commit d925ffd770136738977e311e335cd7f53e462146 Author: ModelHub XC Date: Sun Jul 19 05:57:09 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: lldois/v16_rec_cot_pattern_clean_lr15e6 Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz 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 +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..287f200 --- /dev/null +++ b/README.md @@ -0,0 +1,58 @@ +--- +library_name: transformers +license: other +base_model: OpenOneRec/OneReason-0.8B-pretrain-competition +tags: +- llama-factory +- full +- generated_from_trainer +model-index: +- name: v16_rec_cot_pattern_clean_lr15e6 + results: [] +--- + + + +# v16_rec_cot_pattern_clean_lr15e6 + +This model is a fine-tuned version of [OpenOneRec/OneReason-0.8B-pretrain-competition](https://huggingface.co/OpenOneRec/OneReason-0.8B-pretrain-competition) on the exp_rec_cot_pattern_clean dataset. + +## Model description + +More information needed + +## Intended uses & limitations + +More information needed + +## Training and evaluation data + +More information needed + +## Training procedure + +### Training hyperparameters + +The following hyperparameters were used during training: +- learning_rate: 1.5e-05 +- train_batch_size: 1 +- eval_batch_size: 8 +- seed: 202607162 +- gradient_accumulation_steps: 4 +- total_train_batch_size: 4 +- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments +- lr_scheduler_type: cosine +- lr_scheduler_warmup_steps: 0.03 +- num_epochs: 1 + +### Training results + + + +### Framework versions + +- Transformers 5.6.0 +- Pytorch 2.7.1+cu126 +- Datasets 4.0.0 +- Tokenizers 0.22.2 diff --git a/all_results.json b/all_results.json new file mode 100644 index 0000000..75589ab --- /dev/null +++ b/all_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 1.0, + "total_flos": 3.202384716355461e+17, + "train_loss": 1.559677630303854, + "train_runtime": 5251.4031, + "train_samples_per_second": 0.505, + "train_steps_per_second": 0.126 +} \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..01be9b3 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,89 @@ +{%- if tools %} + {{- '<|im_start|>system\n' }} + {%- if messages[0].role == 'system' %} + {{- messages[0].content + '\n\n' }} + {%- endif %} + {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n" }} + {%- for tool in tools %} + {{- "\n" }} + {{- tool | tojson }} + {%- endfor %} + {{- "\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n<|im_end|>\n" }} +{%- else %} + {%- if messages[0].role == 'system' %} + {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }} + {%- endif %} +{%- endif %} +{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %} +{%- for message in messages[::-1] %} + {%- set index = (messages|length - 1) - loop.index0 %} + {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %} + {%- set ns.multi_step_tool = false %} + {%- set ns.last_query_index = index %} + {%- endif %} +{%- endfor %} +{%- for message in messages %} + {%- if message.content is string %} + {%- set content = message.content %} + {%- else %} + {%- set content = '' %} + {%- endif %} + {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} + {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }} + {%- elif message.role == "assistant" %} + {%- set reasoning_content = '' %} + {%- if message.reasoning_content is string %} + {%- set reasoning_content = message.reasoning_content %} + {%- else %} + {%- if '' in content %} + {%- set reasoning_content = content.split('')[0].rstrip('\n').split('')[-1].lstrip('\n') %} + {%- set content = content.split('')[-1].lstrip('\n') %} + {%- endif %} + {%- endif %} + {%- if loop.index0 > ns.last_query_index %} + {%- if loop.last or (not loop.last and reasoning_content) %} + {{- '<|im_start|>' + message.role + '\n\n' + reasoning_content.strip('\n') + '\n\n\n' + content.lstrip('\n') }} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + content }} + {%- endif %} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + content }} + {%- endif %} + {%- if message.tool_calls %} + {%- for tool_call in message.tool_calls %} + {%- if (loop.first and content) or (not loop.first) %} + {{- '\n' }} + {%- endif %} + {%- if tool_call.function %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n{"name": "' }} + {{- tool_call.name }} + {{- '", "arguments": ' }} + {%- if tool_call.arguments is string %} + {{- tool_call.arguments }} + {%- else %} + {{- tool_call.arguments | tojson }} + {%- endif %} + {{- '}\n' }} + {%- endfor %} + {%- endif %} + {{- '<|im_end|>\n' }} + {%- elif message.role == "tool" %} + {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|im_start|>user' }} + {%- endif %} + {{- '\n\n' }} + {{- content }} + {{- '\n' }} + {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} + {{- '<|im_end|>\n' }} + {%- endif %} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {{- '<|im_start|>assistant\n' }} + {%- if enable_thinking is defined and enable_thinking is false %} + {{- '\n\n\n\n' }} + {%- endif %} +{%- endif %} \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..c4eb20b --- /dev/null +++ b/config.json @@ -0,0 +1,63 @@ +{ + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": null, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": 151643, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": false, + "use_sliding_window": false, + "vocab_size": 176253 +} diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000..cf5bf49 --- /dev/null +++ b/generation_config.json @@ -0,0 +1,12 @@ +{ + "do_sample": true, + "eos_token_id": [ + 151645, + 151643 + ], + "pad_token_id": 151643, + "temperature": 0.6, + "top_k": 20, + "top_p": 0.95, + "transformers_version": "5.6.0" +} diff --git a/model.safetensors b/model.safetensors new file mode 100644 index 0000000..e61d320 --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:84568a6a5b8d259eade6830a7d9827e16493e5b301315dc2b7f5baf2970ddd29 +size 1602902832 diff --git a/runs/Jul08_01-09-14_ubuntu-System-Product-Name/events.out.tfevents.1783444154.ubuntu-System-Product-Name.3834353.0 b/runs/Jul08_01-09-14_ubuntu-System-Product-Name/events.out.tfevents.1783444154.ubuntu-System-Product-Name.3834353.0 new file mode 100644 index 0000000..f72c0b7 --- /dev/null +++ b/runs/Jul08_01-09-14_ubuntu-System-Product-Name/events.out.tfevents.1783444154.ubuntu-System-Product-Name.3834353.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:93dc3935987ecd659cafb7773e482ef7d55a8762d5d591e8e8b0c4a26d97d827 +size 34053 diff --git a/tokenizer.json b/tokenizer.json new file mode 100644 index 0000000..8aee134 --- /dev/null +++ b/tokenizer.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cd4d15f596979aecbc11ba12668cb21c9fb8452ce1235cd9d7878cc950170421 +size 16016554 diff --git a/tokenizer_config.json b/tokenizer_config.json new file mode 100644 index 0000000..9b55322 --- /dev/null +++ b/tokenizer_config.json @@ -0,0 +1,16 @@ +{ + "add_prefix_space": false, + "backend": "tokenizers", + "bos_token": null, + "clean_up_tokenization_spaces": false, + "eos_token": "<|im_end|>", + "errors": "replace", + "is_local": false, + "local_files_only": false, + "model_max_length": 131072, + "pad_token": "<|endoftext|>", + "padding_side": "right", + "split_special_tokens": false, + "tokenizer_class": "Qwen2Tokenizer", + "unk_token": null +} diff --git a/train.log b/train.log new file mode 100644 index 0000000..5ec6f15 --- /dev/null +++ b/train.log @@ -0,0 +1,2409 @@ +/home/ll/llm4rec/demo/LLaMA-Factory/.venv/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. + import pkg_resources +[transformers] warmup_ratio is deprecated and will be removed in v5.2. Use `warmup_steps` instead. +[transformers] `logging_dir` is deprecated and will be removed in v5.2. Please set `TENSORBOARD_LOGGING_DIR` instead. +[INFO|2026-07-08 01:08:23] llamafactory.hparams.parser:523 >> Process rank: 0, world size: 1, device: cuda:0, distributed training: False, compute dtype: torch.bfloat16 +[INFO|configuration_utils.py:771] 2026-07-08 01:08:24,290 >> loading configuration file config.json from cache at /home/ll/.cache/huggingface/hub/models--OpenOneRec--OneReason-0.8B-pretrain-competition/snapshots/5a0a9ec5c601c268bb2300d99baa7a4ad526ab0c/config.json +[INFO|configuration_utils.py:847] 2026-07-08 01:08:24,299 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 151643, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": null, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": true, + "use_sliding_window": false, + "vocab_size": 176253 +} + +[INFO|configuration_utils.py:771] 2026-07-08 01:08:28,949 >> loading configuration file config.json from cache at /home/ll/.cache/huggingface/hub/models--OpenOneRec--OneReason-0.8B-pretrain-competition/snapshots/5a0a9ec5c601c268bb2300d99baa7a4ad526ab0c/config.json +[INFO|configuration_utils.py:847] 2026-07-08 01:08:28,951 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 151643, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": null, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": true, + "use_sliding_window": false, + "vocab_size": 176253 +} + +[INFO|configuration_utils.py:771] 2026-07-08 01:08:29,433 >> loading configuration file config.json from cache at /home/ll/.cache/huggingface/hub/models--OpenOneRec--OneReason-0.8B-pretrain-competition/snapshots/5a0a9ec5c601c268bb2300d99baa7a4ad526ab0c/config.json +[INFO|configuration_utils.py:847] 2026-07-08 01:08:29,436 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 151643, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": null, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": true, + "use_sliding_window": false, + "vocab_size": 176253 +} + +[INFO|2026-07-08 01:08:31] llamafactory.data.loader:144 >> Loading dataset /home/ll/llm4rec/experiments/data/rec_cot_pattern_clean.jsonl... +Setting num_proc from 16 back to 1 for the train split to disable multiprocessing as it only contains one shard. + Generating train split: 0 examples [00:00, ? examples/s] Generating train split: 9503 examples [00:00, 80024.23 examples/s] Generating train split: 21169 examples [00:00, 93639.50 examples/s] Generating train split: 32849 examples [00:00, 97564.58 examples/s] Generating train split: 44466 examples [00:00, 96174.66 examples/s] Generating train split: 51684 examples [00:00, 90488.42 examples/s] + Converting format of dataset (num_proc=16): 0%| | 0/51684 [00:00user +【用户交互历史】： +【2025-04-05】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-04-15】 + --:-- [直播-关注] <|living_begin|> +【2025-04-19】 + --:-- [直播-关注] <|living_begin|> +【2025-04-23】 + --:-- [直播-关注] <|living_begin|> +【2025-05-03】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-05-04】 + --:-- [直播-关注] <|living_begin|> +【2025-05-06】 + --:-- [直播-关注] <|living_begin|> +【2025-05-14】 + --:-- [直播-关注] <|living_begin|> +【2025-05-31】 + --:-- [直播-关注] <|living_begin|> +【2025-06-05】 + --:-- [直播-关注] <|living_begin|> +【2025-06-10】 + --:-- [直播-关注] <|living_begin|> +【2025-06-11】 + --:-- [直播-关注] <|living_begin|> +【2025-06-18】 + --:-- [直播-关注] <|living_begin|> +【2025-07-01】 + --:-- [直播-关注] <|living_begin|> +【2025-07-06】 + --:-- [直播-关注] <|living_begin|> +【2025-07-12】 + --:-- [直播-关注] <|living_begin|> +【2025-07-13】 + --:-- [直播-关注] <|living_begin|> +【2025-07-28】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-08-14】 + --:-- [直播-关注] <|living_begin|> +【2025-09-10】 + --:-- [直播-关注] <|living_begin|> +【2025-09-26】 + --:-- [直播-关注] <|living_begin|> +【2025-10-15】 + --:-- [直播-关注] <|living_begin|> +【2025-10-24】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-26】 + --:-- [直播-关注] <|living_begin|> +【2025-10-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-07】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-12】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-13】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-14】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-16】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-19】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-20】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-22】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-23】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-24】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-25】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-27】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-28】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-29】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-01】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-02】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-03】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-04】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-06】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-08】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-10】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-12】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-15】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-16】 + 13:31 [广告-深度转化] <|ad_begin|> +【2025-12-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 08:01 [广告-深度转化] <|ad_begin|> + 08:02 [广告-深度转化] <|ad_begin|> + 08:09 [广告-深度转化] <|ad_begin|> + 08:12 [广告-深度转化] <|ad_begin|> + 08:18 [广告-深度转化] <|ad_begin|> + 08:55 [广告-深度转化] <|ad_begin|> + 09:08 [广告-深度转化] <|ad_begin|> + 09:23 [广告-深度转化] <|ad_begin|> + 12:57 [广告-深度转化] <|ad_begin|> +【2025-12-18】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 10:22 [广告-深度转化] <|ad_begin|> + 12:26 [广告-深度转化] <|ad_begin|> +【2025-12-19】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 05:19 [广告-深度转化] <|ad_begin|> + 07:59 [广告-深度转化] <|ad_begin|> +【2025-12-20】 + 09:42 [广告-深度转化] <|ad_begin|> +【2025-12-23】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-24】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 08:39 [广告-深度转化] <|ad_begin|> + 08:57 [广告-深度转化] <|ad_begin|> + 08:59 [广告-深度转化] <|ad_begin|> +【2025-12-25】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-27】 + 07:23 [广告-深度转化] <|ad_begin|> +【2025-12-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-30】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-31】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 09:50 [广告-深度转化] <|ad_begin|> +【2026-01-01】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + 02:34 [广告-深度转化] <|ad_begin|> + 06:30 [广告-深度转化] <|ad_begin|> + 06:31 [广告-深度转化] <|ad_begin|> + 08:02 [广告-深度转化] <|ad_begin|> + 09:23 [广告-深度转化] <|ad_begin|> + 12:24 [广告-深度转化] <|ad_begin|> +【2026-01-02】 + 08:46 [广告-深度转化] <|ad_begin|> + 11:47 [广告-深度转化] <|ad_begin|> + 12:15 [广告-深度转化] <|ad_begin|> + 23:26 [广告-深度转化] <|ad_begin|> + 23:27 [广告-深度转化] <|ad_begin|> +【2026-01-03】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 00:21 [广告-深度转化] <|ad_begin|> + 03:25 [广告-深度转化] <|ad_begin|> + 03:27 [广告-深度转化] <|ad_begin|> + 08:36 [广告-深度转化] <|ad_begin|> +【2026-01-04】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + 02:21 [广告-深度转化] <|ad_begin|> + 02:31 [广告-深度转化] <|ad_begin|> + 05:15 [广告-深度转化] <|ad_begin|> + 08:16 [广告-深度转化] <|ad_begin|> + 10:44 [广告-深度转化] <|ad_begin|> +【2026-01-05】 + --:-- [商品-购买] <|prod_begin|> + 02:56 [广告-深度转化] <|ad_begin|> + 03:16 [广告-深度转化] <|ad_begin|> + 03:20 [广告-点击] <|ad_begin|> + 03:24 [广告-深度转化] <|ad_begin|> + 03:24 [广告-点击] <|ad_begin|> + 08:34 [广告-点击] <|ad_begin|> + 08:53 [广告-点击] <|ad_begin|> + 23:34 [广告-点击] <|ad_begin|> + 23:48 [广告-点击] <|ad_begin|> + 23:51 [广告-点击] <|ad_begin|> +【2026-01-06】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 01:24 [广告-点击] <|ad_begin|> + 04:11 [广告-点击] <|ad_begin|> + 09:14 [广告-深度转化] <|ad_begin|> + 09:14 [广告-点击] <|ad_begin|> + 09:46 [广告-点击] <|ad_begin|> + 10:35 [广告-点击] <|ad_begin|> + 10:44 [广告-点击] <|ad_begin|> + 11:19 [广告-点击] <|ad_begin|> + 23:31 [广告-点击] <|ad_begin|> + 23:39 [广告-点击] <|ad_begin|> + 23:43 [广告-点击] <|ad_begin|> + 23:44 [广告-点击] <|ad_begin|> +【2026-01-07】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + 00:24 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 01:21 [广告-点击] <|ad_begin|> + 02:13 [广告-点击] <|ad_begin|> + 02:15 [广告-点击] <|ad_begin|> + 02:16 [广告-点击] <|ad_begin|> +【2026-01-08】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 11:06 [广告-点击] <|ad_begin|> +【2026-01-09】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-11】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2026-01-12】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-13】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-14】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-15】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + 02:41 [广告-点击] <|ad_begin|> + 13:50 [广告-深度转化] <|ad_begin|> + 13:50 [广告-点击] <|ad_begin|> +【2026-01-16】 + --:-- [直播-关注] <|living_begin|> + 00:31 [广告-深度转化] <|ad_begin|> + 00:31 [广告-点击] <|ad_begin|> + 01:39 [广告-深度转化] <|ad_begin|> + 01:39 [广告-点击] <|ad_begin|> +【2026-01-17】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 07:24 [广告-深度转化] <|ad_begin|> + 07:24 [广告-点击] <|ad_begin|> +【2026-01-18】 + --:-- [商品-点击] <|prod_begin|> + 02:56 [广告-深度转化] <|ad_begin|> + 02:56 [广告-点击] <|ad_begin|> + 04:42 [广告-深度转化] <|ad_begin|> + 04:42 [广告-点击] <|ad_begin|> + 10:07 [广告-深度转化] <|ad_begin|> + 10:07 [广告-点击] <|ad_begin|> +【2026-01-19】 + --:-- [商品-点击] <|prod_begin|> + 03:43 [广告-深度转化] <|ad_begin|> + 03:43 [广告-点击] <|ad_begin|> + 23:18 [广告-点击] <|ad_begin|> +【2026-01-20】 + 08:26 [广告-深度转化] <|ad_begin|> + 08:26 [广告-点击] <|ad_begin|> + 08:31 [广告-点击] <|ad_begin|> + 10:18 [广告-点击] <|ad_begin|> +【2026-01-21】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 02:21 [广告-点击] <|ad_begin|> +【2026-01-22】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + 00:51 [视频-收藏/长播] <|video_begin|> + 08:10 [广告-点击] <|ad_begin|> + 10:15 [视频-转发/长播] <|video_begin|> + 10:42 [视频-收藏/长播] <|video_begin|> + 10:51 [视频-点赞/长播] <|video_begin|> + 11:04 [视频-关注/长播] <|video_begin|> + 12:32 [视频-收藏/长播] <|video_begin|> +【2026-01-23】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 04:32 [广告-深度转化] <|ad_begin|> + 04:32 [广告-点击] <|ad_begin|> + 04:48 [视频-点赞/长播] <|video_begin|> + 06:17 [视频-点赞/长播] <|video_begin|> + 06:23 [视频-评论/长播] <|video_begin|> + 22:15 [视频-浏览] <|video_begin|> +【2026-01-24】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 01:20 [广告-深度转化] <|ad_begin|> + 01:20 [广告-点击] <|ad_begin|> + 21:51 [广告-深度转化] <|ad_begin|> + 21:51 [广告-点击] <|ad_begin|> +【2026-01-25】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 00:33 [广告-点击] <|ad_begin|> + 08:05 [视频-转发/长播] <|video_begin|> + 11:18 [视频-转发/长播] <|video_begin|> + 11:28 [视频-点赞/长播] <|video_begin|> + 22:05 [视频-收藏/长播] <|video_begin|> +【2026-01-26】 + 00:37 [视频-点赞/长播] <|video_begin|> + 12:18 [视频-收藏/长播] <|video_begin|> + 12:21 [视频-收藏/长播] <|video_begin|> + 12:46 [视频-转发/收藏/长播] <|video_begin|> + 13:58 [视频-收藏/长播] <|video_begin|> + 22:21 [视频-收藏/长播] <|video_begin|> + 23:08 [视频-收藏/长播] <|video_begin|> +【2026-01-27】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 01:19 [广告-深度转化] <|ad_begin|> + 01:19 [广告-点击] <|ad_begin|> + 02:46 [视频-评论/长播] <|video_begin|> + 10:07 [广告-深度转化] <|ad_begin|> + 10:07 [广告-点击] <|ad_begin|> + 11:44 [视频-转发/长播] <|video_begin|> + 21:51 [视频-点赞/长播] <|video_begin|> + 22:17 [视频-收藏/长播] <|video_begin|> + 23:56 [视频-评论/长播] <|video_begin|> +【2026-01-28】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 04:34 [视频-收藏/长播] <|video_begin|> + 12:39 [广告-点击] <|ad_begin|> + 22:09 [广告-深度转化] <|ad_begin|> + 22:09 [广告-点击] <|ad_begin|> + 22:15 [广告-深度转化] <|ad_begin|> + 22:15 [广告-点击] <|ad_begin|> + 23:16 [视频-评论/长播] <|video_begin|> + 23:21 [视频-评论/长播] <|video_begin|> + 23:21 [视频-评论/长播] <|video_begin|> +【2026-01-29】 + 09:29 [视频-点赞/长播] <|video_begin|> +【2026-01-30】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 06:33 [广告-深度转化] <|ad_begin|> + 06:33 [广告-点击] <|ad_begin|> + 07:46 [广告-深度转化] <|ad_begin|> + 07:46 [广告-点击] <|ad_begin|> + 08:06 [广告-深度转化] <|ad_begin|> + 08:06 [广告-点击] <|ad_begin|> + 08:38 [视频-评论/长播] <|video_begin|> +【2026-01-31】 + --:-- [直播-关注] <|living_begin|> + 00:00 [广告-深度转化] <|ad_begin|> + 00:00 [广告-点击] <|ad_begin|> + 03:05 [广告-深度转化] <|ad_begin|> + 03:05 [广告-点击] <|ad_begin|> + 04:07 [广告-深度转化] <|ad_begin|> + 04:07 [广告-点击] <|ad_begin|> + 09:21 [广告-点击] <|ad_begin|> + 09:46 [视频-关注/长播] <|video_begin|> + 12:12 [视频-点赞/长播] <|video_begin|> + 12:19 [视频-评论/长播] <|video_begin|> + 13:21 [广告-点击] <|ad_begin|> +【2026-02-01】 + --:-- [商品-点击] <|prod_begin|> + 06:15 [广告-深度转化] <|ad_begin|> + 06:15 [广告-点击] <|ad_begin|> + 10:17 [视频-转发/长播] <|video_begin|> +【2026-02-02】 + --:-- [直播-关注] <|living_begin|> + 01:52 [广告-深度转化] <|ad_begin|> + 01:52 [广告-点击] <|ad_begin|> + 03:03 [广告-深度转化] <|ad_begin|> + 03:03 [广告-点击] <|ad_begin|> + 04:31 [视频-评论/长播] <|video_begin|> + 07:25 [视频-评论/长播] <|video_begin|> + 11:50 [视频-收藏/长播] <|video_begin|> + 12:14 [广告-点击] <|ad_begin|> + 13:29 [视频-转发/长播] <|video_begin|> + 13:34 [广告-点击] <|ad_begin|> + 13:37 [广告-点击] <|ad_begin|> + 23:08 [视频-转发/长播] <|video_begin|> +【2026-02-03】 + 01:35 [广告-点击] <|ad_begin|> + 11:45 [广告-点击] <|ad_begin|> + 11:46 [广告-点击] <|ad_begin|> + 22:17 [视频-关注/长播] <|video_begin|> + 22:37 [广告-点击] <|ad_begin|> +【2026-02-04】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 08:34 [视频-收藏/长播] <|video_begin|> + 08:39 [视频-关注/评论/长播] <|video_begin|> + 21:56 [视频-收藏/长播] <|video_begin|> + 22:24 [视频-长播] <|video_begin|> + 22:24 [视频-长播] <|video_begin|> + 22:25 [视频-长播] <|video_begin|> + 22:25 [视频-长播] <|video_begin|> +【2026-02-05】 + 00:21 [视频-长播] <|video_begin|> + 00:35 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 01:25 [视频-长播] <|video_begin|> + 01:25 [视频-长播] <|video_begin|> + 01:31 [视频-长播] <|video_begin|> + 02:29 [视频-长播] <|video_begin|> + 02:29 [视频-长播] <|video_begin|> + 02:43 [视频-长播] <|video_begin|> + 02:43 [视频-长播] <|video_begin|> + 02:43 [视频-长播] <|video_begin|> + 02:43 [视频-长播] <|video_begin|> + 02:47 [视频-长播] <|video_begin|> + 03:31 [视频-长播] <|video_begin|> + 03:31 [视频-长播] <|video_begin|> + 04:39 [广告-深度转化] <|ad_begin|> + 04:39 [广告-点击] <|ad_begin|> + 05:11 [视频-长播] <|video_begin|> + 05:52 [视频-长播] <|video_begin|> + 05:53 [视频-长播] <|video_begin|> + 05:53 [视频-长播] <|video_begin|> + 05:53 [视频-长播] <|video_begin|> + 07:58 [视频-长播] <|video_begin|> + 08:02 [视频-长播] <|video_begin|> + 08:12 [视频-长播] <|video_begin|> + 08:22 [视频-长播] <|video_begin|> + 08:47 [视频-长播] <|video_begin|> + 08:47 [视频-长播] <|video_begin|> + 09:31 [视频-长播] <|video_begin|> + 09:31 [视频-长播] <|video_begin|> + 09:58 [视频-长播] <|video_begin|> + 11:43 [广告-点击] <|ad_begin|> + 11:54 [广告-点击] <|ad_begin|> + 11:55 [广告-点击] <|ad_begin|> + 11:58 [视频-长播] <|video_begin|> + 12:03 [视频-长播] <|video_begin|> + 12:03 [视频-长播] <|video_begin|> + 12:03 [视频-长播] <|video_begin|> + 13:42 [视频-长播] <|video_begin|> + 14:01 [视频-关注/长播] <|video_begin|> + 14:04 [视频-关注/长播] <|video_begin|> + 14:06 [视频-长播] <|video_begin|> + 14:06 [视频-长播] <|video_begin|> + 14:09 [视频-长播] <|video_begin|> + 14:09 [视频-长播] <|video_begin|> + 14:09 [视频-关注/长播] <|video_begin|> + 14:10 [视频-长播] <|video_begin|> + 14:10 [视频-关注/长播] <|video_begin|> + 14:15 [视频-长播] <|video_begin|> + 14:15 [视频-长播] <|video_begin|> + 14:16 [视频-长播] <|video_begin|> + 14:16 [视频-长播] <|video_begin|> + 14:18 [视频-长播] <|video_begin|> + 14:18 [视频-长播] <|video_begin|> + 14:21 [视频-长播] <|video_begin|> + 14:21 [视频-关注/长播] <|video_begin|> + 14:22 [视频-长播] <|video_begin|> +【2026-02-06】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +角色任务：你是一位极端严苛的用户行为数据挖掘专家兼数据格式化专家，请依托以上用户交互历史，针对指定主题提炼具有递进逻辑与内部关联的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 搜索-搜索、视频-浏览、视频-长播、直播-关注、直播-首次打赏、商品-点击、商品-购买、广告-点击、广告-深度转化 +核心判定逻辑（递进性准则）： +按照时间顺序抽取 5 步以内的行为链，要求后续交互B必须是前序交互A在场景需求协同补充、副作用诱发或兴趣细化升级上的延伸——也就是说，B 必须因 A 而产生，严禁提取任何品类平级、内容重复或仅受天气/促销影响形成的并列凑单行为。 +1.场景驱动协同：交互 B 相对于交互 A，必须在同一生活场景与主题类目下形成强需求补完关系。 +2.兴趣因果递进：交互 B 必须由交互 A 引发的副作用或新需求而产生，或者 A 本身就是 B 的诱因。 +3.需求深度细化：在相同主题类目下，交互 B 的需求应较交互 A 更加具体和细致，但不包含多次重复的购买、点击、搜索等行为，以避免节点内容重复。 +请面向以下主题提取行为逻辑链： +主题：从泛化兴趣到专业化聚焦的收藏品认知演化 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从泛化兴趣到专业化聚焦的收藏品认知演化", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +}/no_think<|im_end|> +<|im_start|>assistant + + +{"logic_chain": {"name": "从泛化兴趣到专业化聚焦的收藏品认知演化", "events": [{"date": "2025-12-17", "action": "[广告-深度转化] <|ad_begin|>", "logic": "初始触发：被动接收大量泛化的“老物件上门回收”广告，形成初步认知。"}, {"date": "2026-01-27", "action": "[视频-评论/长播] <|video_begin|>", "logic": "兴趣聚焦：从泛化的“老物件”概念，收敛到对特定品类“古钱币”的价值和历史产生主动兴趣，并参与评论互动。"}, {"date": "2026-01-27", "action": "[视频-评论/长播] <|video_begin|>", "logic": "认知跃迁：兴趣从“物品本身”深化至“专业交易场景”，关注点转向“拍卖会”这一专业变现或投资渠道，完成了从了解到实践的认知闭环。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-04-26】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-11】 + --:-- [直播-打赏] <|living_begin|> +【2025-05-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-21】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-26】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-05-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-26】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-26】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-12】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-10-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-03】 + --:-- [直播-关注] <|living_begin|> +【2025-11-04】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-09】 + --:-- [直播-关注] <|living_begin|> +【2025-11-12】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-13】 + --:-- [直播-关注] <|living_begin|> +【2025-11-14】 + --:-- [直播-关注] <|living_begin|> +【2025-11-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-16】 + --:-- [直播-关注] <|living_begin|> +【2025-11-18】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-20】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-21】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-24】 + --:-- [直播-关注] <|living_begin|> +【2025-11-25】 + --:-- [直播-关注] <|living_begin|> +【2025-11-27】 + --:-- [直播-关注] <|living_begin|> +【2025-11-28】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-29】 + --:-- [直播-关注] <|living_begin|> +【2025-12-03】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-05】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-09】 + --:-- [直播-关注] <|living_begin|> +【2025-12-10】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-11】 + --:-- [直播-关注] <|living_begin|> +【2025-12-12】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-14】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-16】 + --:-- [直播-关注] <|living_begin|> +【2025-12-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-21】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-24】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-26】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-31】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-04】 + 10:18 [广告-深度转化] <|ad_begin|> + 10:18 [广告-深度转化] <|ad_begin|> + 10:21 [广告-深度转化] <|ad_begin|> + 10:35 [广告-深度转化] <|ad_begin|> +【2026-01-05】 + 03:11 [广告-深度转化] <|ad_begin|> + 03:13 [广告-深度转化] <|ad_begin|> + 03:14 [广告-深度转化] <|ad_begin|> + 03:15 [广告-深度转化] <|ad_begin|> + 03:18 [广告-深度转化] <|ad_begin|> + 03:18 [广告-深度转化] <|ad_begin|> + 07:31 [广告-深度转化] <|ad_begin|> +【2026-01-06】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + 01:43 [广告-深度转化] <|ad_begin|> + 06:43 [广告-深度转化] <|ad_begin|> + 07:45 [广告-深度转化] <|ad_begin|> +【2026-01-07】 + --:-- [商品-购买] <|prod_begin|> + 09:19 [广告-深度转化] <|ad_begin|> + 12:10 [广告-深度转化] <|ad_begin|> +【2026-01-08】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 04:50 [广告-深度转化] <|ad_begin|> + 04:52 [广告-深度转化] <|ad_begin|> + 04:52 [广告-深度转化] <|ad_begin|> + 07:30 [广告-深度转化] <|ad_begin|> + 07:30 [广告-深度转化] <|ad_begin|> + 08:59 [广告-深度转化] <|ad_begin|> +【2026-01-09】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2026-01-10】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + 02:13 [广告-深度转化] <|ad_begin|> +【2026-01-11】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-12】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + 05:01 [广告-深度转化] <|ad_begin|> + 05:34 [广告-深度转化] <|ad_begin|> + 09:31 [广告-深度转化] <|ad_begin|> +【2026-01-13】 + 02:24 [广告-深度转化] <|ad_begin|> + 07:15 [广告-深度转化] <|ad_begin|> + 07:16 [广告-深度转化] <|ad_begin|> +【2026-01-14】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2026-01-15】 + --:-- [商品-购买] <|prod_begin|> + 09:11 [广告-深度转化] <|ad_begin|> + 09:11 [广告-深度转化] <|ad_begin|> + 09:13 [广告-深度转化] <|ad_begin|> + 09:31 [广告-深度转化] <|ad_begin|> +【2026-01-16】 + 03:44 [广告-深度转化] <|ad_begin|> + 06:36 [广告-深度转化] <|ad_begin|> +【2026-01-19】 + 04:41 [广告-深度转化] <|ad_begin|> + 04:43 [广告-深度转化] <|ad_begin|> +【2026-01-20】 + 01:12 [广告-深度转化] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-21】 + 04:21 [广告-深度转化] <|ad_begin|> + 07:58 [广告-深度转化] <|ad_begin|> + 07:58 [广告-深度转化] <|ad_begin|> + 09:19 [广告-点击] <|ad_begin|> +【2026-01-22】 + 02:35 [视频-收藏/长播] <|video_begin|> + 02:35 [视频-点赞/长播] <|video_begin|> + 03:11 [广告-深度转化] <|ad_begin|> + 03:11 [广告-点击] <|ad_begin|> + 03:12 [广告-深度转化] <|ad_begin|> + 03:12 [广告-点击] <|ad_begin|> + 03:12 [广告-点击] <|ad_begin|> + 03:54 [视频-转发/收藏/点赞/长播] <|video_begin|> + 04:02 [视频-点赞/长播] <|video_begin|> + 04:02 [视频-点赞/长播] <|video_begin|> + 04:02 [视频-关注/长播] <|video_begin|> + 04:07 [广告-点击] <|ad_begin|> + 04:25 [视频-收藏/长播] <|video_begin|> + 04:27 [视频-关注/长播] <|video_begin|> + 04:32 [视频-点赞/长播] <|video_begin|> + 04:32 [视频-关注/长播] <|video_begin|> + 05:20 [广告-深度转化] <|ad_begin|> + 05:20 [广告-点击] <|ad_begin|> + 05:22 [广告-深度转化] <|ad_begin|> + 05:22 [广告-点击] <|ad_begin|> + 05:24 [广告-点击] <|ad_begin|> + 06:40 [视频-收藏/长播] <|video_begin|> + 07:45 [广告-深度转化] <|ad_begin|> + 07:45 [广告-点击] <|ad_begin|> + 08:45 [广告-点击] <|ad_begin|> +【2026-01-23】 + 03:18 [视频-收藏/长播] <|video_begin|> + 05:43 [视频-关注/长播] <|video_begin|> + 07:46 [广告-深度转化] <|ad_begin|> + 07:46 [广告-点击] <|ad_begin|> + 07:46 [广告-点击] <|ad_begin|> + 09:08 [视频-关注/点赞/长播] <|video_begin|> + 09:59 [视频-点赞/长播] <|video_begin|> + 10:13 [视频-点赞/长播] <|video_begin|> +【2026-01-24】 + 00:07 [视频-收藏/长播] <|video_begin|> + 01:02 [视频-关注/收藏/长播] <|video_begin|> + 01:02 [视频-关注/收藏/长播] <|video_begin|> + 04:53 [广告-点击] <|ad_begin|> + 05:38 [广告-点击] <|ad_begin|> + 07:42 [视频-关注/长播] <|video_begin|> + 07:48 [视频-关注/长播] <|video_begin|> + 08:06 [视频-转发/收藏/长播] <|video_begin|> + 08:06 [视频-关注/点赞/长播] <|video_begin|> + 08:45 [视频-关注/长播] <|video_begin|> + 10:20 [视频-收藏/长播] <|video_begin|> + 10:45 [视频-点赞/长播] <|video_begin|> + 23:01 [视频-转发/长播] <|video_begin|> +【2026-01-25】 + 00:02 [视频-收藏/长播] <|video_begin|> + 06:19 [广告-深度转化] <|ad_begin|> + 06:19 [广告-点击] <|ad_begin|> + 06:32 [广告-点击] <|ad_begin|> +【2026-01-26】 + 00:44 [视频-收藏/长播] <|video_begin|> + 02:07 [广告-深度转化] <|ad_begin|> + 02:07 [广告-点击] <|ad_begin|> + 02:19 [视频-关注/长播] <|video_begin|> + 02:44 [视频-点赞/长播] <|video_begin|> + 07:17 [视频-点赞/长播] <|video_begin|> +【2026-01-27】 + 03:38 [视频-关注/长播] <|video_begin|> + 03:44 [视频-转发/长播] <|video_begin|> + 03:49 [视频-点赞/长播] <|video_begin|> + 03:49 [视频-关注/长播] <|video_begin|> + 04:04 [视频-关注/长播] <|video_begin|> + 04:11 [广告-深度转化] <|ad_begin|> + 04:11 [广告-点击] <|ad_begin|> + 04:11 [广告-点击] <|ad_begin|> + 04:26 [广告-深度转化] <|ad_begin|> + 04:26 [广告-点击] <|ad_begin|> + 04:27 [广告-深度转化] <|ad_begin|> + 04:27 [广告-点击] <|ad_begin|> + 04:27 [广告-点击] <|ad_begin|> + 04:28 [视频-关注/长播] <|video_begin|> + 04:33 [视频-关注/长播] <|video_begin|> + 04:33 [视频-关注/点赞/长播] <|video_begin|> + 04:37 [广告-点击] <|ad_begin|> + 04:53 [视频-点赞/长播] <|video_begin|> + 04:54 [视频-收藏/长播] <|video_begin|> + 05:04 [视频-关注/长播] <|video_begin|> + 09:25 [视频-关注/点赞/长播] <|video_begin|> + 09:34 [视频-收藏/点赞/长播] <|video_begin|> +【2026-01-28】 + 03:40 [广告-深度转化] <|ad_begin|> + 03:40 [广告-点击] <|ad_begin|> + 03:41 [广告-点击] <|ad_begin|> + 04:17 [视频-点赞/长播] <|video_begin|> + 04:19 [广告-深度转化] <|ad_begin|> + 04:19 [广告-点击] <|ad_begin|> + 04:20 [广告-深度转化] <|ad_begin|> + 04:20 [广告-点击] <|ad_begin|> + 04:21 [广告-点击] <|ad_begin|> + 04:24 [广告-深度转化] <|ad_begin|> + 04:24 [广告-点击] <|ad_begin|> + 05:47 [广告-深度转化] <|ad_begin|> + 05:47 [广告-点击] <|ad_begin|> + 05:47 [广告-点击] <|ad_begin|> + 08:48 [视频-关注/转发/长播] <|video_begin|> + 09:50 [广告-点击] <|ad_begin|> + 11:03 [广告-深度转化] <|ad_begin|> + 11:03 [广告-点击] <|ad_begin|> +【2026-01-29】 + 01:32 [视频-点赞/长播] <|video_begin|> + 02:00 [视频-点赞/长播] <|video_begin|> + 10:38 [广告-点击] <|ad_begin|> +【2026-01-30】 + --:-- [直播-关注] <|living_begin|> + 06:58 [视频-点赞/长播] <|video_begin|> +【2026-01-31】 + 01:08 [广告-点击] <|ad_begin|> + 04:11 [广告-点击] <|ad_begin|> + 04:18 [广告-深度转化] <|ad_begin|> + 04:18 [广告-点击] <|ad_begin|> + 04:36 [广告-点击] <|ad_begin|> + 06:09 [广告-点击] <|ad_begin|> + 23:31 [视频-点赞/长播] <|video_begin|> +【2026-02-01】 + 00:35 [广告-点击] <|ad_begin|> + 00:38 [广告-点击] <|ad_begin|> + 00:40 [广告-点击] <|ad_begin|> + 02:51 [广告-深度转化] <|ad_begin|> + 02:51 [广告-点击] <|ad_begin|> + 02:51 [广告-点击] <|ad_begin|> + 04:18 [广告-点击] <|ad_begin|> + 04:43 [广告-点击] <|ad_begin|> + 04:45 [广告-点击] <|ad_begin|> + 05:09 [广告-点击] <|ad_begin|> + 09:12 [广告-点击] <|ad_begin|> + 10:57 [广告-深度转化] <|ad_begin|> + 10:57 [广告-点击] <|ad_begin|> + 23:57 [视频-点赞/长播] <|video_begin|> +【2026-02-02】 + 00:14 [广告-点击] <|ad_begin|> + 01:33 [视频-点赞/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + 09:28 [视频-长播] <|video_begin|> + 23:58 [视频-长播] <|video_begin|> +【2026-02-03】 + 00:05 [视频-长播] <|video_begin|> + 00:33 [视频-长播] <|video_begin|> + 00:33 [视频-点赞/长播] <|video_begin|> + 00:34 [视频-长播] <|video_begin|> + 01:21 [视频-长播] <|video_begin|> + 01:53 [视频-转发/长播] <|video_begin|> + 02:00 [视频-长播] <|video_begin|> + 02:00 [视频-长播] <|video_begin|> + 03:19 [视频-收藏/长播] <|video_begin|> + 03:27 [视频-收藏/长播] <|video_begin|> + 03:32 [视频-长播] <|video_begin|> + 03:37 [视频-长播] <|video_begin|> + 03:42 [视频-长播] <|video_begin|> + 04:15 [视频-点赞/长播] <|video_begin|> + 04:38 [广告-深度转化] <|ad_begin|> + 04:38 [广告-点击] <|ad_begin|> + 04:58 [视频-长播] <|video_begin|> + 04:58 [视频-长播] <|video_begin|> + 04:58 [视频-长播] <|video_begin|> + 07:22 [广告-深度转化] <|ad_begin|> + 07:22 [广告-点击] <|ad_begin|> + 07:23 [广告-点击] <|ad_begin|> + 07:24 [视频-长播] <|video_begin|> + 07:24 [视频-长播] <|video_begin|> + 23:38 [视频-长播] <|video_begin|> + 23:38 [视频-长播] <|video_begin|> + 23:45 [视频-长播] <|video_begin|> + 23:46 [视频-长播] <|video_begin|> + 23:51 [广告-点击] <|ad_begin|> + 23:56 [视频-长播] <|video_begin|> + 23:56 [视频-长播] <|video_begin|> +【2026-02-04】 + 00:09 [视频-长播] <|video_begin|> + 03:26 [视频-长播] <|video_begin|> + 03:26 [视频-长播] <|video_begin|> + 03:36 [广告-点击] <|ad_begin|> + 03:37 [广告-深度转化] <|ad_begin|> + 03:37 [广告-点击] <|ad_begin|> + 03:44 [视频-长播] <|video_begin|> + 04:24 [广告-点击] <|ad_begin|> + 05:06 [视频-长播] <|video_begin|> + 05:09 [广告-深度转化] <|ad_begin|> + 05:09 [广告-点击] <|ad_begin|> + 05:10 [广告-深度转化] <|ad_begin|> + 05:10 [广告-点击] <|ad_begin|> + 05:10 [广告-点击] <|ad_begin|> + 05:43 [视频-收藏/点赞/长播] <|video_begin|> + 05:46 [视频-长播] <|video_begin|> + 05:46 [视频-长播] <|video_begin|> + 08:59 [广告-点击] <|ad_begin|> + 09:00 [视频-点赞/长播] <|video_begin|> + 09:07 [视频-关注/点赞/长播] <|video_begin|> + 09:15 [视频-长播] <|video_begin|> + 09:15 [视频-收藏/点赞/长播] <|video_begin|> + 09:16 [视频-关注/点赞/长播] <|video_begin|> + 23:32 [视频-长播] <|video_begin|> + 23:33 [视频-长播] <|video_begin|> +【2026-02-05】 + 00:10 [视频-长播] <|video_begin|> + 00:10 [视频-点赞/长播] <|video_begin|> + 00:14 [视频-长播] <|video_begin|> + 00:16 [视频-长播] <|video_begin|> + 01:08 [视频-长播] <|video_begin|> + 01:08 [视频-长播] <|video_begin|> + 01:11 [视频-长播] <|video_begin|> + 01:29 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 03:11 [广告-点击] <|ad_begin|> + 03:31 [视频-收藏/长播] <|video_begin|> + 03:35 [视频-点赞/长播] <|video_begin|> + 03:39 [视频-收藏/点赞/长播] <|video_begin|> + 04:00 [视频-长播] <|video_begin|> + 04:47 [视频-长播] <|video_begin|> + 04:53 [视频-长播] <|video_begin|> + 04:54 [视频-长播] <|video_begin|> + 04:54 [视频-长播] <|video_begin|> + 04:54 [视频-长播] <|video_begin|> + 05:02 [视频-长播] <|video_begin|> + 06:17 [广告-点击] <|ad_begin|> + 06:18 [广告-深度转化] <|ad_begin|> + 06:18 [广告-点击] <|ad_begin|> + 06:19 [广告-深度转化] <|ad_begin|> + 06:19 [广告-点击] <|ad_begin|> + 06:20 [广告-深度转化] <|ad_begin|> + 06:20 [广告-点击] <|ad_begin|> + 06:20 [广告-点击] <|ad_begin|> + 08:11 [视频-评论/收藏/长播] <|video_begin|> + 08:22 [视频-长播] <|video_begin|> + 08:33 [视频-长播] <|video_begin|> + 08:43 [视频-长播] <|video_begin|> + 08:51 [视频-长播] <|video_begin|> + 08:59 [广告-深度转化] <|ad_begin|> + 08:59 [广告-点击] <|ad_begin|> + 11:43 [广告-点击] <|ad_begin|> +【2026-02-06】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +角色任务：你是一名极端严苛的用户行为数据挖掘专家和数据格式化专家，请基于以上用户交互历史，围绕指定主题提取具备行为递进关系与内在关联的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 直播-关注、直播-首次打赏、商品-点击、商品-购买、搜索-搜索、视频-浏览、视频-长播、广告-点击、广告-深度转化 +核心判定逻辑（递进性准则）： +按时间顺序提取 5 步以内的行为链，要求后项交互B必须是前项交互A的场景需求协同补充、副作用诱因或兴趣细化升级——即 B 必须是因为 A 才产生的延伸需求，严禁提取任何品类平级、内容重复或仅因天气/促销产生的凑单并列行为。 +1.场景驱动协同：交互 B 与交互 A 必须同属一个生活场景及主题类目，并呈现极强的需求补完性。 +2.兴趣因果递进：交互 B 必须由交互 A 引发的副作用或新需求而产生，或者 A 本身就是 B 的诱因。 +3.需求深度细化：在相同主题类目下，交互 B 的需求应较交互 A 更加具体和细致，但不包含多次重复的购买、点击、搜索等行为，以避免节点内容重复。 +请围绕以下主题提取行为逻辑链： +主题：从泛化健康焦虑到聚焦特定问题的解决方案探索 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从泛化健康焦虑到聚焦特定问题的解决方案探索", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +}/no_think<|im_end|> +<|im_start|>assistant + + +{"logic_chain": {"name": "从泛化健康焦虑到聚焦特定问题的解决方案探索", "events": [{"date": "2025-06-26", "action": "[商品-购买] <|prod_begin|>", "logic": "问题触发与初步尝试：首次购买明确指向“便秘”问题的产品，表明用户开始正视该健康问题，并尝试通过天然食疗方式解决。"}, {"date": "2025-06-29", "action": "[商品-购买] <|prod_begin|>", "logic": "认知升级与路径修正：在购买食疗产品后仅3天，用户迅速转向购买具有明确药理作用的“通便”胶囊。这表明食疗方案可能效果不佳或不及时，促使用户寻求更直接、更强效的药物解决方案，完成了从“调理”到“治疗”的认知跃迁。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2024-10-20】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-21】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-04】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-14】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-02】 + --:-- [直播-打赏] <|living_begin|> +【2025-05-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-07】 + --:-- [直播-关注] <|living_begin|> +【2025-07-10】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-07-16】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-07-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2025-07-18】 + --:-- [直播-关注] <|living_begin|> +【2025-07-19】 + --:-- [直播-关注] <|living_begin|> +【2025-07-21】 + --:-- [直播-关注] <|living_begin|> +【2025-07-22】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-07-26】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-07-28】 + --:-- [直播-关注] <|living_begin|> +【2025-07-31】 + --:-- [直播-关注] <|living_begin|> +【2025-08-02】 + --:-- [直播-关注] <|living_begin|> +【2025-08-04】 + --:-- [直播-关注] <|living_begin|> +【2025-08-14】 + --:-- [直播-关注] <|living_begin|> +【2025-08-19】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-08-22】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-23】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-25】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-08-29】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-08-30】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-08-31】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-09-04】 + --:-- [直播-关注] <|living_begin|> +【2025-09-05】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-08】 + --:-- [直播-关注] <|living_begin|> +【2025-09-16】 + --:-- [直播-关注] <|living_begin|> +【2025-09-23】 + --:-- [直播-打赏] <|living_begin|> +【2025-10-01】 + --:-- [直播-打赏] <|living_begin|> +【2025-10-15】 + --:-- [直播-关注] <|living_begin|> +【2025-10-27】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-10-28】 + --:-- [直播-关注] <|living_begin|> +【2025-10-31】 + --:-- [直播-关注] <|living_begin|> +【2025-11-21】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-14】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-21】 + --:-- [直播-关注] <|living_begin|> +【2025-12-27】 + 02:54 [广告-深度转化] <|ad_begin|> + 03:46 [广告-深度转化] <|ad_begin|> + 05:26 [广告-深度转化] <|ad_begin|> + 08:33 [广告-深度转化] <|ad_begin|> + 10:03 [广告-深度转化] <|ad_begin|> + 11:52 [广告-深度转化] <|ad_begin|> + 14:53 [广告-深度转化] <|ad_begin|> +【2025-12-29】 + 10:01 [广告-深度转化] <|ad_begin|> +【2025-12-31】 + 00:29 [广告-深度转化] <|ad_begin|> + 00:40 [广告-深度转化] <|ad_begin|> +【2026-01-02】 + 09:50 [广告-深度转化] <|ad_begin|> + 11:58 [广告-深度转化] <|ad_begin|> +【2026-01-03】 + 11:44 [广告-深度转化] <|ad_begin|> + 12:09 [广告-深度转化] <|ad_begin|> + 16:00 [广告-深度转化] <|ad_begin|> + 17:05 [广告-深度转化] <|ad_begin|> +【2026-01-04】 + 11:29 [广告-深度转化] <|ad_begin|> + 13:22 [广告-深度转化] <|ad_begin|> + 16:14 [广告-深度转化] <|ad_begin|> + 17:00 [广告-深度转化] <|ad_begin|> +【2026-01-05】 + 10:44 [广告-深度转化] <|ad_begin|> + 13:12 [广告-深度转化] <|ad_begin|> +【2026-01-06】 + 06:49 [广告-深度转化] <|ad_begin|> + 18:38 [广告-深度转化] <|ad_begin|> + 19:15 [广告-深度转化] <|ad_begin|> + 19:41 [广告-深度转化] <|ad_begin|> +【2026-01-07】 + 01:52 [广告-深度转化] <|ad_begin|> + 13:47 [广告-深度转化] <|ad_begin|> +【2026-01-08】 + 05:04 [广告-深度转化] <|ad_begin|> + 16:17 [广告-深度转化] <|ad_begin|> +【2026-01-09】 + --:-- [直播-关注] <|living_begin|> + 08:15 [广告-深度转化] <|ad_begin|> + 08:30 [广告-深度转化] <|ad_begin|> +【2026-01-11】 + 02:48 [广告-深度转化] <|ad_begin|> +【2026-01-12】 + 06:12 [广告-深度转化] <|ad_begin|> + 08:20 [广告-深度转化] <|ad_begin|> +【2026-01-13】 + 05:16 [广告-深度转化] <|ad_begin|> + 08:17 [广告-深度转化] <|ad_begin|> + 12:15 [广告-深度转化] <|ad_begin|> + 12:15 [广告-点击] <|ad_begin|> + 13:24 [广告-深度转化] <|ad_begin|> + 13:24 [广告-点击] <|ad_begin|> +【2026-01-14】 + 05:47 [广告-深度转化] <|ad_begin|> + 05:47 [广告-点击] <|ad_begin|> + 06:32 [广告-深度转化] <|ad_begin|> + 06:32 [广告-点击] <|ad_begin|> + 11:54 [广告-点击] <|ad_begin|> + 12:11 [广告-深度转化] <|ad_begin|> + 12:11 [广告-点击] <|ad_begin|> + 12:43 [广告-点击] <|ad_begin|> + 14:28 [广告-点击] <|ad_begin|> +【2026-01-15】 + 03:23 [广告-点击] <|ad_begin|> + 13:04 [广告-深度转化] <|ad_begin|> + 13:04 [广告-点击] <|ad_begin|> +【2026-01-16】 + 04:24 [广告-深度转化] <|ad_begin|> + 04:24 [广告-点击] <|ad_begin|> + 07:37 [广告-深度转化] <|ad_begin|> + 07:37 [广告-点击] <|ad_begin|> + 16:20 [广告-深度转化] <|ad_begin|> + 16:20 [广告-点击] <|ad_begin|> + 20:35 [广告-点击] <|ad_begin|> +【2026-01-17】 + 01:27 [广告-深度转化] <|ad_begin|> + 01:27 [广告-点击] <|ad_begin|> + 03:32 [广告-深度转化] <|ad_begin|> + 03:32 [广告-点击] <|ad_begin|> + 05:36 [广告-深度转化] <|ad_begin|> + 05:36 [广告-点击] <|ad_begin|> + 07:13 [广告-深度转化] <|ad_begin|> + 07:13 [广告-点击] <|ad_begin|> + 13:13 [广告-点击] <|ad_begin|> + 13:27 [广告-点击] <|ad_begin|> + 13:33 [广告-点击] <|ad_begin|> + 14:47 [广告-深度转化] <|ad_begin|> + 14:47 [广告-点击] <|ad_begin|> +【2026-01-18】 + 08:11 [广告-深度转化] <|ad_begin|> + 08:11 [广告-点击] <|ad_begin|> + 09:23 [广告-点击] <|ad_begin|> +【2026-01-19】 + 11:47 [广告-点击] <|ad_begin|> + 13:37 [广告-深度转化] <|ad_begin|> + 13:37 [广告-点击] <|ad_begin|> +【2026-01-20】 + 04:07 [广告-深度转化] <|ad_begin|> + 04:07 [广告-点击] <|ad_begin|> +【2026-01-21】 + 15:51 [广告-深度转化] <|ad_begin|> + 15:51 [广告-点击] <|ad_begin|> +【2026-01-22】 + 00:28 [广告-深度转化] <|ad_begin|> + 00:28 [广告-点击] <|ad_begin|> + 01:22 [广告-点击] <|ad_begin|> + 04:37 [广告-深度转化] <|ad_begin|> + 04:37 [广告-点击] <|ad_begin|> + 04:51 [广告-点击] <|ad_begin|> +【2026-01-23】 + 03:41 [广告-点击] <|ad_begin|> + 14:46 [广告-点击] <|ad_begin|> + 17:44 [广告-点击] <|ad_begin|> + 20:38 [广告-点击] <|ad_begin|> +【2026-01-24】 + --:-- [商品-购买] <|prod_begin|> + 07:31 [广告-点击] <|ad_begin|> + 08:02 [广告-深度转化] <|ad_begin|> + 08:02 [广告-点击] <|ad_begin|> + 08:12 [广告-点击] <|ad_begin|> + 12:37 [广告-点击] <|ad_begin|> + 15:07 [广告-深度转化] <|ad_begin|> + 15:07 [广告-点击] <|ad_begin|> + 15:24 [广告-点击] <|ad_begin|> +【2026-01-25】 + 04:00 [广告-点击] <|ad_begin|> + 23:17 [广告-点击] <|ad_begin|> +【2026-01-26】 + 07:12 [广告-深度转化] <|ad_begin|> + 07:12 [广告-点击] <|ad_begin|> + 07:36 [广告-深度转化] <|ad_begin|> + 07:36 [广告-点击] <|ad_begin|> + 12:35 [广告-深度转化] <|ad_begin|> + 12:35 [广告-点击] <|ad_begin|> + 18:12 [广告-深度转化] <|ad_begin|> + 18:12 [广告-点击] <|ad_begin|> +【2026-01-27】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 09:33 [广告-深度转化] <|ad_begin|> + 09:33 [广告-点击] <|ad_begin|> + 12:33 [广告-点击] <|ad_begin|> + 16:11 [广告-点击] <|ad_begin|> +【2026-01-28】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 13:03 [广告-点击] <|ad_begin|> + 14:27 [视频-评论/长播] <|video_begin|> + 15:32 [视频-评论/长播] <|video_begin|> +【2026-01-29】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 07:03 [广告-深度转化] <|ad_begin|> + 07:03 [广告-点击] <|ad_begin|> + 15:12 [视频-评论/长播] <|video_begin|> + 15:16 [视频-评论/长播] <|video_begin|> + 16:05 [视频-评论/长播] <|video_begin|> + 16:13 [视频-评论/长播] <|video_begin|> + 16:24 [广告-点击] <|ad_begin|> + 17:46 [视频-转发/长播] <|video_begin|> +【2026-01-30】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 04:18 [广告-点击] <|ad_begin|> +【2026-01-31】 + 05:00 [广告-深度转化] <|ad_begin|> + 05:00 [广告-点击] <|ad_begin|> + 11:46 [广告-点击] <|ad_begin|> + 14:54 [视频-转发/长播] <|video_begin|> + 17:10 [广告-点击] <|ad_begin|> + 17:11 [视频-评论/长播] <|video_begin|> + 17:40 [广告-点击] <|ad_begin|> +【2026-02-01】 + 00:48 [视频-评论/长播] <|video_begin|> + 02:09 [视频-评论/长播] <|video_begin|> + 02:16 [视频-评论/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 04:30 [广告-点击] <|ad_begin|> + 04:44 [广告-深度转化] <|ad_begin|> + 04:44 [广告-点击] <|ad_begin|> + 11:55 [广告-点击] <|ad_begin|> + 14:57 [视频-转发/点赞/长播] <|video_begin|> + 19:22 [视频-评论/长播] <|video_begin|> +【2026-02-02】 + 01:19 [视频-评论/长播] <|video_begin|> + 01:47 [视频-评论/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 05:25 [视频-评论/长播] <|video_begin|> + 05:40 [广告-点击] <|ad_begin|> + 05:57 [广告-点击] <|ad_begin|> + 07:03 [广告-点击] <|ad_begin|> + 07:47 [视频-评论/长播] <|video_begin|> + 08:10 [视频-评论/长播] <|video_begin|> + 12:04 [视频-评论/长播] <|video_begin|> +【2026-02-03】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 06:37 [视频-评论/长播] <|video_begin|> + 06:47 [视频-浏览] <|video_begin|> + 06:54 [视频-评论/长播] <|video_begin|> + 07:01 [视频-评论/长播] <|video_begin|> + 12:33 [广告-深度转化] <|ad_begin|> + 12:33 [广告-点击] <|ad_begin|> + 12:57 [广告-深度转化] <|ad_begin|> + 12:57 [广告-点击] <|ad_begin|> + 13:23 [视频-评论/长播] <|video_begin|> + 14:47 [视频-评论/长播] <|video_begin|> + 15:20 [视频-评论/长播] <|video_begin|> + 16:58 [视频-评论/长播] <|video_begin|> + 17:21 [视频-评论/长播] <|video_begin|> + 17:39 [视频-浏览] <|video_begin|> +【2026-02-04】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 20:43 [视频-评论/长播] <|video_begin|> + 20:53 [视频-评论/长播] <|video_begin|> + 21:10 [视频-评论/长播] <|video_begin|> + 21:18 [视频-评论/长播] <|video_begin|> + 21:25 [视频-评论/长播] <|video_begin|> + 21:35 [视频-评论/长播] <|video_begin|> + 21:39 [视频-评论/长播] <|video_begin|> + 21:44 [视频-评论/长播] <|video_begin|> + 21:52 [视频-评论/长播] <|video_begin|> + 21:52 [视频-评论/长播] <|video_begin|> + 21:59 [视频-评论/长播] <|video_begin|> + 22:12 [视频-评论/长播] <|video_begin|> + 23:19 [广告-点击] <|ad_begin|> + 23:52 [视频-浏览] <|video_begin|> +【2026-02-05】 + 00:37 [视频-长播] <|video_begin|> + 00:41 [视频-长播] <|video_begin|> + 00:41 [视频-长播] <|video_begin|> + 00:41 [视频-长播] <|video_begin|> + 01:08 [视频-收藏/长播] <|video_begin|> + 01:25 [视频-长播] <|video_begin|> + 01:31 [视频-长播] <|video_begin|> + 01:31 [视频-长播] <|video_begin|> + 01:36 [视频-评论/长播] <|video_begin|> + 01:38 [视频-长播] <|video_begin|> + 01:38 [视频-长播] <|video_begin|> + 01:54 [视频-评论/长播] <|video_begin|> + 01:54 [视频-长播] <|video_begin|> + 01:54 [视频-长播] <|video_begin|> + 02:06 [视频-评论/长播] <|video_begin|> + 02:06 [视频-长播] <|video_begin|> + 02:16 [视频-评论/长播] <|video_begin|> + 02:25 [视频-评论/长播] <|video_begin|> + 02:25 [视频-长播] <|video_begin|> + 02:25 [视频-长播] <|video_begin|> + 02:30 [视频-长播] <|video_begin|> + 02:40 [视频-长播] <|video_begin|> + 02:43 [视频-长播] <|video_begin|> + 02:43 [视频-长播] <|video_begin|> + 02:47 [视频-长播] <|video_begin|> + 02:51 [视频-评论/长播] <|video_begin|> + 03:11 [视频-评论/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 03:32 [视频-长播] <|video_begin|> + 03:32 [视频-长播] <|video_begin|> + 03:37 [视频-长播] <|video_begin|> + 03:55 [视频-长播] <|video_begin|> + 03:55 [视频-长播] <|video_begin|> + 04:01 [视频-评论/长播] <|video_begin|> + 04:01 [视频-评论/长播] <|video_begin|> + 04:01 [视频-长播] <|video_begin|> + 04:09 [视频-评论/长播] <|video_begin|> + 04:09 [视频-长播] <|video_begin|> + 04:17 [视频-长播] <|video_begin|> + 04:53 [视频-长播] <|video_begin|> + 04:53 [视频-长播] <|video_begin|> + 05:45 [视频-长播] <|video_begin|> + 05:59 [视频-长播] <|video_begin|> + 05:59 [视频-长播] <|video_begin|> + 05:59 [视频-长播] <|video_begin|> + 06:13 [视频-长播] <|video_begin|> + 06:13 [视频-长播] <|video_begin|> + 06:32 [视频-评论/长播] <|video_begin|> + 06:32 [视频-长播] <|video_begin|> + 06:39 [视频-长播] <|video_begin|> + 06:45 [视频-长播] <|video_begin|> + 07:01 [视频-长播] <|video_begin|> + 07:01 [视频-长播] <|video_begin|> + 07:07 [视频-评论/长播] <|video_begin|> + 07:07 [视频-长播] <|video_begin|> + 07:22 [视频-长播] <|video_begin|> + 07:22 [视频-长播] <|video_begin|> + 07:22 [视频-长播] <|video_begin|> + 07:34 [视频-评论/长播] <|video_begin|> + 07:34 [视频-长播] <|video_begin|> + 07:34 [视频-长播] <|video_begin|> + 07:41 [视频-长播] <|video_begin|> + 07:41 [视频-长播] <|video_begin|> + 07:57 [视频-长播] <|video_begin|> + 07:57 [视频-长播] <|video_begin|> + 08:10 [广告-点击] <|ad_begin|> + 13:01 [广告-深度转化] <|ad_begin|> + 13:01 [广告-点击] <|ad_begin|> +角色任务：你需要以极端严苛的用户行为数据挖掘专家及数据格式化专家身份，根据以上用户交互历史，针对给定主题抽取存在行为递进性与内在联系的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 广告-点击、广告-深度转化、视频-浏览、视频-长播、搜索-搜索、商品-点击、商品-购买、直播-关注、直播-首次打赏 +核心逻辑要求（递进性准则）： +请按时间顺序提取 5 步以内的行为链，且后项交互B必须构成前项交互A在场景需求上的协同补充、副作用诱因或兴趣细化升级——即 B 必须源于 A 所延伸出的需求，严禁提取任何品类平级、内容重复或仅由天气/促销触发的并列凑单行为。 +1.场景驱动协同：交互 B 和交互 A 必须落在同一生活场景、同一主题类目中，且存在很强的需求补完关系。 +2.兴趣因果递进：交互 B 应是交互 A 带来的副作用需求或新增需求的结果，或者 A 对 B 具有明确诱发作用。 +3.需求深度细化：在同一主题类目下，交互 B 的需求必须比交互 A 更具体、更细化，但不包括重复出现的购买、点击、搜索等行为，避免节点内容重复。 +请根据以下主题提取行为逻辑链： +主题：从被动接收信息到主动探索知识的兴趣演化 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从被动接收信息到主动探索知识的兴趣演化", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +}/no_think<|im_end|> +<|im_start|>assistant + + +{"logic_chain": {"name": "从被动接收信息到主动探索知识的兴趣演化", "events": [{"date": "2026-01-26", "action": "[广告-深度转化] <|ad_begin|>", "logic": "初始阶段：用户在2025年底至2026年1月期间，被动且大量地深度转化/点击了内容相似的短剧广告（如穿越、逆袭、宫斗题材），行为模式为被动信息接收。"}, {"date": "2026-01-29", "action": "[视频-转发/长播] <|video_begin|>", "logic": "行为转折：用户首次出现主动的、带有知识探索性质的“转发”行为，内容是关于“机械门锁原理”的硬核科普。这标志着用户的兴趣从被动的剧情消费，演化为对具体事物工作原理的主动探究，交互深度和认知意图均发生显著变化。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2024-10-03】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-06】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-21】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-29】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-01】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-07】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-21】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-12-01】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-22】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-23】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2024-12-31】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-02】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-01-04】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-01-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-12】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-18】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-17】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-05-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-31】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-18】 + --:-- [直播-关注] <|living_begin|> +【2025-11-25】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-26】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-03】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-05】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-06】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-07】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-08】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-14】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-15】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-16】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-19】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-21】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-22】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-24】 + 07:35 [广告-深度转化] <|ad_begin|> + 07:55 [广告-深度转化] <|ad_begin|> +【2025-12-25】 + 04:12 [广告-深度转化] <|ad_begin|> + 04:13 [广告-深度转化] <|ad_begin|> + 04:34 [广告-深度转化] <|ad_begin|> + 04:37 [广告-深度转化] <|ad_begin|> + 04:44 [广告-深度转化] <|ad_begin|> + 04:47 [广告-深度转化] <|ad_begin|> + 04:50 [广告-深度转化] <|ad_begin|> + 05:15 [广告-深度转化] <|ad_begin|> + 05:59 [广告-深度转化] <|ad_begin|> + 06:09 [广告-深度转化] <|ad_begin|> + 06:12 [广告-深度转化] <|ad_begin|> + 06:26 [广告-深度转化] <|ad_begin|> +【2025-12-26】 + 01:21 [广告-深度转化] <|ad_begin|> +【2025-12-29】 + 05:36 [广告-深度转化] <|ad_begin|> + 05:48 [广告-深度转化] <|ad_begin|> + 11:13 [广告-深度转化] <|ad_begin|> +【2025-12-30】 + 03:05 [广告-深度转化] <|ad_begin|> + 06:31 [广告-深度转化] <|ad_begin|> + 12:16 [广告-深度转化] <|ad_begin|> +【2025-12-31】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-01】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-02】 + 02:32 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-03】 + 14:37 [广告-深度转化] <|ad_begin|> +【2026-01-04】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-05】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-06】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-07】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-08】 + 02:45 [广告-深度转化] <|ad_begin|> +【2026-01-09】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-10】 + 07:21 [广告-深度转化] <|ad_begin|> + 07:26 [广告-深度转化] <|ad_begin|> + 07:44 [广告-深度转化] <|ad_begin|> + 10:40 [广告-深度转化] <|ad_begin|> +【2026-01-11】 + 03:48 [广告-深度转化] <|ad_begin|> + 04:20 [广告-深度转化] <|ad_begin|> + 05:35 [广告-深度转化] <|ad_begin|> + 09:40 [广告-深度转化] <|ad_begin|> +【2026-01-13】 + 12:14 [广告-深度转化] <|ad_begin|> + 12:19 [广告-深度转化] <|ad_begin|> + 13:07 [广告-深度转化] <|ad_begin|> +【2026-01-14】 + 16:02 [广告-深度转化] <|ad_begin|> +【2026-01-15】 + 05:54 [广告-深度转化] <|ad_begin|> +【2026-01-17】 + 13:28 [广告-深度转化] <|ad_begin|> +【2026-01-18】 + 05:16 [广告-深度转化] <|ad_begin|> + 06:00 [广告-深度转化] <|ad_begin|> +【2026-01-21】 + 07:07 [广告-深度转化] <|ad_begin|> +【2026-01-22】 + 07:19 [广告-深度转化] <|ad_begin|> + 08:00 [广告-深度转化] <|ad_begin|> + 08:10 [广告-深度转化] <|ad_begin|> + 08:13 [广告-深度转化] <|ad_begin|> + 08:23 [广告-深度转化] <|ad_begin|> + 08:32 [广告-深度转化] <|ad_begin|> + 08:46 [广告-深度转化] <|ad_begin|> + 09:28 [广告-深度转化] <|ad_begin|> +【2026-01-23】 + 03:24 [广告-深度转化] <|ad_begin|> + 03:36 [广告-深度转化] <|ad_begin|> +【2026-01-24】 + 13:25 [广告-深度转化] <|ad_begin|> +【2026-01-29】 + 14:51 [广告-深度转化] <|ad_begin|> + 15:01 [广告-点击] <|ad_begin|> + 15:03 [广告-深度转化] <|ad_begin|> + 15:03 [广告-点击] <|ad_begin|> + 15:41 [广告-点击] <|ad_begin|> + 15:43 [广告-点击] <|ad_begin|> +【2026-01-30】 + 01:45 [广告-点击] <|ad_begin|> + 01:50 [广告-点击] <|ad_begin|> + 02:30 [广告-点击] <|ad_begin|> + 02:41 [广告-深度转化] <|ad_begin|> + 02:41 [广告-点击] <|ad_begin|> + 02:45 [广告-深度转化] <|ad_begin|> + 02:45 [广告-点击] <|ad_begin|> + 03:09 [广告-深度转化] <|ad_begin|> + 03:09 [广告-点击] <|ad_begin|> + 04:11 [广告-点击] <|ad_begin|> + 04:17 [广告-点击] <|ad_begin|> + 04:19 [广告-点击] <|ad_begin|> + 04:49 [广告-点击] <|ad_begin|> + 05:07 [广告-点击] <|ad_begin|> + 05:08 [广告-点击] <|ad_begin|> + 05:09 [广告-点击] <|ad_begin|> + 06:29 [广告-深度转化] <|ad_begin|> + 06:29 [广告-点击] <|ad_begin|> + 06:49 [广告-深度转化] <|ad_begin|> + 06:49 [广告-点击] <|ad_begin|> + 07:01 [广告-点击] <|ad_begin|> +【2026-01-31】 + 02:32 [广告-深度转化] <|ad_begin|> + 02:32 [广告-点击] <|ad_begin|> + 04:21 [广告-点击] <|ad_begin|> + 04:22 [广告-点击] <|ad_begin|> + 04:23 [广告-点击] <|ad_begin|> + 08:43 [广告-点击] <|ad_begin|> + 09:04 [广告-点击] <|ad_begin|> + 09:17 [广告-点击] <|ad_begin|> + 09:28 [广告-深度转化] <|ad_begin|> + 09:28 [广告-点击] <|ad_begin|> + 09:30 [广告-点击] <|ad_begin|> +【2026-02-01】 + 06:30 [广告-点击] <|ad_begin|> + 07:22 [广告-深度转化] <|ad_begin|> + 07:22 [广告-点击] <|ad_begin|> + 07:27 [广告-点击] <|ad_begin|> +【2026-02-02】 + 04:29 [广告-点击] 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<|video_begin|> + 02:33 [广告-点击] <|ad_begin|> + 02:43 [视频-长播] <|video_begin|> + 03:22 [广告-点击] <|ad_begin|> + 03:23 [广告-点击] <|ad_begin|> + 03:25 [广告-点击] <|ad_begin|> + 03:25 [广告-点击] <|ad_begin|> + 03:26 [广告-点击] <|ad_begin|> +【2026-02-04】 + 02:07 [视频-长播] <|video_begin|> + 02:07 [视频-长播] <|video_begin|> + 02:07 [视频-长播] <|video_begin|> + 02:10 [广告-点击] <|ad_begin|> + 02:12 [广告-点击] <|ad_begin|> + 02:16 [广告-点击] <|ad_begin|> + 02:19 [广告-点击] <|ad_begin|> + 02:33 [视频-长播] <|video_begin|> + 02:42 [广告-点击] <|ad_begin|> + 03:07 [视频-长播] <|video_begin|> + 03:26 [视频-长播] <|video_begin|> + 03:34 [视频-长播] <|video_begin|> + 03:34 [视频-长播] <|video_begin|> + 03:45 [视频-长播] <|video_begin|> + 04:09 [视频-长播] <|video_begin|> + 04:09 [视频-长播] <|video_begin|> + 04:09 [视频-长播] <|video_begin|> + 04:09 [视频-长播] <|video_begin|> + 04:40 [视频-收藏/长播] <|video_begin|> + 04:57 [视频-长播] <|video_begin|> + 05:22 [广告-深度转化] <|ad_begin|> + 05:22 [广告-点击] <|ad_begin|> + 05:28 [广告-深度转化] <|ad_begin|> + 05:28 [广告-点击] <|ad_begin|> + 05:34 [视频-长播] <|video_begin|> + 05:48 [广告-点击] <|ad_begin|> + 05:49 [广告-点击] <|ad_begin|> + 09:27 [视频-长播] <|video_begin|> + 09:29 [广告-点击] <|ad_begin|> + 09:31 [广告-点击] <|ad_begin|> +【2026-02-05】 + 05:29 [广告-点击] <|ad_begin|> + 05:30 [广告-点击] <|ad_begin|> + 05:31 [广告-点击] <|ad_begin|> + 05:32 [广告-点击] <|ad_begin|> + 05:38 [视频-长播] <|video_begin|> + 05:49 [广告-深度转化] <|ad_begin|> + 05:49 [广告-点击] <|ad_begin|> + 06:00 [视频-长播] <|video_begin|> + 06:00 [视频-长播] <|video_begin|> + 06:00 [视频-长播] <|video_begin|> + 06:01 [视频-长播] <|video_begin|> + 06:01 [视频-长播] <|video_begin|> + 06:05 [视频-长播] <|video_begin|> + 06:05 [视频-长播] <|video_begin|> + 06:16 [视频-长播] <|video_begin|> + 06:16 [视频-长播] <|video_begin|> + 06:21 [视频-长播] <|video_begin|> + 06:21 [视频-长播] <|video_begin|> + 06:26 [视频-长播] <|video_begin|> + 06:28 [广告-点击] <|ad_begin|> + 06:29 [视频-长播] <|video_begin|> + 06:44 [广告-深度转化] <|ad_begin|> + 06:44 [广告-点击] <|ad_begin|> + 06:48 [视频-长播] <|video_begin|> + 06:49 [广告-点击] <|ad_begin|> + 06:52 [视频-长播] <|video_begin|> + 06:52 [视频-长播] <|video_begin|> + +角色任务：你是一位极端严苛的用户行为数据挖掘专家兼数据格式化专家，请依托以上用户交互历史，针对指定主题，严格提取出所有相关的历史交互条目。 + +主题：从被动接收健康科普到主动筛选特定功效产品的决策路径 + +输出格式要求：请严格只输出包含 SID 字段的 JSON 数组格式，禁止输出任何其他补充说明或解释性文本。/no_think<|im_end|> +<|im_start|>assistant + + +["<|prod_begin|>", "<|prod_begin|>", "<|prod_begin|>", "<|prod_begin|>", "<|prod_begin|>", "<|living_begin|>", "<|prod_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>"]<|im_end|> +<|im_start|>user +你是一位商品理解专家，可以根据商品描述生成准确的商品token。 +请根据给定的商品内容描述生成商品token：《人民检察院民事行政检察案例选（第25集）》是法律实务类专业书籍，由胡卫列主编，中国检察出版社出版，开本为16开，定价48元，当前售价35.59元。封面采用浅蓝色底色搭配深蓝色标题与简洁天平图案，设计简约专业，突出司法公正主题。本书精选最高人民检察院民事行政检察厅编撰的典型案例，涵盖定金罚则、婚内财产归属、网络技术公司合规监管等实务热点，内容权威实用，适合法律从业者、检察官、法学专业学生及司法研究者阅读参考。作为司法案例与实务解析类书籍，本书旨在提升读者对民事行政检察实务的理解与应用能力，是法律实务工作的重要参考资料。/think<|im_end|> +<|im_start|>assistant +提取了书籍的法律实务属性、权威案例内容、专业受众群体以及司法公正的主题风格。 +<|prod_begin|><|im_end|> +<|endoftext|> +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 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-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 151667, 107439, 34187, 107041, 9370, 100376, 118603, 79256, 5373, 105058, 102206, 43815, 5373, 99878, 112138, 104553, 101034, 104341, 106496, 109151, 104040, 1773, 151668, 198, 176247, 152699, 167107, 173487, 151645, 198, -100][INFO|configuration_utils.py:771] 2026-07-08 01:09:12,367 >> loading configuration file config.json from cache at /home/ll/.cache/huggingface/hub/models--OpenOneRec--OneReason-0.8B-pretrain-competition/snapshots/5a0a9ec5c601c268bb2300d99baa7a4ad526ab0c/config.json +[INFO|configuration_utils.py:847] 2026-07-08 01:09:12,368 >> Model config Qwen3Config { + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 151643, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": null, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": true, + "use_sliding_window": false, + "vocab_size": 176253 +} + + +labels: + + +{"logic_chain": {"name": "从泛化兴趣到专业化聚焦的收藏品认知演化", "events": [{"date": "2025-12-17", "action": "[广告-深度转化] <|ad_begin|>", "logic": "初始触发：被动接收大量泛化的“老物件上门回收”广告，形成初步认知。"}, {"date": "2026-01-27", "action": "[视频-评论/长播] <|video_begin|>", "logic": "兴趣聚焦：从泛化的“老物件”概念，收敛到对特定品类“古钱币”的价值和历史产生主动兴趣，并参与评论互动。"}, {"date": "2026-01-27", "action": "[视频-评论/长播] <|video_begin|>", "logic": "认知跃迁：兴趣从“物品本身”深化至“专业交易场景”，关注点转向“拍卖会”这一专业变现或投资渠道，完成了从了解到实践的认知闭环。"}]}}<|im_end|> + + +{"logic_chain": {"name": "从泛化健康焦虑到聚焦特定问题的解决方案探索", "events": [{"date": "2025-06-26", "action": "[商品-购买] <|prod_begin|>", "logic": "问题触发与初步尝试：首次购买明确指向“便秘”问题的产品，表明用户开始正视该健康问题，并尝试通过天然食疗方式解决。"}, {"date": "2025-06-29", "action": "[商品-购买] <|prod_begin|>", "logic": "认知升级与路径修正：在购买食疗产品后仅3天，用户迅速转向购买具有明确药理作用的“通便”胶囊。这表明食疗方案可能效果不佳或不及时，促使用户寻求更直接、更强效的药物解决方案，完成了从“调理”到“治疗”的认知跃迁。"}]}}<|im_end|> + + +{"logic_chain": {"name": "从被动接收信息到主动探索知识的兴趣演化", "events": [{"date": "2026-01-26", "action": "[广告-深度转化] <|ad_begin|>", "logic": "初始阶段：用户在2025年底至2026年1月期间，被动且大量地深度转化/点击了内容相似的短剧广告（如穿越、逆袭、宫斗题材），行为模式为被动信息接收。"}, {"date": "2026-01-29", "action": "[视频-转发/长播] <|video_begin|>", "logic": "行为转折：用户首次出现主动的、带有知识探索性质的“转发”行为，内容是关于“机械门锁原理”的硬核科普。这标志着用户的兴趣从被动的剧情消费，演化为对具体事物工作原理的主动探究，交互深度和认知意图均发生显著变化。"}]}}<|im_end|> + + +["<|prod_begin|>", "<|prod_begin|>", "<|prod_begin|>", "<|prod_begin|>", "<|prod_begin|>", "<|living_begin|>", "<|prod_begin|>", "<|ad_begin|>", "<|ad_begin|>", "<|ad_begin|>"]<|im_end|> +提取了书籍的法律实务属性、权威案例内容、专业受众群体以及司法公正的主题风格。 +<|prod_begin|><|im_end|> + +[INFO|2026-07-08 01:09:12] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +[INFO|2026-07-08 01:09:12] llamafactory.model.model_utils.liger_kernel:144 >> Liger kernel has been applied to the model. +[INFO|modeling_utils.py:751] 2026-07-08 01:09:13,155 >> loading weights file model.safetensors from cache at /home/ll/.cache/huggingface/hub/models--OpenOneRec--OneReason-0.8B-pretrain-competition/snapshots/5a0a9ec5c601c268bb2300d99baa7a4ad526ab0c/model.safetensors +[INFO|modeling_utils.py:821] 2026-07-08 01:09:13,155 >> Will use dtype=torch.bfloat16 as defined in model's config object +[INFO|configuration_utils.py:1027] 2026-07-08 01:09:13,207 >> Generate config GenerationConfig { + "bos_token_id": 151643, + "eos_token_id": 151645, + "output_attentions": false, + "output_hidden_states": false, + "use_cache": false +} + + Loading weights: 0%| | 0/311 [00:00> loading configuration file generation_config.json from cache at /home/ll/.cache/huggingface/hub/models--OpenOneRec--OneReason-0.8B-pretrain-competition/snapshots/5a0a9ec5c601c268bb2300d99baa7a4ad526ab0c/generation_config.json +[INFO|configuration_utils.py:1027] 2026-07-08 01:09:14,094 >> Generate config GenerationConfig { + "bos_token_id": 151643, + "do_sample": true, + "eos_token_id": [ + 151645, + 151643 + ], + "pad_token_id": 151643, + "temperature": 0.6, + "top_k": 20, + "top_p": 0.95 +} + +[INFO|dynamic_module_utils.py:406] 2026-07-08 01:09:14,459 >> Could not locate the custom_generate/generate.py inside OpenOneRec/OneReason-0.8B-pretrain-competition. +[INFO|2026-07-08 01:09:14] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-07-08 01:09:14] llamafactory.model.model_utils.attention:144 >> Using FlashAttention-2 for faster training and inference. +[INFO|2026-07-08 01:09:14] llamafactory.model.adapter:144 >> Pure bf16 / BAdam detected, remaining trainable params in half precision. +[INFO|2026-07-08 01:09:14] llamafactory.model.adapter:144 >> Fine-tuning method: Full +[INFO|2026-07-08 01:09:14] llamafactory.model.loader:144 >> trainable params: 801,433,600 || all params: 801,433,600 || trainable%: 100.0000 +[WARNING|trainer_utils.py:1238] 2026-07-08 01:09:14,483 >> The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'bos_token_id': None, 'pad_token_id': 151643}. +[INFO|trainer.py:1469] 2026-07-08 01:09:14,615 >> ***** Running training ***** +[INFO|trainer.py:1470] 2026-07-08 01:09:14,615 >> Num examples = 2,654 +[INFO|trainer.py:1471] 2026-07-08 01:09:14,615 >> Num Epochs = 1 +[INFO|trainer.py:1472] 2026-07-08 01:09:14,615 >> Num update steps per epoch = 664 +[INFO|trainer.py:1473] 2026-07-08 01:09:14,615 >> Instantaneous batch size per device = 1 +[INFO|trainer.py:1476] 2026-07-08 01:09:14,615 >> Total train batch size (w. parallel, distributed & accumulation) = 4 +[INFO|trainer.py:1477] 2026-07-08 01:09:14,615 >> Gradient Accumulation steps = 4 +[INFO|trainer.py:1478] 2026-07-08 01:09:14,615 >> Total optimization steps = 664 +[INFO|trainer.py:1479] 2026-07-08 01:09:14,616 >> Number of trainable parameters = 801,433,600 + 0%| | 0/664 [00:00> `use_return_dict` is deprecated! 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Do not forget to share your model on huggingface.co/models =) + + + 100%|██████████| 664/664 [1:27:31<00:00, 6.40s/it] 100%|██████████| 664/664 [1:27:31<00:00, 7.91s/it] +[INFO|trainer.py:3815] 2026-07-08 02:36:46,020 >> Saving model checkpoint to /home/ll/llm4rec/experiments/outputs/v16_rec_cot_pattern_clean_lr15e6 +[INFO|configuration_utils.py:533] 2026-07-08 02:36:46,021 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v16_rec_cot_pattern_clean_lr15e6/config.json +[INFO|configuration_utils.py:816] 2026-07-08 02:36:46,021 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v16_rec_cot_pattern_clean_lr15e6/generation_config.json + +{'loss': '1.47', 'grad_norm': '2.016', 'learning_rate': '3.145e-06', 'epoch': '0.7084'} +{'loss': '1.457', 'grad_norm': '1.969', 'learning_rate': '2.997e-06', 'epoch': '0.7159'} +{'loss': '1.427', 'grad_norm': '2.438', 'learning_rate': '2.852e-06', 'epoch': '0.7234'} +{'loss': '1.441', 'grad_norm': '2.047', 'learning_rate': 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'epoch': '0.8139'} +{'loss': '1.43', 'grad_norm': '1.875', 'learning_rate': '1.249e-06', 'epoch': '0.8214'} +{'loss': '1.441', 'grad_norm': '2.109', 'learning_rate': '1.15e-06', 'epoch': '0.8289'} +{'loss': '1.493', 'grad_norm': '2.234', 'learning_rate': '1.054e-06', 'epoch': '0.8365'} +{'loss': '1.518', 'grad_norm': '1.914', 'learning_rate': '9.625e-07', 'epoch': '0.844'} +{'loss': '1.513', 'grad_norm': '2.234', 'learning_rate': '8.748e-07', 'epoch': '0.8515'} +{'loss': '1.474', 'grad_norm': '2.062', 'learning_rate': '7.911e-07', 'epoch': '0.8591'} +{'loss': '1.445', 'grad_norm': '2.203', 'learning_rate': '7.113e-07', 'epoch': '0.8666'} +{'loss': '1.453', 'grad_norm': '2.203', 'learning_rate': '6.356e-07', 'epoch': '0.8742'} +{'loss': '1.496', 'grad_norm': '1.969', 'learning_rate': '5.639e-07', 'epoch': '0.8817'} +{'loss': '1.447', 'grad_norm': '2.328', 'learning_rate': '4.964e-07', 'epoch': '0.8892'} +{'loss': '1.487', 'grad_norm': '1.969', 'learning_rate': '4.33e-07', 'epoch': '0.8968'} +{'loss': '1.465', 'grad_norm': '2.172', 'learning_rate': '3.739e-07', 'epoch': '0.9043'} +{'loss': '1.454', 'grad_norm': '2.234', 'learning_rate': '3.19e-07', 'epoch': '0.9118'} +{'loss': '1.442', 'grad_norm': '1.969', 'learning_rate': '2.683e-07', 'epoch': '0.9194'} +{'loss': '1.464', 'grad_norm': '2.469', 'learning_rate': '2.22e-07', 'epoch': '0.9269'} +{'loss': '1.483', 'grad_norm': '2.453', 'learning_rate': '1.8e-07', 'epoch': '0.9344'} +{'loss': '1.478', 'grad_norm': '2.188', 'learning_rate': '1.423e-07', 'epoch': '0.942'} +{'loss': '1.516', 'grad_norm': '1.961', 'learning_rate': '1.091e-07', 'epoch': '0.9495'} +{'loss': '1.437', 'grad_norm': '2.109', 'learning_rate': '8.017e-08', 'epoch': '0.957'} +{'loss': '1.47', 'grad_norm': '2.234', 'learning_rate': '5.571e-08', 'epoch': '0.9646'} +{'loss': '1.505', 'grad_norm': '2.031', 'learning_rate': '3.567e-08', 'epoch': '0.9721'} +{'loss': '1.512', 'grad_norm': '2.281', 'learning_rate': '2.007e-08', 'epoch': '0.9797'} +{'loss': '1.472', 'grad_norm': '2.75', 'learning_rate': '8.922e-09', 'epoch': '0.9872'} +{'loss': '1.43', 'grad_norm': '2.016', 'learning_rate': '2.231e-09', 'epoch': '0.9947'} +{'train_runtime': '5251', 'train_samples_per_second': '0.505', 'train_steps_per_second': '0.126', 'train_loss': '1.56', 'epoch': '1'} + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/outputs/v16_rec_cot_pattern_clean_lr15e6/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-08 02:36:47,012 >> chat template saved in /home/ll/llm4rec/experiments/outputs/v16_rec_cot_pattern_clean_lr15e6/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-08 02:36:47,034 >> tokenizer config file saved in /home/ll/llm4rec/experiments/outputs/v16_rec_cot_pattern_clean_lr15e6/tokenizer_config.json +***** train metrics ***** + epoch = 1.0 + total_flos = 298245317GF + train_loss = 1.5597 + train_runtime = 1:27:31.40 + train_samples_per_second = 0.505 + train_steps_per_second = 0.126 +Figure saved at: /home/ll/llm4rec/experiments/outputs/v16_rec_cot_pattern_clean_lr15e6/training_loss.png +[WARNING|2026-07-08 02:36:47] llamafactory.extras.ploting:149 >> No metric eval_loss to plot. +[WARNING|2026-07-08 02:36:47] llamafactory.extras.ploting:149 >> No metric eval_accuracy to plot. +[INFO|modelcard.py:263] 2026-07-08 02:36:47,136 >> Dropping the following result as it does not have all the necessary fields: +{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}} diff --git a/train_results.json b/train_results.json new file mode 100644 index 0000000..75589ab --- /dev/null +++ b/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 1.0, + "total_flos": 3.202384716355461e+17, + "train_loss": 1.559677630303854, + "train_runtime": 5251.4031, + "train_samples_per_second": 0.505, + "train_steps_per_second": 0.126 +} \ No newline at end of file diff --git a/trainer_log.jsonl b/trainer_log.jsonl new file mode 100644 index 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