-    name: "^lm_head$"  # regular expression 
+    name: "^lm_head$"  # regular expression
     class: torch.nn.Linear  # only match modules matching name and class simultaneously
   replace:
     class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
@@ -18,7 +18,7 @@
       prefill_op: "KLinearTorch"
 
 - match:
-    name: "^model\\.layers\\.(?!.*self_attn\\.kv_b_proj).*$"  # regular expression 
+    name: "^model\\.layers\\.(?!.*self_attn\\.kv_b_proj).*$"  # regular expression
     class: torch.nn.Linear  # only match modules matching name and class simultaneously
   replace:
     class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
@@ -74,4 +74,4 @@
     class: "default"
     kwargs:
       generate_device: "cpu"
-      prefill_device: "cpu"
+      prefill_device: "cpu"

src/llamafactory/chat/kt_engine.py

@@ -65,8 +65,7 @@ class KTransformersEngine(BaseEngine):
         self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args)
 
         self.model = load_model(
-            self.tokenizer, model_args, finetuning_args,
-            is_trainable=False, add_valuehead=(not self.can_generate)
+            self.tokenizer, model_args, finetuning_args, is_trainable=False, add_valuehead=(not self.can_generate)
         )
 
         self.generating_args = generating_args.to_dict()
@@ -143,14 +142,14 @@ class KTransformersEngine(BaseEngine):
         input_tensor = torch.tensor([prompt_ids], dtype=torch.long, device=device)
         if self.force_think:
             think = torch.tensor(
-                [self.tokenizer.encode("<think>\n", add_special_tokens=False)],
-                dtype=torch.long, device=device
+                [self.tokenizer.encode("<think>\n", add_special_tokens=False)], dtype=torch.long, device=device
             )
             input_tensor = torch.cat([input_tensor, think], dim=1)
 
         use_flashinfer = (
             platform.system() != "Windows"
-            and getattr(self.model.config, "architectures", [""])[0] in {"DeepseekV2ForCausalLM", "DeepseekV3ForCausalLM"}
+            and getattr(self.model.config, "architectures", [""])[0]
+            in {"DeepseekV2ForCausalLM", "DeepseekV3ForCausalLM"}
             and flashinfer_enabled
             and get_compute_capability() >= 8
             and device_manager.gpu_vendor == GPUVendor.NVIDIA
@@ -159,19 +158,32 @@ class KTransformersEngine(BaseEngine):
         def make_gen():
             if use_flashinfer:
                 return prefill_and_generate_capture(
-                    self.model, self.tokenizer, input_tensor, max_tokens, self.use_cuda_graph,
-                    mode=self.mode, force_think=self.force_think, chunk_size=self.chunk_size,
+                    self.model,
+                    self.tokenizer,
+                    input_tensor,
+                    max_tokens,
+                    self.use_cuda_graph,
+                    mode=self.mode,
+                    force_think=self.force_think,
+                    chunk_size=self.chunk_size,
                     use_flashinfer_mla=True,
                     num_heads=self.model.config.num_attention_heads,
                     head_dim_ckv=getattr(self.model.config, "kv_lora_rank", 0),
                     head_dim_kpe=getattr(self.model.config, "qk_rope_head_dim", 0),
-                    q_head_dim=getattr(self.model.config, "qk_rope_head_dim", 0) + getattr(self.model.config, "qk_nope_head_dim", 0),
+                    q_head_dim=getattr(self.model.config, "qk_rope_head_dim", 0)
+                    + getattr(self.model.config, "qk_nope_head_dim", 0),
                     echo_stream=False,
                 )
             else:
                 return prefill_and_generate_capture(
-                    self.model, self.tokenizer, input_tensor, max_tokens, self.use_cuda_graph,
-                    mode=self.mode, force_think=self.force_think, chunk_size=self.chunk_size,
+                    self.model,
+                    self.tokenizer,
+                    input_tensor,
+                    max_tokens,
+                    self.use_cuda_graph,
+                    mode=self.mode,
+                    force_think=self.force_think,
+                    chunk_size=self.chunk_size,
                     echo_stream=False,
                 )
 
@@ -182,9 +194,11 @@ class KTransformersEngine(BaseEngine):
             try:
                 gen = make_gen()
                 if hasattr(gen, "__aiter__"):
+
                     async def drain_async():
                         async for t in gen:
                             loop.call_soon_threadsafe(q.put_nowait, t if isinstance(t, str) else str(t))
+
                     asyncio.run(drain_async())
                 elif hasattr(gen, "__iter__"):
                     for t in gen:
@@ -252,7 +266,7 @@ class KTransformersEngine(BaseEngine):
         async with self.semaphore:
             produced = ""
             async for t in self._generate(messages, system, tools, **input_kwargs):
-                delta = t[len(produced):] if t.startswith(produced) else t
+                delta = t[len(produced) :] if t.startswith(produced) else t
                 produced = t
                 if delta:
                     yield delta

src/llamafactory/data/template.py

@@ -616,7 +616,14 @@ def get_template_and_fix_tokenizer(tokenizer: "PreTrainedTokenizer", data_args:
         logger.info_rank0(f"Using default system message: {data_args.default_system}.")
         template.default_system = data_args.default_system
 
-    template.enable_thinking = data_args.enable_thinking
+    if isinstance(template, ReasoningTemplate):
+        logger.warning_rank0(
+            "You are using reasoning template, "
+            "please add `_nothink` suffix if the model is not a reasoning model. "
+            "e.g., qwen3_vl_nothink"
+        )
+        template.enable_thinking = data_args.enable_thinking
+
     template.fix_special_tokens(tokenizer)
     template.fix_jinja_template(tokenizer)
     return template

src/llamafactory/extras/misc.py

@@ -312,9 +312,11 @@ def use_openmind() -> bool:
 def use_ray() -> bool:
     return is_env_enabled("USE_RAY")
 
+
 def use_kt() -> bool:
     return is_env_enabled("USE_KT")
 
+
 def find_available_port() -> int:
     r"""Find an available port on the local machine."""
     sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)

src/llamafactory/extras/packages.py

@@ -110,6 +110,7 @@ def is_starlette_available():
 def is_transformers_version_greater_than(content: str):
     return _get_package_version("transformers") >= version.parse(content)
 
+
 @lru_cache
 def is_torch_version_greater_than(content: str):
     return _get_package_version("torch") >= version.parse(content)

src/llamafactory/hparams/finetuning_args.py

@@ -439,6 +439,7 @@ class SwanLabArguments:
         metadata={"help": "The Lark(飞书) secret for SwanLab."},
     )
 
+
 @dataclass
 class FinetuningArguments(
     SwanLabArguments,

src/llamafactory/hparams/model_args.py

@@ -485,7 +485,9 @@ class KTransformersArguments:
     )
     kt_optimize_rule: Optional[str] = field(
         default=None,
-        metadata={"help": "Path To The KTransformers Optimize Rule; See https://github.com/kvcache-ai/ktransformers/."},
+        metadata={
+            "help": "Path To The KTransformers Optimize Rule; See https://github.com/kvcache-ai/ktransformers/."
+        },
     )
     cpu_infer: Optional[int] = field(
         default=32,
@@ -517,9 +519,16 @@ class KTransformersArguments:
         metadata={"help": "Force-Think Toggle For The KT Engine."},
     )
 
+
 @dataclass
 class ModelArguments(
-    SGLangArguments, VllmArguments, KTransformersArguments, ExportArguments, ProcessorArguments, QuantizationArguments, BaseModelArguments
+    SGLangArguments,
+    VllmArguments,
+    KTransformersArguments,
+    ExportArguments,
+    ProcessorArguments,
+    QuantizationArguments,
+    BaseModelArguments,
 ):
     r"""Arguments pertaining to which model/config/tokenizer we are going to fine-tune or infer.
 

src/llamafactory/hparams/training_args.py

@@ -90,6 +90,7 @@ class RayArguments:
             elif self.ray_storage_filesystem == "gs" or self.ray_storage_filesystem == "gcs":
                 self.ray_storage_filesystem = fs.GcsFileSystem()
 
+
 @dataclass
 class TrainingArguments(RayArguments, BaseTrainingArguments):
     r"""Arguments pertaining to the trainer."""

src/llamafactory/launcher.py

@@ -57,7 +57,9 @@ def launch():
     if is_env_enabled("USE_MCA"):  # force use torchrun
         os.environ["FORCE_TORCHRUN"] = "1"
 
-    if command == "train" and (is_env_enabled("FORCE_TORCHRUN") or (get_device_count() > 1 and not use_ray() and not use_kt())):
+    if command == "train" and (
+        is_env_enabled("FORCE_TORCHRUN") or (get_device_count() > 1 and not use_ray() and not use_kt())
+    ):
         # launch distributed training
         nnodes = os.getenv("NNODES", "1")
         node_rank = os.getenv("NODE_RANK", "0")

src/llamafactory/model/adapter.py

@@ -167,7 +167,7 @@ def _setup_lora_tuning(
             is_mergeable = False
 
         if model_args.use_kt:
-            assert len(model_args.adapter_name_or_path) == 1, "Up to now, KTransformers model only accepts a single adapter, for more features, you can contact with us."
+            assert len(model_args.adapter_name_or_path) == 1, "KTransformers model only accepts a single adapter"
             is_mergeable = False
 
         if model_args.use_unsloth:
@@ -190,7 +190,9 @@ def _setup_lora_tuning(
 
         if model_args.use_kt:
             if model_args.infer_backend != EngineName.KT:
-                raise ValueError("We should use ktransformers as backend to infer the adapter fine-tuned by ktransformers.")
+                raise ValueError(
+                    "We should use ktransformers as backend to infer the adapter fine-tuned by ktransformers."
+                )
 
         for adapter in adapter_to_merge:
             model: LoraModel = PeftModel.from_pretrained(model, adapter, **init_kwargs)
@@ -218,9 +220,9 @@ def _setup_lora_tuning(
         if model_args.use_kt:
             new_list = []
             for m in target_modules:
-                if m in ('down_proj', 'up_proj', 'gate_proj'):
+                if m in ("down_proj", "up_proj", "gate_proj"):
                     new_list.extend([f"mlp.{m}", f"shared_experts.{m}"])
-                elif m not in ('generate_linear', 'orig_module', 'prefill_linear'):
+                elif m not in ("generate_linear", "orig_module", "prefill_linear"):
                     new_list.append(m)
 
             target_modules[:] = new_list

src/llamafactory/model/loader.py

@@ -146,6 +146,7 @@ def load_model(
     lazy_load = False
     if model_args.use_kt:
         from ktransformers.sft.monkey_patch_torch_module import install_patch
+
         install_patch()
         model = load_kt_pretrained_model(config, model_args)
     elif model_args.use_unsloth:

src/llamafactory/model/model_utils/attention.py

@@ -59,6 +59,7 @@ def configure_attn_implementation(config: "PretrainedConfig", model_args: "Model
         requested_attn_implementation = "sdpa"
     elif model_args.flash_attn == AttentionFunction.FA2:
         from transformers import is_torch_npu_available
+
         if not (is_flash_attn_2_available() or is_torch_npu_available()):
             logger.warning_rank0("FlashAttention-2 is not installed.")
             return

src/llamafactory/model/model_utils/ktransformers.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import importlib.util as _u
-from typing import TYPE_CHECKING, Any, Optional
+from typing import TYPE_CHECKING, Any
 
 import torch
 
@@ -43,6 +43,7 @@ if KT_AVAILABLE:
 
 logger = logging.get_logger(__name__)
 
+
 def _get_kt_kwargs(
     config: "PretrainedConfig",
     model_name_or_path: str,
@@ -64,9 +65,7 @@ def _get_kt_kwargs(
     }
 
 
-def load_kt_pretrained_model(
-    config: "PretrainedConfig", model_args: "ModelArguments"
-) -> Optional["PreTrainedModel"]:
+def load_kt_pretrained_model(config: "PretrainedConfig", model_args: "ModelArguments") -> "PreTrainedModel":
     r"""Optionally load pretrained model with KTransformers. Used in training."""
     custom_models = {
         "DeepseekV2ForCausalLM": DeepseekV2ForCausalLM,
@@ -79,7 +78,7 @@ def load_kt_pretrained_model(
     Config().chunk_size = model_args.chunk_size
     config = AutoConfig.from_pretrained(model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code)
 
-    if model_args.mode == 'long_context':
+    if model_args.mode == "long_context":
         assert config.architectures[0] == "LlamaForCausalLM", "only LlamaForCausalLM support long_context mode"
         torch.set_default_dtype(torch.float16)
     else:
@@ -88,9 +87,7 @@ def load_kt_pretrained_model(
     with torch.device("meta"):
         if config.architectures[0] in custom_models:
             print("using custom modeling_xxx.py.")
-            if (
-                "Qwen2Moe" in config.architectures[0]
-            ):  # Qwen2Moe must use flash_attention_2 to avoid overflow.
+            if "Qwen2Moe" in config.architectures[0]:  # Qwen2Moe must use flash_attention_2 to avoid overflow.
                 config._attn_implementation = "flash_attention_2"
             if "Llama" in config.architectures[0]:
                 config._attn_implementation = "eager"
@@ -115,21 +112,17 @@ def load_kt_pretrained_model(
     return model
 
 
-def get_kt_peft_model(
-    model: "PreTrainedModel", peft_kwargs: dict[str, Any]
-) -> "PreTrainedModel":
+def get_kt_peft_model(model: "PreTrainedModel", peft_kwargs: dict[str, Any]) -> "PreTrainedModel":
     r"""Get the peft model for the pretrained model with KTransformers. Used in training."""
     from ktransformers.sft.peft_utils.mapping import get_peft_model
 
     return get_peft_model(model, peft_kwargs)
 
 
-def load_kt_peft_model(
-    model_args: "ModelArguments", model: "PreTrainedModel",
-) -> "PreTrainedModel":
+def load_kt_peft_model(model_args: "ModelArguments", model: "PreTrainedModel") -> "PreTrainedModel":
     r"""Load peft model with KTransformers. Used in both training and inference."""
     load_adapter_name_or_path = model_args.adapter_name_or_path[0]
-    if load_adapter_name_or_path.endswith('.gguf'):
+    if load_adapter_name_or_path.endswith(".gguf"):
         inject_lora_layer(model, load_adapter_name_or_path)
         adapter_gguf_loader = GGUFLoader(load_adapter_name_or_path)
         load_weights(model, adapter_gguf_loader, adapter_gguf=True)

src/llamafactory/train/ksft/workflow.py

@@ -47,6 +47,7 @@ def run_sft(
     model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
 
     from ktransformers.util.globals import GLOBAL_CONFIG
+
     GLOBAL_CONFIG._config["mod"] = "sft"
 
     if getattr(model, "is_quantized", False) and not training_args.do_train:
@@ -66,12 +67,13 @@ def run_sft(
     # Metric utils
     metric_module = {}
     if training_args.predict_with_generate:
-        raise NotImplementedError("`predict_with_generate` is not supported in KTransformers SFT yet. if you do need it, please open an issue.")
+        raise NotImplementedError("`predict_with_generate` is not supported in KTransformers SFT yet.")
     elif finetuning_args.compute_accuracy:
-        raise NotImplementedError("`compute_accuracy` is not supported in KTransformers SFT yet. if you do need it, please open an issue.")
+        raise NotImplementedError("`compute_accuracy` is not supported in KTransformers SFT yet.")
 
     # Initialize our Trainer
     from ktransformers.sft.lora import KTrainer
+
     trainer = KTrainer(
         model=model,
         args=training_args,

src/llamafactory/train/tuner.py

@@ -24,7 +24,7 @@ from ..data import get_template_and_fix_tokenizer
 from ..extras import logging
 from ..extras.constants import V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
 from ..extras.misc import infer_optim_dtype
-from ..extras.packages import is_kt_available, is_mcore_adapter_available, is_ray_available
+from ..extras.packages import is_mcore_adapter_available, is_ray_available
 from ..hparams import get_infer_args, get_ray_args, get_train_args, read_args
 from ..model import load_model, load_tokenizer
 from .callbacks import LogCallback, PissaConvertCallback, ReporterCallback
@@ -86,12 +86,12 @@ def _training_function(config: dict[str, Any]) -> None:
         run_pt(model_args, data_args, training_args, finetuning_args, callbacks)
     elif finetuning_args.stage == "sft":
         if model_args.use_kt:
-            if not is_kt_available():
-                raise ImportError("KTransformers is not installed. Please install it with `pip install ktransformers`.")
             from .ksft.workflow import run_sft as run_sft_kt
+
             run_sft_kt(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
-        
-        run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
+        else:
+            run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
+
     elif finetuning_args.stage == "rm":
         run_rm(model_args, data_args, training_args, finetuning_args, callbacks)
     elif finetuning_args.stage == "ppo":