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@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+epoch_1/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+epoch_1/step_7762/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+epoch_1/step_11643/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+epoch_1/step_3881/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+epoch_1/step_15524/tokenizer.json filter=lfs diff=lfs merge=lfs -text

Qwen2p5Coder_1p5B_SolRet.md

@@ -0,0 +1,456 @@
+
+# 处理方法
+- 把训练集SolRet_training的query和value做对比学习
+
+
+
+# 参考代码
+```python
+########################################################################### imports
+import os
+import json
+# os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
+import argparse
+import random
+import math
+from time import time
+import numpy as np
+import torch
+from torch.utils.data import DataLoader
+from torch.optim import AdamW
+from torch.optim.lr_scheduler import LambdaLR
+from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
+from transformers.models.qwen2.modeling_qwen2 import Qwen2ForCausalLM as Qwen2ForCausalLMOrig
+from transformers.models.qwen2.modeling_qwen2 import QWEN2_INPUTS_DOCSTRING,_CONFIG_FOR_DOC
+from transformers.utils import add_start_docstrings_to_model_forward, replace_return_docstrings
+from typing import List, Optional, Tuple, Union
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from torch.nn import functional as F
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+from datasets import load_dataset
+import deepspeed
+from peft import LoraConfig, get_peft_model, PeftModel
+from functools import partial
+import pynvml
+
+class print_time:
+    def __init__(self, *desc):
+        self.desc = desc
+
+    def __enter__(self):
+        print(*self.desc)
+        self.t = time()
+
+    def __exit__(self, type, value, traceback):
+        print(f'{time()-self.t:.02f}s')
+# 指定 gpu
+def set_gpus(gpu):
+    torch.cuda.set_device(gpu)
+
+def set_seed(seed):
+    os.environ['PYTHONHASHSEED'] = str(seed)
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+
+def set_cuda(deterministic=True):
+    if torch.cuda.is_available():
+        # 卷积算法确定
+        torch.backends.cudnn.deterministic = deterministic
+    # 设置 torch.backends.cudnn.benchmark=True 将会让程序在开始时花费一点额外时间，为整个网络的每个卷积层搜索最适合它的卷积实现算法，进而实现网络的加速。适用场景是网络结构固定（不是动态变化的），网络的输入形状（包括 batch size，图片大小，输入的通道）是不变的，其实也就是一般情况下都比较适用。反之，如果卷积层的设置一直变化，将会导致程序不停地做优化，反而会耗费更多的时间。
+        torch.backends.cudnn.benchmark = not deterministic
+
+class Qwen2ForCausalLM(Qwen2ForCausalLMOrig):
+    
+    def __init__(self, config):
+        super().__init__(config)
+        retrieve_size=256
+        self.retrieve_proj = nn.Linear(config.hidden_size, retrieve_size, bias=False)
+    
+    @add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, Qwen2ForCausalLM
+
+        >>> model = Qwen2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+
+        ################################################################################################################# 
+        loss_fct = CrossEntropyLoss()
+        # query_feats
+        # value_feats
+        with torch.no_grad():
+            outputs = self.model(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                inputs_embeds=inputs_embeds,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+                cache_position=cache_position,
+            )
+        hidden_states = outputs[0]
+        input_feats = F.normalize(
+            self.retrieve_proj(hidden_states[:, -1, :]), dim=-1
+        )        
+        # 计算第一维度的一半
+        half_size = input_feats.shape[0] // 2
+
+        # 取出前一半
+        query_feats = input_feats[:half_size, :]
+
+        # 取出后一半
+        value_feats = input_feats[half_size:, :]
+
+        sim_t2q = torch.matmul(
+            query_feats, value_feats.permute(1,0)
+        )
+        sim_q2t = torch.matmul(
+            value_feats, query_feats.permute(1,0)
+        )
+        #n*n
+        
+        targets = torch.linspace(0, query_feats.size(0) - 1, query_feats.size(0), dtype=int).to(
+            query_feats.device
+        )
+        loss_itc = loss_fct(sim_t2q, targets) + loss_fct(sim_q2t, targets)
+        # print(f'loss_itc\n{loss_itc}\n\nsim_t2q\n{sim_t2q}\n\nsim_q2t\n{sim_q2t}')
+        
+        return CausalLMOutputWithPast(
+            loss=loss_itc,
+            logits=None,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+# 送入模型前先tokenize
+def tokenize_batch(batch,tokenizer):
+    # 前面半截是query，后面半截是value
+    batch_list=[]
+    batch_list.extend(batch['query'])
+    batch_list.extend(batch['value'])
+        
+    batch_tokenize=tokenizer(batch_list,return_tensors='pt',padding='longest',return_token_type_ids=False)
+    
+    # 手动添加eos token
+    batch_size=batch_tokenize['input_ids'].shape[0]
+    eos_tokens = torch.full((batch_size, 1), tokenizer.eos_token_id, dtype=batch_tokenize['input_ids'].dtype)
+    
+    # 拼接EOS token到input_ids的末尾
+    batch_tokenize['input_ids'] = torch.cat([batch_tokenize['input_ids'], eos_tokens], dim=-1)
+    
+    # 对于attention_mask，我们也需要添加1来表明新添加的EOS token是有效的
+    attention_masks_eos = torch.ones((batch_size, 1), dtype=batch_tokenize['attention_mask'].dtype)
+    batch_tokenize['attention_mask'] = torch.cat([batch_tokenize['attention_mask'], attention_masks_eos], dim=-1)
+
+    return batch_tokenize
+
+# 保存模型，参数
+def save_model_tokenizer_config_params(args,model_engine,tokenizer,training_params_save,epoch,step=None):
+    if step is not None:
+        dir_name=os.path.join(args.save_dir,f'epoch_{epoch}',f'step_{step}')
+    else:
+        dir_name=os.path.join(args.save_dir,f'epoch_{epoch}')
+    model_engine.save_16bit_model(dir_name)
+    tokenizer.save_pretrained(dir_name)
+    model_engine.model.config.save_pretrained(dir_name)
+    # 保存训练参数
+    with open(os.path.join(dir_name,'training_params_save.json'), 'w') as f:
+        json.dump(training_params_save, f, indent=4)
+        
+# 加载数据，并且选择刚好合适的大小
+def load_training_dataset(args, file_folder_path):
+    print('loading dataset \n')
+    # 给定的路径是目录，从文件夹中加载parquet文件
+    if os.path.isdir(file_folder_path):
+        parquet_files=[]
+        for filepath,dirnames,filenames in os.walk(file_folder_path):
+            for filename in filenames:
+                if filename.endswith('parquet'):
+                    fullname = os.path.join(filepath, filename)
+                    parquet_files.append(fullname)
+        ds = load_dataset("parquet", data_files=parquet_files)['train']
+
+    # 给定的路径是文件名，从文件中加载parquet文件
+    elif os.path.isfile(file_folder_path):
+        ds = load_dataset("parquet", data_files=file_folder_path)['train']
+        
+    # shuffle数据集
+    ds = ds.shuffle(seed=args.seed)
+    return ds
+
+# 初始化模型
+def initialize_model_tokenizer(args):
+    tokenizer = AutoTokenizer.from_pretrained(args.model)
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+    tokenizer.padding_side='left'
+    tokenizer.add_bos_token=False
+    tokenizer.add_eos_token=False
+
+    print('initializing model \n')
+
+    config = AutoConfig.from_pretrained(args.model)
+    if args.gradient_checkpointing:
+        config.gradient_checkpointing = True
+    config.use_cache = False
+
+    model = Qwen2ForCausalLM.from_pretrained(args.model, config=config)
+    
+    # if hasattr(model, "enable_input_require_grads"):
+    #     model.enable_input_require_grads()
+    # else:
+    #     def make_inputs_require_grad(module, input, output):
+    #          output.requires_grad_(True)
+
+    #     model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
+
+    model.train()
+    
+    def find_all_linear_names(model):
+        cls = torch.nn.Linear
+        lora_module_names = set()
+        for name, module in model.named_modules():
+            if isinstance(module, cls):
+                names = name.split('.')
+                lora_module_names.add(names[0] if len(names) == 1 else names[-1])
+
+        if 'lm_head' in lora_module_names:  # needed for 16-bit
+            lora_module_names.remove('lm_head')
+        return list(lora_module_names)
+    
+    ###############################################LoRA
+    if LoRA:
+        loraconfig = LoraConfig(
+            r=128,
+            lora_alpha=256,
+            target_modules=find_all_linear_names(model),
+            lora_dropout=0.05,
+            bias="none",
+            task_type="CAUSAL_LM",
+        )
+        model = get_peft_model(model, loraconfig)
+        # model=PeftModel.from_pretrained(model, args.save_dir)
+        
+    # TODO(enijkamp): we need to set this flag twice?
+    #forget all activations during forward and recompute during the backward.
+    if args.gradient_checkpointing:
+        model.gradient_checkpointing_enable()
+
+    return model,tokenizer
+
+def deepspeed_train(args,ds,model,tokenizer):
+    set_seed(args.seed)
+    set_cuda(deterministic=deterministic)    
+    
+    print('initializing deepspeed \n')
+
+    model_parameters = list(filter(lambda p: p.requires_grad, model.parameters()))
+    
+    optimizer=AdamW(model_parameters, lr=lr, betas=(0.9, 0.999),eps=1e-8,weight_decay=0.05)
+    
+    def _get_cosine_schedule_with_warmup_lr_lambda(current_step: int, *, num_warmup_steps: int, num_training_steps: int, num_cycles: float):
+        if current_step < num_warmup_steps:
+            return float(current_step) / float(max(1, num_warmup_steps))
+        progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
+        return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)))
+
+    lr_lambda = partial(
+        _get_cosine_schedule_with_warmup_lr_lambda,
+        num_warmup_steps=100,
+        num_training_steps=num_epochs*steps,
+        num_cycles=0.5,
+    )
+    scheduler=LambdaLR(optimizer, lr_lambda)
+    
+    model_engine, optimizer, dataloader, lr_scheduler = deepspeed.initialize(config=args.deepspeed_config, model=model, model_parameters=model_parameters,training_data=ds,optimizer=optimizer, lr_scheduler=scheduler,)
+    
+    torch.cuda.empty_cache()
+
+    print('starting training \n')
+    training_params_save={
+        'epoch':[],
+        'step':[],
+        'loss':[],
+        'lr':[],
+        'cost_time':[],
+        'total_cost_time':[],
+        'percent':[],
+    }
+    total_start_time=time()
+    for epoch in range(1,num_epochs+1):
+            
+        for step, batch in enumerate(dataloader, start=1):
+            batch=tokenize_batch(batch,tokenizer)
+            start_time=time()
+            batch.to(model_engine.device)
+            loss = model_engine(input_ids=batch['input_ids'],attention_mask=batch['attention_mask']).loss
+            model_engine.backward(loss)
+            model_engine.step()
+            
+            if step % steps_per_print == 0:
+                # 打印训练相关信息
+                percent=step*args.deepspeed_config['train_micro_batch_size_per_gpu']*num_gpus/len(ds)
+                cost_time=time()-start_time
+                total_cost_time=time()-total_start_time
+                print(f'epoch {epoch} step {step} {percent:.2%} loss:{loss:8.3f} time:{cost_time:.2f} total time:{total_cost_time:.2f}\n')
+                
+                meminfo = pynvml.nvmlDeviceGetMemoryInfo(handle)
+                print(f'GPU State --- Free:{meminfo.free/1024**2} Used:{meminfo.used/1024**2} Total:{meminfo.total/1024**2}\n') #总的显存大小（float）
+                
+                utilization = pynvml.nvmlDeviceGetUtilizationRates(handle)
+                print(f'GPU Util --- {utilization.gpu}%\n')  # gpu利用率
+            
+                # 保存训练相关参数
+                training_params_save['epoch'].append(epoch)
+                training_params_save['step'].append(step)
+                training_params_save['loss'].append(float(loss))
+                training_params_save['lr'].append(optimizer.param_groups[0]['lr'])
+                training_params_save['cost_time'].append(round(cost_time,4))
+                training_params_save['total_cost_time'].append(round(total_cost_time,4))
+                training_params_save['percent'].append(round(percent,4))
+
+            
+            # 每隔steps_per_save step保存一次
+            if save_flag and step%steps_per_save==0:
+                save_model_tokenizer_config_params(args,model_engine,tokenizer,training_params_save,epoch,step)
+
+        # 每个epoch保存一次
+        if save_flag:
+            save_model_tokenizer_config_params(args,model_engine,tokenizer,training_params_save,epoch)
+
+if __name__ == '__main__':
+    
+    args=argparse.Namespace()
+    
+    args.seed = 0
+    model_name='Qwen2p5Coder_1p5B'
+    args.model = f'/home/chenzy/models/{model_name}'
+    args.training_dataset = '/home/chenzy/sysu_datasets/SolRet_training'
+    args.save_dir=f'/home/chenzy/models/save/{model_name}_SolRet'
+    
+    ds=load_training_dataset(args, args.training_dataset)
+    
+    lr=1e-4
+    train_micro_batch_size_per_gpu=128
+    gradient_accumulation_steps=1
+    num_gpus=4
+    dataset_len=len(ds)
+    steps=dataset_len//(num_gpus*train_micro_batch_size_per_gpu)
+    # steps=5
+    select_dataset_num=steps*num_gpus*train_micro_batch_size_per_gpu
+    
+    ds=ds.select(range(select_dataset_num))
+    
+    num_epochs=3
+    steps_per_print=25
+    steps_per_save=steps//4
+    args.gradient_checkpointing=True
+    save_flag=True
+    deterministic=True
+    LoRA=False
+    pynvml.nvmlInit()
+    handle = pynvml.nvmlDeviceGetHandleByIndex(0) # 指定显卡号
+
+    model,tokenizer=initialize_model_tokenizer(args)
+    
+    DEEPSPEED_CONFIG = \
+    {
+        # 'fp16': {'enabled': True, 'loss_scale': 0, 'loss_scale_window': 1000, 'initial_scale_power': 12, 'hysteresis': 2, 'min_loss_scale': 1},
+        # 'bf16': {'enabled':True},
+        'fp16': {'enabled':True},
+        # 'optimizer': {'type': 'AdamW', 'params': {'lr': 1e-05, 'betas': [0.9, 0.95], 'eps': 1e-08, 'weight_decay': 0.0}},
+        # 'scheduler': {'type': 'WarmupLR', 'params': {'warmup_min_lr': 0, 'warmup_max_lr': 1e-05, 'warmup_num_steps': 100}},
+        #stage 1: optimizer states 
+        #stage 2: gradients
+        #stage 3: model parameters
+        # 'zero_optimization': {
+        #     'stage': 2,
+        #     #pin_memory improve the throughput
+        #     #device: or none
+        #     # 'offload_optimizer': {'device': 'cpu', 'pin_memory': True},
+        #     # 'offload _param': {'device': 'cpu', 'pin_memory': True},
+        #     #trade off GPU Ram and latency
+        #     'overlap_comm': True,
+        #     # reduce memory fragmentation
+        #     'contiguous_gradients': True,
+        #     # default =1e9, when not using NVMe
+        #     # 'sub_group_size': 1e9,
+        #     # 'reduce_bucket_size': 16777216,
+        #     # 'stage3_prefetch_bucket_size': 15099494.4,
+        #     # 'stage3_param_persistence_threshold': 40960,
+        #     # 'gather_16bit_weights_on_model_save': True,      
+        # },
+
+        'zero_optimization': {'stage': 0},
+        
+        # This is the amount of data samples that leads to one step of model update.train_batch_size must be equal to train_micro_batch_size_per_gpu * gradient_accumulation * number of GPUs
+        #'train_batch_size': train_batch_size,
+        
+        # Batch size to be processed by one GPU in one step (without gradient accumulation).
+        'train_micro_batch_size_per_gpu': train_micro_batch_size_per_gpu,
+        
+        # Number of training steps to accumulate gradients before averaging and applying them. 
+        'gradient_accumulation_steps': gradient_accumulation_steps,
+        'gradient_clipping': 1.0,
+        
+        # Print progress report every N training steps. The report includes the number of training steps, number of skipped optimizer updates
+        'steps_per_print': steps_per_print*5,
+        'wall_clock_breakdown': False,
+        'compression_training': {'weight_quantization': {'shared_parameters': {}, 'different_groups': {}}, 'activation_quantization': {'shared_parameters': {}, 'different_groups': {}}, 'sparse_pruning': {'shared_parameters': {}, 'different_groups': {}}, 'row_pruning': {'shared_parameters': {}, 'different_groups': {}}, 'head_pruning': {'shared_parameters': {}, 'different_groups': {}}, 'channel_pruning': {'shared_parameters': {}, 'different_groups': {}}}
+    }
+    args.deepspeed_config = DEEPSPEED_CONFIG
+
+    deepspeed_train(args=args,ds=ds,model=model,tokenizer=tokenizer)
+```

epoch_1/added_tokens.json

@@ -0,0 +1,24 @@
+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

epoch_1/config.json

@@ -0,0 +1,30 @@
+{
+  "_attn_implementation_autoset": true,
+  "_name_or_path": "/home/chenzy/models/Qwen2p5Coder_1p5B",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 21,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
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+  "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,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
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+  "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,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
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+}

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