Initial upload of fine‑tuned Gemma + custom tokenizer

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+  }
+}

qwen_explicit_tokenizer.py

@@ -0,0 +1,448 @@
+"""
+Custom Qwen Tokenizer for explicit Format
+
+This tokenizer implements the explicit format for message processing:
+Format: Uses the standard chat template with proper role labels (user/assistant)
+
+The explicit format uses the model's built-in chat template and includes proper 
+loss computation flags for training.
+
+To save:
+uv run tokenizers/qwen_explicit_tokenizer.py 
+which will save the tokenizer to the repos/explicit-qwen-tokenizer directory.
+"""
+
+from typing import List, Dict, Any, Optional, Union
+from transformers import AutoTokenizer
+from transformers import Qwen2Tokenizer
+from transformers import Qwen2TokenizerFast
+import warnings
+import difflib
+import json
+import os
+
+
+class QwenExplicitTokenizer(Qwen2TokenizerFast):
+    """
+    Custom tokenizer for Qwen models that implements explicit format message processing.
+    
+    This tokenizer formats messages using the explicit format where:
+    - Messages use the standard chat template with proper role labels
+    - Uses the model's built-in chat formatting
+    - Loss is computed on the assistant/output sections
+    
+    Attributes:
+        start_string (str): The starting string used for output generation (depends on tokenizer)
+        end_string (str): The ending string used for output generation (depends on tokenizer)
+    """
+    
+    def __init__(self, *args, **kwargs):
+        """
+        Initialize the custom tokenizer.
+        
+        Accepts the same arguments as Qwen2TokenizerFast.
+        """
+        super().__init__(*args, **kwargs)
+        
+        # For explicit format, we use Qwen-specific tokens
+        self.start_string = "<|im_start|>"
+        self.end_string = "<|im_end|>"
+        
+        if not hasattr(self, 'init_kwargs'):
+            self.init_kwargs = {}
+        self.init_kwargs['start_string'] = self.start_string
+        self.init_kwargs['end_string'] = self.end_string
+    
+    @classmethod
+    def from_qwen_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
+        """
+        Load a tokenizer from a pretrained model or path.
+        
+        This method ensures our custom class is used instead of the base Qwen2TokenizerFast.
+        """
+        # Load the base tokenizer first to get all configuration
+        base_tokenizer = Qwen2TokenizerFast.from_pretrained(
+            pretrained_model_name_or_path, *args, **kwargs
+        )
+        
+        # Create new instance of our custom class by copying the base tokenizer
+        custom_tokenizer = cls.__new__(cls)
+        
+        # Copy all attributes from base tokenizer
+        for attr, value in base_tokenizer.__dict__.items():
+            setattr(custom_tokenizer, attr, value)
+        
+        # Initialize our custom attributes for explicit format
+        custom_tokenizer.start_string = "<|im_start|>"
+        custom_tokenizer.end_string = "<|im_end|>"
+        
+        # Update init_kwargs to include our custom attributes
+        if not hasattr(custom_tokenizer, 'init_kwargs'):
+            custom_tokenizer.init_kwargs = {}
+        custom_tokenizer.init_kwargs['start_string'] = custom_tokenizer.start_string
+        custom_tokenizer.init_kwargs['end_string'] = custom_tokenizer.end_string
+        
+        return custom_tokenizer
+    
+    def save_pretrained(self, save_directory: Union[str, os.PathLike], **kwargs):
+        """
+        Save the tokenizer to a directory, including custom configuration.
+        """
+        # Call parent save method
+        super().save_pretrained(save_directory, **kwargs)
+        
+        # Save custom configuration
+        config_file = os.path.join(save_directory, "tokenizer_config.json")
+        if os.path.exists(config_file):
+            with open(config_file, 'r') as f:
+                config = json.load(f)
+        else:
+            config = {}
+        
+        # Add our custom class info
+        config["tokenizer_class"] = "QwenExplicitTokenizer"
+        config["start_string"] = self.start_string
+        config["end_string"] = self.end_string
+        # Point to our custom class in the uploaded file
+        config["auto_map"] = {
+            "AutoTokenizer": ["qwen_explicit_tokenizer.QwenExplicitTokenizer", "qwen_explicit_tokenizer.QwenExplicitTokenizer"]
+        }
+        
+        with open(config_file, 'w') as f:
+            json.dump(config, f, indent=2)
+    
+    def messages_to_loss_texts(
+            self,
+            messages: List[Dict[str, Any]], 
+            start_generation: bool = False,
+        ) -> List[Dict[str, Any]]:
+        """
+        From messages (description / input / output) to texts (text / compute_loss) with whether or not loss should be calculated on the text for training.
+        Uses the explicit format matching chat_utils.py.
+        """
+        
+        # Qwen3 explicit parameters copied from chat_utils.py
+        description_map = lambda x: [{
+            "text": "<|im_start|>description\n" + x + "<|im_end|>\n",
+            "compute_loss": False,
+        }]
+        input_map = lambda x: [{
+            "text": "<|im_start|>input\n" + x + "<|im_end|>\n",
+            "compute_loss": False,
+        }]
+        output_map = lambda x: [{
+            "text": "<|im_start|>output\n",
+            "compute_loss": False,
+        },{
+            "text": x + "<|im_end|>",
+            "compute_loss": True,
+        },{
+            "text": "\n",
+            "compute_loss": False,
+        }]
+        
+        texts = []
+        has_description = False
+        first_output = True
+        
+        for message in messages:
+            role = message["role"]
+            content = message["content"]
+            
+            if role == "description":
+                has_description = True
+                texts.extend(description_map(content))
+            elif role == "input":
+                texts.extend(input_map(content))
+            elif role == "output":
+                out_texts = output_map(content)
+                if first_output and not has_description:
+                    # set compute_loss to False for all
+                    for text in out_texts:
+                        text["compute_loss"] = False
+                texts.extend(out_texts)
+                first_output = False
+            else:
+                raise ValueError(f"Unknown role: {role}. Must be description, input, or output.")
+        
+        # Add generation prompt if start_generation is True
+        if start_generation:
+            start_generation_text = "<|im_start|>output\n"
+            texts.extend([{"text": start_generation_text, "compute_loss": False}])
+        
+        return texts
+    
+    def messages_to_text(
+            self, 
+            messages: List[Dict[str, Any]], 
+            start_generation: bool = False, 
+        ) -> str:
+        """
+        Messages (description / input / output) to raw text (text).
+        Uses the explicit format matching chat_utils.py.
+        """
+        texts = self.messages_to_loss_texts(messages, start_generation=start_generation)
+        text = "".join([text["text"] for text in texts])
+        return text
+    
+
+    def tokenize_messages(
+            self, 
+            messages: List[Dict[str, Any]] | List[List[Dict[str, Any]]],
+            start_generation: bool = False, 
+            **kwargs,
+        ):
+        """
+        For tokenizing from messages to texts. Supports batching. Good for generation
+        """
+        if isinstance(messages, list) and isinstance(messages[0], list):
+            # Handle list of lists of messages
+            all_texts = []
+            for message_list in messages:
+                texts = self.messages_to_text(message_list, start_generation)
+                all_texts.append(texts)
+        else:
+            # Handle single list of messages
+            texts = self.messages_to_text(messages, start_generation)
+            all_texts = [texts]
+        
+        # Tokenize all texts
+        processed = self(text=all_texts, **kwargs)
+        # if start_generation, remove the last token if it is the eos token
+        if start_generation and processed["input_ids"][-1] == self.eos_token_id:
+            processed["input_ids"] = processed["input_ids"][:-1]
+            processed["attention_mask"] = processed["attention_mask"][:-1]
+            processed["labels"] = processed["labels"][:-1]
+        return processed
+    
+    
+    def tokenize_loss_texts(
+            self, 
+            texts: List[Dict[str, Any]], 
+            loss_on_eos: bool = False, 
+            include_eos: bool = True,
+        ):
+        """
+        Tokenize texts (text / compute_loss) to tokenized texts (input_ids / attention_mask / labels).
+
+        Needs more complex logic to handle the back and forth labeling.
+        """
+        if loss_on_eos:
+            raise ValueError("Loss on EOS is not currently supported.")
+        
+        # Handle single string input
+        if isinstance(texts, str):
+            processed = self(text=texts)
+            # Add EOS token if needed
+            if (self.eos_token_id is not None and 
+                processed["input_ids"][-1] != self.eos_token_id):
+                processed["input_ids"] = processed["input_ids"] + [self.eos_token_id]
+                processed["attention_mask"] = processed["attention_mask"] + [1]
+            return processed
+        
+        # Handle list of text dictionaries
+        all_processed = []
+        all_texts = ''
+        example_inds = []
+        dataset_inds = []
+        
+        for i, item in enumerate(texts):
+            processed = self(text=item["text"])
+            
+            # Remove BOS token from all but first item
+            if i != 0 and self.bos_token_id == processed["input_ids"][0]:
+                processed["input_ids"] = processed["input_ids"][1:]
+                processed["attention_mask"] = processed["attention_mask"][1:]
+            
+            # # Remove EOS token if present at the end
+            # if processed["input_ids"][-1] == self.eos_token_id:
+            #     processed["input_ids"] = processed["input_ids"][:-1]
+            #     processed["attention_mask"] = processed["attention_mask"][:-1]
+            
+            # # Check for EOS token in the middle (with special handling for <|im_end|>)
+            # if self.eos_token_id in processed["input_ids"]:
+            #     if not self.decode([self.eos_token_id]) == "<|im_end|>":
+            #         raise ValueError(f"EOS token is present in input_ids: {processed['input_ids']}. Not currently supported.")
+            
+            # Set labels based on compute_loss flag
+            if item["compute_loss"]:
+                processed["labels"] = processed["input_ids"].copy()
+            else:
+                processed["labels"] = [-100] * len(processed["input_ids"])
+            
+            # Remove duplicate BOS tokens
+            if all_processed:
+                if processed["input_ids"][0] == self.bos_token_id:
+                    processed["input_ids"] = processed["input_ids"][1:]
+                    processed["attention_mask"] = processed["attention_mask"][1:]
+                    processed["labels"] = processed["labels"][1:]
+            
+            all_processed.append(processed)
+            all_texts += item["text"]
+            
+            # Handle example indices
+            this_num = -1
+            if 'example_ind' in item.keys():
+                if item["example_ind"] is not None:
+                    this_num = item["example_ind"]
+            example_inds.extend([this_num] * len(processed["input_ids"]))
+            
+            # Handle dataset indices
+            dataset_ind = -1
+            if "data_id" in item.keys():
+                if item["data_id"] is not None:
+                    dataset_ind = item["data_id"]
+            dataset_inds.extend([dataset_ind] * len(processed["input_ids"]))
+        
+        # Combine all processed results
+        processed = all_processed[0].copy()
+        processed["input_ids"] = [item for sublist in [p["input_ids"] for p in all_processed] for item in sublist]
+        processed["attention_mask"] = [item for sublist in [p["attention_mask"] for p in all_processed] for item in sublist]
+        processed["labels"] = [item for sublist in [p["labels"] for p in all_processed] for item in sublist]
+        processed["example_inds"] = example_inds
+        processed["data_ids"] = dataset_inds
+        
+        # Validate by tokenizing all_texts at once and comparing
+        processed_all = self(text=all_texts)
+        if len(processed_all["input_ids"]) != len(processed["input_ids"]):
+            warnings.warn(f"All texts are not the same length as the first text. Please check your dataset. {len(processed_all['input_ids'])} != {len(processed['input_ids'])}")
+            
+            # Generate diff for debugging
+            all_text = self.decode(processed_all["input_ids"], skip_special_tokens=False)
+            processed_text = self.decode(processed["input_ids"], skip_special_tokens=False)
+            
+            diff = difflib.unified_diff(all_text.splitlines(), processed_text.splitlines())
+            diff_str = "\n".join(diff)
+            print("Diff between texts:")
+            print(diff_str)
+            
+            # Token diff
+            all_tokens_str = '\n'.join([str(s) for s in processed_all["input_ids"]])
+            processed_tokens_str = '\n'.join([str(s) for s in processed["input_ids"]])
+            token_diff = difflib.unified_diff(all_tokens_str.splitlines(), processed_tokens_str.splitlines())
+            token_diff_str = "\n".join(token_diff)
+            print("Diff between tokenized texts:")
+            print(token_diff_str)
+            breakpoint()
+        
+        # Add EOS token if needed
+        if (self.eos_token_id is not None and 
+            processed["input_ids"][-1] != self.eos_token_id):
+            processed["input_ids"] = processed["input_ids"] + [self.eos_token_id]
+            processed["example_inds"] = processed["example_inds"] + [-1]
+            processed["attention_mask"] = processed["attention_mask"] + [1]
+            if processed["labels"] is not None:
+                if loss_on_eos:
+                    processed["labels"] = processed["labels"] + [self.eos_token_id]
+                else:
+                    processed["labels"] = processed["labels"] + [-100]
+            if "data_ids" in processed:
+                processed["data_ids"] = processed["data_ids"] + [-1]
+            
+        # if not include_eos:
+        #     # check if EOS token is present
+        #     if processed["input_ids"][-1] == self.eos_token_id:
+        #         # remove EOS token
+        #         processed["input_ids"] = processed["input_ids"][:-1]
+        #         processed["attention_mask"] = processed["attention_mask"][:-1]
+        #         processed["labels"] = processed["labels"][:-1]
+        #         processed["example_inds"] = processed["example_inds"][:-1]
+        #         processed["data_ids"] = processed["data_ids"][:-1]
+        
+        return processed
+    
+    def tokenize_messages_with_loss(
+            self, 
+            messages: List[Dict[str, Any]], 
+            loss_on_eos: bool = False,
+            include_eos: bool = True,
+        ) -> Dict[str, Any]:
+        """
+        Intended for tokenize from messages to tokenized texts with the loss applied.
+        """
+        # First convert messages to text with loss computation flags
+        texts = self.messages_to_loss_texts(messages)
+        
+        # Then tokenize the texts
+        return self.tokenize_loss_texts(texts, loss_on_eos, include_eos = include_eos)
+
+
+# Register tokenizer classes for AutoTokenizer
+AutoTokenizer.register("QwenExplicitTokenizer", slow_tokenizer_class=None, fast_tokenizer_class=QwenExplicitTokenizer)
+
+if __name__ == "__main__":
+    # Example usage
+    # for first load
+    custom_tokenizer = QwenExplicitTokenizer.from_qwen_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
+
+    # Test messages in role/content format
+    test_messages = [
+        [
+            {"role": "description", "content": "This is a test task"},
+            {"role": "input", "content": "What is 2+2?"},
+            {"role": "output", "content": "4"},
+            {"role": "input", "content": "What is 3+3?"},
+        ],
+        [
+            {"role": "description", "content": "This is a test task"},
+            {"role": "output", "content": "4"},
+            {"role": "output", "content": "10"},
+            {"role": "output", "content": "13"},
+        ],
+        [
+            {"role": "output", "content": "4"},
+            {"role": "output", "content": "10"},
+            {"role": "output", "content": "13"},
+        ],
+        [
+            {"role": "input", "content": "What is 2+2?"},
+            {"role": "output", "content": "4"},
+            {"role": "input", "content": "What is 3+3?"},
+            {"role": "output", "content": "10"},
+            {"role": "input", "content": "What is 4+4?"},
+        ],
+    ]
+    for messages in test_messages:
+        # get messages to text_loss
+        texts = custom_tokenizer.messages_to_loss_texts(messages)
+
+        print("Texts with loss flags:")
+        for i, text in enumerate(texts):
+            print(f"  {i}: {text}")
+        processed = custom_tokenizer.tokenize_loss_texts(texts)
+        print(f"\nProcessed:")
+        print(str(processed["input_ids"][:10]) + "..." + str(processed["input_ids"][-10:]))
+
+        text = custom_tokenizer.messages_to_text(messages, start_generation=True)
+        print(f"\nFull text with generation prompt:")
+        print(text)
+        # tokenize messages and print input_ids
+        processed = custom_tokenizer.tokenize_messages(messages, start_generation=True)
+        print(f"\nProcessed:")
+        print(str(processed["input_ids"][:10]) + "..." + str(processed["input_ids"][-10:]))
+    
+    # test messages in chat format
+    test_messages = [
+        [
+            {"role": "user", "content": "What is 2+2?"},
+            {"role": "assistant", "content": "4"},
+        ],
+    ]
+    chat_text = custom_tokenizer.apply_chat_template(test_messages, tokenize=False)[0]
+    print(f"\nChat text:")
+    print(chat_text)
+
+    processed = custom_tokenizer.apply_chat_template(test_messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")[0]
+    print(f"\nProcessed:")
+    print(str(processed[:10]) + "..." + str(processed[-10:]))
+    
+    print("\nTesting save/load cycle:")
+    # Test saving and loading
+    tokenizer_path = "repos/explicit-qwen-tokenizer"
+    custom_tokenizer.save_pretrained(tokenizer_path)
+    print("Tokenizer saved successfully!")
+
+    # also save this file in the tokenizer_path
+    import shutil
+    shutil.copy(__file__, os.path.join(tokenizer_path, "qwen_explicit_tokenizer.py"))
+    print("QwenExplicitTokenizer.py saved successfully!")

special_tokens_map.json

@@ -0,0 +1,31 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
+size 11421896

tokenizer_config.json

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+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "end_string": "<|im_end|>",
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "start_string": "<|im_start|>",
+  "tokenizer_class": "QwenExplicitTokenizer",
+  "unk_token": null,
+  "auto_map": {
+    "AutoTokenizer": [
+      "qwen_explicit_tokenizer.QwenExplicitTokenizer",
+      "qwen_explicit_tokenizer.QwenExplicitTokenizer"
+    ]
+  }
+}

training_args.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fd3c3e79506098b312079a468a96eaee310fd7528559a43912518f666bfa0d3c
+size 7313