[refactor] rename lfm template to lfm2 and add LFM 2.5 to README

README.md

@@ -298,6 +298,7 @@ Read technical notes:
 | [InternLM/Intern-S1-mini](https://huggingface.co/internlm/)       | 8B                               | intern_s1            |
 | [Kimi-VL](https://huggingface.co/moonshotai)                      | 16B                              | kimi_vl              |
 | [Ling 2.0 (mini/flash)](https://huggingface.co/inclusionAI)       | 16B/100B                         | bailing_v2           |
+| [LFM 2.5 (VL)](https://huggingface.co/LiquidAI)                   | 1.2B/1.6B                        | lfm2/lfm2_vl         |
 | [Llama](https://github.com/facebookresearch/llama)                | 7B/13B/33B/65B                   | -                    |
 | [Llama 2](https://huggingface.co/meta-llama)                      | 7B/13B/70B                       | llama2               |
 | [Llama 3-3.3](https://huggingface.co/meta-llama)                  | 1B/3B/8B/70B                     | llama3               |

README_zh.md

@@ -300,6 +300,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 | [InternLM/Intern-S1-mini](https://huggingface.co/internlm/)       | 8B                               | intern_s1            |
 | [Kimi-VL](https://huggingface.co/moonshotai)                      | 16B                              | kimi_vl              |
 | [Ling 2.0 (mini/flash)](https://huggingface.co/inclusionAI)       | 16B/100B                         | bailing_v2           |
+| [LFM 2.5 (VL)](https://huggingface.co/LiquidAI)                   | 1.2B/1.6B                        | lfm2/lfm2_vl         |
 | [Llama](https://github.com/facebookresearch/llama)                | 7B/13B/33B/65B                   | -                    |
 | [Llama 2](https://huggingface.co/meta-llama)                      | 7B/13B/70B                       | llama2               |
 | [Llama 3-3.3](https://huggingface.co/meta-llama)                  | 1B/3B/8B/70B                     | llama3               |

src/llamafactory/data/template.py

@@ -1331,18 +1331,18 @@ def get_template_and_fix_tokenizer(tokenizer: "PreTrainedTokenizer", data_args:
 
 
 register_template(
-    name="lfm",
+    name="lfm2",
     format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
     format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
     format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
-    format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="lfm"),
+    format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="lfm2"),
     format_observation=StringFormatter(
         slots=[
             "<|im_start|>tool\n<|tool_response_start|>{{content}}<|tool_response_end|><|im_end|>\n"
             "<|im_start|>assistant\n"
         ]
     ),
-    format_tools=ToolFormatter(tool_format="lfm"),
+    format_tools=ToolFormatter(tool_format="lfm2"),
     default_system="You are a helpful AI assistant.",
     stop_words=["<|im_end|>"],
     tool_call_words=("<|tool_call_start|>", "<|tool_call_end|>"),
@@ -1355,14 +1355,14 @@ def get_template_and_fix_tokenizer(tokenizer: "PreTrainedTokenizer", data_args:
     format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
     format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
     format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
-    format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="lfm"),
+    format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="lfm2"),
     format_observation=StringFormatter(
         slots=[
             "<|im_start|>tool\n<|tool_response_start|>{{content}}<|tool_response_end|><|im_end|>\n"
             "<|im_start|>assistant\n"
         ]
     ),
-    format_tools=ToolFormatter(tool_format="lfm"),
+    format_tools=ToolFormatter(tool_format="lfm2"),
     default_system="You are a helpful multimodal assistant by Liquid AI.",
     stop_words=["<|im_end|>"],
     tool_call_words=("<|tool_call_start|>", "<|tool_call_end|>"),

src/llamafactory/data/tool_utils.py

@@ -102,7 +102,7 @@ class FunctionCall(NamedTuple):
     """"arguments": <args-json-object>}}\n</tool_call>"""
 )
 
-LFM_TOOL_PROMPT = "List of tools: <|tool_list_start|>{tool_text}<|tool_list_end|>"
+LFM2_TOOL_PROMPT = "List of tools: <|tool_list_start|>{tool_text}<|tool_list_end|>"
 
 
 @dataclass
@@ -549,8 +549,8 @@ def tool_formatter(tools: list[dict[str, Any]]) -> str:
         return LING_TOOL_PROMPT.format(tool_text=tool_text) + "\n" + "detailed thinking off"
 
 
-class LFMToolUtils(ToolUtils):
-    r"""LFM 2.5 tool using template with Pythonic function call syntax."""
+class LFM2ToolUtils(ToolUtils):
+    r"""LFM2.5 tool using template with Pythonic function call syntax."""
 
     @override
     @staticmethod
@@ -560,7 +560,7 @@ def tool_formatter(tools: list[dict[str, Any]]) -> str:
             tool = tool.get("function", tool) if tool.get("type") == "function" else tool
             tool_list.append(tool)
 
-        return LFM_TOOL_PROMPT.format(tool_text=json.dumps(tool_list, ensure_ascii=False))
+        return LFM2_TOOL_PROMPT.format(tool_text=json.dumps(tool_list, ensure_ascii=False))
 
     @override
     @staticmethod
@@ -643,7 +643,7 @@ def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]:
             for keyword in node.keywords:
                 key = keyword.arg
                 try:
-                    value = LFMToolUtils._ast_to_value(keyword.value)
+                    value = LFM2ToolUtils._ast_to_value(keyword.value)
                 except (ValueError, SyntaxError):
                     return content
                 args_dict[key] = value
@@ -657,7 +657,7 @@ def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]:
     "default": DefaultToolUtils(),
     "glm4": GLM4ToolUtils(),
     "llama3": Llama3ToolUtils(),
-    "lfm": LFMToolUtils(),
+    "lfm2": LFM2ToolUtils(),
     "minimax1": MiniMaxM1ToolUtils(),
     "minimax2": MiniMaxM2ToolUtils(),
     "mistral": MistralToolUtils(),

src/llamafactory/extras/constants.py

@@ -1502,7 +1502,7 @@ def register_model_group(
             DownloadSource.DEFAULT: "LiquidAI/LFM2.5-1.2B-Instruct",
         },
     },
-    template="lfm",
+    template="lfm2",
 )
 
 

tests/data/test_formatter.py

@@ -295,17 +295,17 @@ def test_qwen_multi_tool_extractor():
 
 
 @pytest.mark.runs_on(["cpu", "mps"])
-def test_lfm_function_formatter():
-    formatter = FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="lfm")
+def test_lfm2_function_formatter():
+    formatter = FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="lfm2")
     tool_calls = json.dumps(FUNCTION)
     assert formatter.apply(content=tool_calls) == [
         """<|tool_call_start|>[tool_name(foo="bar", size=10)]<|tool_call_end|><|im_end|>\n"""
     ]
 
 
 @pytest.mark.runs_on(["cpu", "mps"])
-def test_lfm_multi_function_formatter():
-    formatter = FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="lfm")
+def test_lfm2_multi_function_formatter():
+    formatter = FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="lfm2")
     tool_calls = json.dumps([FUNCTION] * 2)
     assert formatter.apply(content=tool_calls) == [
         """<|tool_call_start|>[tool_name(foo="bar", size=10), tool_name(foo="bar", size=10)]<|tool_call_end|>"""
@@ -314,23 +314,23 @@ def test_lfm_multi_function_formatter():
 
 
 @pytest.mark.runs_on(["cpu", "mps"])
-def test_lfm_tool_formatter():
-    formatter = ToolFormatter(tool_format="lfm")
+def test_lfm2_tool_formatter():
+    formatter = ToolFormatter(tool_format="lfm2")
     assert formatter.apply(content=json.dumps(TOOLS)) == [
         "List of tools: <|tool_list_start|>" + json.dumps(TOOLS, ensure_ascii=False) + "<|tool_list_end|>"
     ]
 
 
 @pytest.mark.runs_on(["cpu", "mps"])
-def test_lfm_tool_extractor():
-    formatter = ToolFormatter(tool_format="lfm")
+def test_lfm2_tool_extractor():
+    formatter = ToolFormatter(tool_format="lfm2")
     result = """<|tool_call_start|>[test_tool(foo="bar", size=10)]<|tool_call_end|>"""
     assert formatter.extract(result) == [("test_tool", """{"foo": "bar", "size": 10}""")]
 
 
 @pytest.mark.runs_on(["cpu", "mps"])
-def test_lfm_multi_tool_extractor():
-    formatter = ToolFormatter(tool_format="lfm")
+def test_lfm2_multi_tool_extractor():
+    formatter = ToolFormatter(tool_format="lfm2")
     result = """<|tool_call_start|>[test_tool(foo="bar", size=10), another_tool(foo="job", size=2)]<|tool_call_end|>"""
     assert formatter.extract(result) == [
         ("test_tool", """{"foo": "bar", "size": 10}"""),
@@ -339,8 +339,8 @@ def test_lfm_multi_tool_extractor():
 
 
 @pytest.mark.runs_on(["cpu", "mps"])
-def test_lfm_tool_extractor_with_nested_dict():
-    formatter = ToolFormatter(tool_format="lfm")
+def test_lfm2_tool_extractor_with_nested_dict():
+    formatter = ToolFormatter(tool_format="lfm2")
     result = """<|tool_call_start|>[search(query="test", options={"limit": 10, "offset": 0})]<|tool_call_end|>"""
     extracted = formatter.extract(result)
     assert len(extracted) == 1
@@ -351,8 +351,8 @@ def test_lfm_tool_extractor_with_nested_dict():
 
 
 @pytest.mark.runs_on(["cpu", "mps"])
-def test_lfm_tool_extractor_with_list_arg():
-    formatter = ToolFormatter(tool_format="lfm")
+def test_lfm2_tool_extractor_with_list_arg():
+    formatter = ToolFormatter(tool_format="lfm2")
     result = """<|tool_call_start|>[batch_process(items=[1, 2, 3], enabled=True)]<|tool_call_end|>"""
     extracted = formatter.extract(result)
     assert len(extracted) == 1
@@ -363,17 +363,17 @@ def test_lfm_tool_extractor_with_list_arg():
 
 
 @pytest.mark.runs_on(["cpu", "mps"])
-def test_lfm_tool_extractor_no_match():
-    formatter = ToolFormatter(tool_format="lfm")
+def test_lfm2_tool_extractor_no_match():
+    formatter = ToolFormatter(tool_format="lfm2")
     result = "This is a regular response without tool calls."
     extracted = formatter.extract(result)
     assert extracted == result
 
 
 @pytest.mark.runs_on(["cpu", "mps"])
-def test_lfm_tool_round_trip():
-    formatter = FunctionFormatter(slots=["{{content}}"], tool_format="lfm")
-    tool_formatter = ToolFormatter(tool_format="lfm")
+def test_lfm2_tool_round_trip():
+    formatter = FunctionFormatter(slots=["{{content}}"], tool_format="lfm2")
+    tool_formatter = ToolFormatter(tool_format="lfm2")
     original = {"name": "my_func", "arguments": {"arg1": "hello", "arg2": 42, "arg3": True}}
     formatted = formatter.apply(content=json.dumps(original))
     extracted = tool_formatter.extract(formatted[0])