From 44005dff9b31bc3230f632cb05519e1a5106e350 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Thu, 3 Sep 2026 15:11:19 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: lldois/v48_v29_clean_fused_lr45e7_ep040 Source: Original Platform --- .gitattributes | 36 + README.md | 58 + all_results.json | 8 + chat_template.jinja | 89 + config.json | 63 + experiment_recipe.json | 121 + generation_config.json | 12 + model.safetensors | 3 + ...37200.ubuntu-System-Product-Name.1409680.0 | 3 + tokenizer.json | 3 + tokenizer_config.json | 16 + train.log | 2256 +++++++++++++++++ train_results.json | 8 + trainer_log.jsonl | 4 + trainer_state.json | 64 + training_args.bin | 3 + training_loss.png | Bin 0 -> 40913 bytes 17 files changed, 2747 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 all_results.json create mode 100644 chat_template.jinja create mode 100644 config.json create mode 100644 experiment_recipe.json create mode 100644 generation_config.json create mode 100644 model.safetensors create mode 100644 runs/Jul16_01-40-00_ubuntu-System-Product-Name/events.out.tfevents.1784137200.ubuntu-System-Product-Name.1409680.0 create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json create mode 100644 train.log create mode 100644 train_results.json create mode 100644 trainer_log.jsonl create mode 100644 trainer_state.json create mode 100644 training_args.bin create mode 100644 training_loss.png 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 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+*.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..bbddbfb --- /dev/null +++ b/README.md @@ -0,0 +1,58 @@ +--- +library_name: transformers +license: other +base_model: lldois/v29_v19_user_world_guard_lr8e7_ep018 +tags: +- llama-factory +- full +- generated_from_trainer +model-index: +- name: v48_v29_clean_fused_lr45e7_ep040 + results: [] +--- + + + +# v48_v29_clean_fused_lr45e7_ep040 + +This model is a fine-tuned version of [lldois/v29_v19_user_world_guard_lr8e7_ep018](https://huggingface.co/lldois/v29_v19_user_world_guard_lr8e7_ep018) on the exp_v48_clean_fused 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: 4.5e-07 +- train_batch_size: 1 +- eval_batch_size: 8 +- seed: 202607198 +- 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: 0.4 + +### 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..b732e94 --- /dev/null +++ b/all_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 0.40634920634920635, + "total_flos": 1.5185329017526272e+16, + "train_loss": 2.147665500640869, + "train_runtime": 258.8642, + "train_samples_per_second": 0.487, + "train_steps_per_second": 0.124 +} \ 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/experiment_recipe.json b/experiment_recipe.json new file mode 100644 index 0000000..61c1a66 --- /dev/null +++ b/experiment_recipe.json @@ -0,0 +1,121 @@ +{ + "created_at": "2026-07-16 01:45:38", + "name": "v48_v29_clean_fused_lr45e7_ep040", + "base": "/home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018", + "dataset": "v48_clean_fused", + "dataset_manifest": { + "name": "v48_clean_fused", + "path": "/home/ll/llm4rec/experiments/data/v48_clean_fused.jsonl", + "records": 11600, + "bytes": 45339493, + "sha256": "d080d8786abbd5f20116c97bfee6d8d073bcecb38143880985452fe0fe503af2", + "requested_components": { + "general_cn_direct": 800, + "general_cn_think": 400, + "general_other_direct": 800, + "general_other_think": 400, + "r0_ground_direct": 1000, + "r0_caption_direct": 400, + "r0_caption_think": 250, + "r3_balanced_ad_direct": 600, + "r3_balanced_ad_think": 200, + "r3_balanced_live_direct": 400, + "r3_balanced_live_think": 500, + "r3_balanced_goods_direct": 250, + "r3_balanced_goods_think": 500, + "r3_balanced_video_direct": 550, + "r3_balanced_video_think": 600, + "old_rec_live_direct": 250, + "old_rec_live_think": 100, + "old_rec_ad_direct": 250, + "old_rec_ad_think": 100, + "old_rec_goods_direct": 250, + "old_rec_goods_think": 100, + "old_rec_video_direct": 250, + "old_rec_video_think": 150, + "old_user_array_direct": 450, + "old_user_logic_direct": 400, + "old_user_logic_think": 150, + "old_item_goods_direct": 300, + "old_item_goods_think": 75, + "old_item_live_direct": 300, + "old_item_live_think": 75, + "old_item_ad_direct": 300, + "old_item_ad_think": 75, + "old_item_video_direct": 300, + "old_item_video_think": 75 + }, + "groups": { + "old_user_clean": 1000, + "r0_clean": 1650, + "general_clean": 2400, + "r3_clean": 3600, + "old_rec_clean": 1450, + "old_item_clean": 1500 + }, + "variants": { + "old_user_logic_direct": 400, + "caption_to_sid_collision_free": 1000, + "strict_single_turn_direct": 1600, + "strict_ad_direct": 600, + "old_rec_video_cot": 150, + "old_rec_live_direct": 250, + "sid_to_caption_grounded_cot": 250, + "old_rec_video_direct": 250, + "old_rec_goods_direct": 250, + "strict_goods_cot": 500, + "old_item_ad_direct": 300, + "old_user_logic_cot": 150, + "strict_goods_direct": 250, + "strict_single_turn_cot": 800, + "sid_to_caption_direct": 400, + "old_item_goods_direct": 300, + "strict_video_cot": 600, + "old_rec_ad_direct": 250, + "strict_live_direct": 400, + "old_item_video_direct": 300, + "strict_live_cot": 500, + "old_item_live_cot": 75, + "old_item_live_direct": 300, + "old_rec_ad_cot": 100, + "old_user_array_direct": 450, + "old_rec_goods_cot": 100, + "old_rec_live_cot": 100, + "strict_ad_cot": 200, + "strict_video_direct": 550, + "old_item_goods_cot": 75, + "old_item_ad_cot": 75, + "old_item_video_cot": 75 + }, + "routes": { + "no_think": 7850, + "think": 3750 + }, + "domains": { + "ad": 1654, + "goods": 2148, + "video": 2764, + "live": 1634 + }, + "canonical_duplicates": 0, + "output_chars_p50": 190, + "output_chars_p90": 1219, + "seed": 202607255 + }, + "learning_rate": "4.5e-7", + "epochs": 0.4, + "stages": null, + "note": "Fused high-confidence candidate combining clean general, collision-free R0, domain-aware strict R3, and deduplicated original-task guards.", + "cot_policy": "Preserve meaningful CoT for /think and pure final outputs for /no_think.", + "checkpoint_policy": "save_strategy=no; only the final model is retained.", + "script": "/home/ll/llm4rec/experiments/run_experiments.py", + "script_sha256": "78d5f1619c271f19b4204ff4fa210ff4bbbadd81cdc0221e5f40cee201fc9877", + "data_builder": "/home/ll/llm4rec/experiments/source_data.py", + "data_builder_sha256": "a689f0bf36e31f612c3800f971501655b403e38420f4aad9116a3110206a1d5c", + "data_cleaner": "/home/ll/llm4rec/experiments/clean_data.py", + "data_cleaner_sha256": "c1831a3d85c86c740ba80e095252574495b06d79d6341ed190ac2e3fe67133bf", + "configs": [ + "/home/ll/llm4rec/experiments/configs/v48_v29_clean_fused_lr45e7_ep040.yaml" + ], + "reproduce": "/home/ll/llm4rec/demo/LLaMA-Factory/.venv/bin/python /home/ll/llm4rec/experiments/run_experiments.py --single v48_v29_clean_fused_lr45e7_ep040 --gpu " +} \ No newline at end of file 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, + 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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..35d445f --- /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": true, + "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..def2b48 --- /dev/null +++ b/train.log @@ -0,0 +1,2256 @@ +[2026-07-16 01:39:36] stage 1/1: /home/ll/llm4rec/experiments/configs/v48_v29_clean_fused_lr45e7_ep040.yaml +/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|2026-07-16 01:39:39] llamafactory.hparams.parser:523 >> Process rank: 0, world size: 1, device: cuda:0, distributed training: False, compute dtype: torch.bfloat16 +[INFO|configuration_utils.py:769] 2026-07-16 01:39:39,139 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-16 01:39:39,142 >> Model config Qwen3Config { + "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 +} + +[INFO|configuration_utils.py:769] 2026-07-16 01:39:39,835 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-16 01:39:39,836 >> Model config Qwen3Config { + "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 +} + +[INFO|configuration_utils.py:769] 2026-07-16 01:39:39,865 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-16 01:39:39,866 >> Model config Qwen3Config { + "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 +} + +[INFO|2026-07-16 01:39:40] llamafactory.data.loader:144 >> Loading dataset /home/ll/llm4rec/experiments/data/v48_clean_fused.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: 11600 examples [00:00, 215187.64 examples/s] + Converting format of dataset (num_proc=16): 0%| | 0/11600 [00:00user +【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|> 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--:-- [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|> 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<|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" + } + ] + } +} + +强约束：最终答案必须是一个可被 json.loads 解析的 JSON，不能包含 Markdown、解释、重复键或 JSON 外额外字符。/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】： +【2025-08-16】 + --:-- [直播-关注] <|living_begin|> +【2025-09-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-08】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-09-17】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-09-18】 + --:-- [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|> +【2025-09-19】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-09-25】 + --:-- [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|> +【2025-09-26】 + --:-- [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|> +【2025-09-30】 + --:-- [商品-点击] <|prod_begin|> +【2025-10-05】 + --:-- [商品-点击] <|prod_begin|> +【2025-10-09】 + --:-- [商品-点击] <|prod_begin|> +【2025-10-10】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-10-11】 + --:-- [商品-点击] <|prod_begin|> +【2025-10-12】 + --:-- [商品-点击] <|prod_begin|> +【2025-10-13】 + --:-- [直播-关注] <|living_begin|> +【2025-10-14】 + --:-- [商品-点击] <|prod_begin|> +【2025-10-16】 + --:-- [商品-点击] <|prod_begin|> +【2025-10-19】 + --:-- [商品-点击] <|prod_begin|> +【2025-10-20】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-10-28】 + --:-- [商品-点击] <|prod_begin|> +【2025-10-29】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-05】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-11-08】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-09】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-10】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-11-16】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-11-17】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-20】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-21】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-17】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-12-28】 + 14:54 [广告-深度转化] <|ad_begin|> + 15:43 [广告-深度转化] <|ad_begin|> +【2025-12-29】 + 01:44 [广告-深度转化] <|ad_begin|> + 02:59 [广告-深度转化] <|ad_begin|> + 09:27 [广告-深度转化] <|ad_begin|> + 12:16 [广告-深度转化] <|ad_begin|> + 15:17 [广告-深度转化] <|ad_begin|> +【2025-12-30】 + 01:40 [广告-深度转化] <|ad_begin|> + 02:39 [广告-深度转化] <|ad_begin|> + 05:21 [广告-深度转化] <|ad_begin|> +【2025-12-31】 + 18:18 [广告-深度转化] <|ad_begin|> + 18:41 [广告-深度转化] <|ad_begin|> + 18:42 [广告-深度转化] <|ad_begin|> +【2026-01-01】 + 16:56 [广告-深度转化] <|ad_begin|> +【2026-01-02】 + --:-- [直播-关注] <|living_begin|> + 14:28 [广告-深度转化] <|ad_begin|> + 16:32 [广告-深度转化] <|ad_begin|> + 18:06 [广告-深度转化] <|ad_begin|> +【2026-01-03】 + 04:13 [广告-深度转化] <|ad_begin|> + 17:35 [广告-深度转化] <|ad_begin|> +【2026-01-04】 + 04:41 [广告-深度转化] <|ad_begin|> + 17:22 [广告-深度转化] <|ad_begin|> +【2026-01-05】 + 13:00 [广告-深度转化] <|ad_begin|> + 18:17 [广告-深度转化] <|ad_begin|> +【2026-01-06】 + 13:12 [广告-深度转化] <|ad_begin|> + 15:53 [广告-深度转化] <|ad_begin|> + 16:11 [广告-深度转化] <|ad_begin|> +【2026-01-07】 + 13:31 [广告-深度转化] <|ad_begin|> + 15:52 [广告-深度转化] <|ad_begin|> +【2026-01-08】 + --:-- [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|> + 04:41 [广告-深度转化] <|ad_begin|> + 04:41 [广告-点击] <|ad_begin|> + 04:54 [广告-深度转化] <|ad_begin|> + 04:54 [广告-点击] <|ad_begin|> + 08:58 [广告-深度转化] <|ad_begin|> + 08:58 [广告-点击] <|ad_begin|> + 10:00 [广告-点击] <|ad_begin|> + 11:43 [广告-点击] <|ad_begin|> + 14:24 [广告-点击] <|ad_begin|> + 15:11 [广告-深度转化] <|ad_begin|> + 15:11 [广告-点击] <|ad_begin|> +【2026-01-09】 + 06:09 [广告-点击] <|ad_begin|> + 15:20 [广告-深度转化] <|ad_begin|> + 15:20 [广告-点击] <|ad_begin|> + 16:58 [广告-深度转化] <|ad_begin|> + 16:58 [广告-点击] <|ad_begin|> +【2026-01-10】 + 05:07 [广告-深度转化] <|ad_begin|> + 05:07 [广告-点击] <|ad_begin|> + 05:46 [广告-深度转化] <|ad_begin|> + 05:46 [广告-点击] <|ad_begin|> + 07:19 [广告-深度转化] <|ad_begin|> + 07:19 [广告-点击] <|ad_begin|> + 13:36 [广告-深度转化] <|ad_begin|> + 13:36 [广告-点击] <|ad_begin|> + 14:28 [广告-点击] <|ad_begin|> + 14:32 [广告-深度转化] <|ad_begin|> + 14:32 [广告-点击] <|ad_begin|> + 15:18 [广告-深度转化] <|ad_begin|> + 15:18 [广告-点击] <|ad_begin|> +【2026-01-11】 + 06:02 [广告-深度转化] <|ad_begin|> + 06:02 [广告-点击] <|ad_begin|> + 06:08 [广告-点击] <|ad_begin|> + 06:14 [广告-深度转化] <|ad_begin|> + 06:14 [广告-点击] <|ad_begin|> + 06:23 [广告-深度转化] <|ad_begin|> + 06:23 [广告-点击] <|ad_begin|> + 06:38 [广告-点击] <|ad_begin|> + 06:56 [广告-深度转化] <|ad_begin|> + 06:56 [广告-点击] <|ad_begin|> + 10:42 [广告-点击] <|ad_begin|> + 14:40 [广告-深度转化] <|ad_begin|> + 14:40 [广告-点击] <|ad_begin|> + 14:40 [广告-点击] <|ad_begin|> + 14:42 [广告-点击] <|ad_begin|> +【2026-01-12】 + 03:53 [广告-深度转化] <|ad_begin|> + 03:53 [广告-点击] <|ad_begin|> + 10:03 [广告-点击] <|ad_begin|> + 12:17 [广告-点击] <|ad_begin|> + 12:21 [广告-深度转化] <|ad_begin|> + 12:21 [广告-点击] <|ad_begin|> + 14:07 [广告-深度转化] <|ad_begin|> + 14:07 [广告-点击] <|ad_begin|> + 14:49 [广告-深度转化] <|ad_begin|> + 14:49 [广告-点击] <|ad_begin|> + 14:53 [广告-深度转化] <|ad_begin|> + 14:53 [广告-点击] <|ad_begin|> + 14:55 [广告-点击] <|ad_begin|> + 15:37 [广告-点击] <|ad_begin|> + 16:05 [广告-点击] <|ad_begin|> + 16:59 [广告-深度转化] <|ad_begin|> + 16:59 [广告-点击] <|ad_begin|> +【2026-01-13】 + 02:44 [广告-点击] <|ad_begin|> + 02:52 [广告-深度转化] <|ad_begin|> + 02:52 [广告-点击] <|ad_begin|> + 03:33 [广告-点击] <|ad_begin|> +【2026-01-15】 + 16:18 [广告-点击] <|ad_begin|> +【2026-01-16】 + 02:38 [广告-点击] <|ad_begin|> +【2026-01-17】 + --:-- [商品-点击] <|prod_begin|> + 02:59 [广告-点击] <|ad_begin|> +【2026-01-18】 + 02:16 [广告-点击] <|ad_begin|> + 08:01 [广告-深度转化] <|ad_begin|> + 08:01 [广告-点击] <|ad_begin|> +【2026-01-19】 + 02:06 [广告-深度转化] <|ad_begin|> + 02:06 [广告-点击] <|ad_begin|> + 02:22 [广告-深度转化] <|ad_begin|> + 02:22 [广告-点击] <|ad_begin|> + 02:37 [广告-深度转化] <|ad_begin|> + 02:37 [广告-点击] <|ad_begin|> + 04:08 [广告-深度转化] <|ad_begin|> + 04:08 [广告-点击] <|ad_begin|> + 10:11 [广告-深度转化] <|ad_begin|> + 10:11 [广告-点击] <|ad_begin|> + 15:18 [广告-深度转化] <|ad_begin|> + 15:18 [广告-点击] <|ad_begin|> + 15:36 [广告-深度转化] <|ad_begin|> + 15:36 [广告-点击] <|ad_begin|> + 16:09 [广告-深度转化] <|ad_begin|> + 16:09 [广告-点击] <|ad_begin|> +【2026-01-20】 + 01:31 [广告-深度转化] <|ad_begin|> + 01:31 [广告-点击] <|ad_begin|> + 08:10 [广告-深度转化] <|ad_begin|> + 08:10 [广告-点击] <|ad_begin|> +【2026-01-21】 + 01:25 [广告-深度转化] <|ad_begin|> + 01:25 [广告-点击] <|ad_begin|> + 02:38 [广告-深度转化] <|ad_begin|> + 02:38 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 02:55 [广告-深度转化] <|ad_begin|> + 02:55 [广告-点击] <|ad_begin|> + 03:07 [广告-深度转化] <|ad_begin|> + 03:07 [广告-点击] <|ad_begin|> +【2026-01-22】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 06:51 [视频-收藏/点赞/长播] <|video_begin|> + 11:27 [广告-点击] <|ad_begin|> +【2026-01-23】 + --:-- [商品-点击] <|prod_begin|> + 03:37 [广告-点击] <|ad_begin|> + 04:05 [广告-点击] <|ad_begin|> + 10:20 [视频-收藏/点赞/长播] <|video_begin|> + 15:22 [视频-收藏/长播] <|video_begin|> + 15:50 [视频-关注/长播] <|video_begin|> + 16:39 [视频-收藏/点赞/长播] <|video_begin|> +【2026-01-24】 + --:-- [直播-关注] <|living_begin|> + 06:44 [视频-评论/点赞/长播] <|video_begin|> + 09:43 [视频-收藏/长播] <|video_begin|> + 17:58 [视频-收藏/长播] <|video_begin|> +【2026-01-25】 + 06:48 [视频-收藏/点赞/长播] <|video_begin|> +【2026-01-26】 + 02:44 [视频-评论/点赞/长播] <|video_begin|> + 02:58 [视频-收藏/点赞/长播] <|video_begin|> + 02:58 [视频-评论/点赞/长播] <|video_begin|> + 02:58 [视频-收藏/点赞/长播] <|video_begin|> + 03:00 [视频-收藏/长播] <|video_begin|> + 03:01 [视频-收藏/点赞/长播] <|video_begin|> + 03:05 [视频-收藏/点赞/长播] <|video_begin|> + 03:05 [视频-收藏/点赞/长播] <|video_begin|> +【2026-01-27】 + 03:07 [视频-收藏/点赞/长播] <|video_begin|> + 03:17 [视频-收藏/点赞/长播] <|video_begin|> + 03:32 [视频-收藏/点赞/长播] <|video_begin|> + 03:42 [视频-收藏/点赞/长播] <|video_begin|> + 05:58 [视频-评论/收藏/点赞/长播] <|video_begin|> + 06:11 [广告-深度转化] <|ad_begin|> + 06:11 [广告-点击] <|ad_begin|> +【2026-01-28】 + 06:28 [视频-收藏/点赞/长播] <|video_begin|> + 07:24 [视频-收藏/点赞/长播] <|video_begin|> + 09:43 [广告-深度转化] <|ad_begin|> + 09:43 [广告-点击] <|ad_begin|> +【2026-01-29】 + 07:04 [视频-评论/点赞/长播] <|video_begin|> + 07:22 [视频-收藏/点赞/长播] <|video_begin|> + 17:44 [视频-收藏/点赞/长播] <|video_begin|> + 18:08 [视频-收藏/点赞/长播] <|video_begin|> +【2026-01-30】 + 03:01 [视频-收藏/点赞/长播] <|video_begin|> + 03:05 [广告-点击] <|ad_begin|> + 06:01 [广告-点击] <|ad_begin|> +【2026-01-31】 + 09:53 [视频-收藏/点赞/长播] <|video_begin|> +【2026-02-01】 + 02:40 [视频-收藏/点赞/长播] <|video_begin|> + 06:22 [视频-点赞/长播] <|video_begin|> + 06:22 [视频-点赞/长播] <|video_begin|> + 09:11 [视频-点赞/长播] <|video_begin|> + 09:12 [视频-收藏/点赞/长播] <|video_begin|> + 09:24 [视频-点赞/长播] <|video_begin|> + 09:24 [视频-收藏/点赞/长播] <|video_begin|> + 11:37 [视频-点赞/长播] <|video_begin|> + 14:24 [视频-点赞/长播] <|video_begin|> + 15:24 [视频-点赞/长播] <|video_begin|> + 15:29 [视频-点赞/长播] <|video_begin|> + 16:27 [视频-点赞/长播] <|video_begin|> + 16:31 [视频-点赞/长播] <|video_begin|> + 16:31 [视频-点赞/长播] <|video_begin|> + 16:31 [视频-点赞/长播] <|video_begin|> + 17:27 [视频-点赞/长播] <|video_begin|> +【2026-02-02】 + 01:58 [视频-浏览] <|video_begin|> + 02:01 [视频-收藏/点赞/长播] <|video_begin|> + 02:01 [视频-收藏/点赞/长播] <|video_begin|> + 02:08 [视频-收藏/点赞/长播] <|video_begin|> + 04:57 [视频-点赞/长播] <|video_begin|> + 07:27 [视频-点赞/长播] <|video_begin|> + 07:27 [视频-收藏/点赞/长播] <|video_begin|> + 08:13 [视频-收藏/点赞/长播] <|video_begin|> + 08:56 [视频-点赞/长播] <|video_begin|> + 09:00 [视频-点赞/长播] <|video_begin|> + 10:34 [视频-点赞/长播] <|video_begin|> + 10:43 [视频-收藏/点赞/长播] <|video_begin|> + 10:56 [视频-收藏/点赞/长播] <|video_begin|> + 11:56 [视频-点赞/长播] <|video_begin|> + 11:56 [视频-点赞/长播] <|video_begin|> + 12:32 [视频-收藏/点赞/长播] <|video_begin|> + 17:35 [视频-点赞/长播] <|video_begin|> + 18:08 [视频-点赞/长播] <|video_begin|> +【2026-02-03】 + 01:44 [视频-评论/长播] <|video_begin|> + 03:50 [视频-点赞/长播] <|video_begin|> + 03:50 [视频-点赞/长播] <|video_begin|> + 04:55 [视频-点赞/长播] <|video_begin|> + 04:55 [视频-点赞/长播] <|video_begin|> + 05:01 [广告-点击] <|ad_begin|> + 08:16 [视频-收藏/点赞/长播] <|video_begin|> + 10:33 [视频-收藏/点赞/长播] <|video_begin|> + 10:33 [视频-收藏/点赞/长播] <|video_begin|> + 10:33 [视频-收藏/点赞/长播] <|video_begin|> + 11:18 [视频-点赞/长播] <|video_begin|> + 11:18 [视频-点赞/长播] <|video_begin|> + 11:18 [视频-点赞] <|video_begin|> + 14:43 [视频-收藏/长播] <|video_begin|> + 15:05 [视频-点赞/长播] <|video_begin|> + 18:10 [视频-收藏/点赞/长播] <|video_begin|> +【2026-02-04】 + 03:57 [视频-点赞/长播] <|video_begin|> + 05:06 [视频-点赞/长播] <|video_begin|> + 05:21 [视频-点赞/长播] <|video_begin|> + 05:21 [视频-点赞/长播] <|video_begin|> + 05:37 [视频-点赞/长播] <|video_begin|> + 05:37 [视频-点赞/长播] <|video_begin|> + 06:13 [视频-点赞/长播] <|video_begin|> + 06:33 [视频-点赞/长播] <|video_begin|> + 11:46 [视频-点赞/长播] <|video_begin|> + 12:05 [广告-点击] <|ad_begin|> + 17:25 [广告-深度转化] <|ad_begin|> + 17:25 [广告-点击] <|ad_begin|> +【2026-02-05】 + 01:40 [视频-点赞/长播] <|video_begin|> + 01:40 [视频-点赞/长播] <|video_begin|> + 01:47 [视频-收藏/点赞/长播] <|video_begin|> + 02:45 [视频-评论/点赞/长播] <|video_begin|> + 03:21 [视频-点赞/长播] <|video_begin|> + 03:21 [视频-点赞/长播] <|video_begin|> + 03:24 [视频-长播] <|video_begin|> + 03:57 [视频-收藏/点赞/长播] <|video_begin|> + 04:05 [视频-长播] <|video_begin|> + 04:05 [视频-长播] <|video_begin|> + 04:12 [视频-点赞/长播] <|video_begin|> + 04:12 [视频-点赞/长播] <|video_begin|> + 04:19 [视频-点赞/长播] <|video_begin|> + 04:19 [视频-点赞/长播] <|video_begin|> + 04:19 [视频-长播] <|video_begin|> + 04:22 [视频-长播] <|video_begin|> + 04:28 [视频-长播] <|video_begin|> + 04:36 [视频-点赞/长播] <|video_begin|> + 05:00 [视频-长播] <|video_begin|> + 05:00 [视频-长播] <|video_begin|> + 05:00 [视频-长播] <|video_begin|> + 05:38 [视频-长播] <|video_begin|> + 05:46 [视频-长播] <|video_begin|> + 05:51 [视频-长播] <|video_begin|> + 05:55 [视频-长播] <|video_begin|> + 05:58 [视频-长播] <|video_begin|> + 05:58 [视频-长播] <|video_begin|> + 06:00 [视频-长播] <|video_begin|> + 06:03 [视频-长播] <|video_begin|> + 06:04 [视频-收藏/长播] <|video_begin|> + 06:05 [视频-长播] <|video_begin|> + 06:05 [视频-收藏/点赞/长播] <|video_begin|> + 06:05 [视频-收藏/点赞/长播] <|video_begin|> + 06:06 [视频-长播] <|video_begin|> + 06:06 [视频-长播] <|video_begin|> + 06:06 [视频-长播] <|video_begin|> + 06:09 [视频-收藏/点赞/长播] <|video_begin|> + 06:14 [视频-点赞/长播] <|video_begin|> + 06:30 [视频-长播] <|video_begin|> + 06:30 [视频-长播] <|video_begin|> + 06:33 [视频-点赞/长播] <|video_begin|> + 06:35 [视频-长播] <|video_begin|> + 06:35 [视频-长播] <|video_begin|> + 06:45 [视频-点赞/长播] <|video_begin|> + 06:45 [视频-长播] <|video_begin|> + 06:46 [视频-长播] <|video_begin|> + 06:49 [视频-长播] <|video_begin|> + 06:49 [视频-长播] <|video_begin|> + 07:12 [视频-长播] <|video_begin|> + 07:15 [视频-长播] <|video_begin|> + 07:15 [视频-长播] <|video_begin|> + 07:16 [视频-长播] <|video_begin|> + 07:16 [视频-长播] <|video_begin|> + 08:20 [视频-长播] <|video_begin|> + 08:20 [视频-长播] <|video_begin|> + 08:35 [视频-长播] <|video_begin|> + 08:35 [视频-长播] <|video_begin|> + 08:49 [视频-长播] <|video_begin|> + 09:35 [视频-长播] <|video_begin|> + 09:35 [视频-长播] <|video_begin|> + 09:35 [视频-长播] <|video_begin|> + 09:46 [视频-长播] <|video_begin|> + 10:03 [视频-长播] <|video_begin|> + 10:03 [视频-长播] <|video_begin|> + 10:03 [视频-长播] <|video_begin|> + 10:33 [视频-长播] <|video_begin|> + 11:07 [视频-长播] <|video_begin|> + 12:36 [视频-长播] <|video_begin|> + 13:46 [视频-收藏/点赞/长播] <|video_begin|> + 13:56 [视频-长播] <|video_begin|> +角色任务：你是极端严苛的用户行为数据挖掘专家与数据格式化专家，请依据以上用户交互历史，围绕给定主题提取具备递进关系和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）： 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 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 的需求比 交互 A 更具体详细, 但不包括多次重复的购买、点击、搜索等行为，避免重复节点内容。 +请根据以下主题提取行为逻辑链： +主题：从泛化美妆教程到精细化局部技巧的深度学习 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从泛化美妆教程到精细化局部技巧的深度学习", + "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" + } + ] + } +} + +强约束：最终答案必须是一个可被 json.loads 解析的 JSON，不能包含 Markdown、解释、重复键或 JSON 外额外字符。/no_think<|im_end|> +<|im_start|>assistant +{"logic_chain":{"name":"从泛化美妆教程到精细化局部技巧的深度学习","events":[{"date":"2026-02-05","action":"[视频-长播] <|video_begin|>","logic":"信息输入阶段：用户在当天密集观看了大量美妆相关视频，内容涵盖粉底、遮瑕、假睫毛、美甲等多个方面，属于知识的广泛摄入期。"},{"date":"2026-02-05","action":"[视频-收藏/长播] <|video_begin|>","logic":"认知分化与瓶颈识别：在广泛浏览后，用户开始对“修容”这一更具技术性的特定环节进行“收藏”，表明其识别出这是知识体系中的一个难点或重点，兴趣开始聚焦。"},{"date":"2026-02-05","action":"[视频-长播] <|video_begin|>","logic":"技能深化与工具探索：用户的学习兴趣进一步细化，从面部轮廓（修容）深化到唇部细节（唇线笔），这代表了从宏观妆效向微观技巧的认知升级，开始关注能提升妆容精致度的具体工具和方法。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-02-20】 + --:-- [直播-关注] <|living_begin|> +【2025-02-25】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-02-26】 + --:-- [直播-关注] <|living_begin|> +【2025-02-28】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-03-03】 + --:-- [直播-关注] <|living_begin|> +【2025-03-07】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-03-12】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-03-13】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-03-17】 + --:-- [直播-关注] <|living_begin|> +【2025-03-18】 + --:-- [直播-关注] <|living_begin|> +【2025-03-21】 + --:-- [直播-关注] <|living_begin|> +【2025-03-23】 + --:-- [直播-关注] <|living_begin|> +【2025-03-31】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-04-14】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-04-16】 + --:-- [直播-关注] <|living_begin|> +【2025-04-21】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-04-23】 + --:-- [直播-关注] <|living_begin|> +【2025-04-24】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-05-01】 + --:-- [直播-关注] <|living_begin|> +【2025-05-03】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-05-06】 + --:-- [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|> +【2025-05-08】 + --:-- [直播-关注] <|living_begin|> +【2025-05-10】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-05-16】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-05-19】 + --:-- [直播-关注] <|living_begin|> +【2025-05-23】 + --:-- [直播-关注] <|living_begin|> +【2025-06-06】 + --:-- [直播-关注] <|living_begin|> +【2025-06-07】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2025-06-16】 + --:-- [直播-关注] <|living_begin|> +【2025-06-25】 + --:-- [直播-关注] <|living_begin|> +【2025-06-28】 + --:-- [直播-关注] <|living_begin|> +【2025-07-23】 + --:-- [直播-关注] <|living_begin|> +【2025-07-30】 + --:-- [直播-关注] <|living_begin|> +【2025-08-01】 + --:-- [直播-关注] <|living_begin|> +【2025-08-15】 + --:-- [直播-关注] <|living_begin|> +【2025-08-23】 + --:-- [直播-关注] <|living_begin|> +【2025-08-26】 + --:-- [直播-关注] <|living_begin|> +【2025-08-31】 + --:-- [直播-关注] <|living_begin|> +【2025-10-29】 + --:-- [直播-关注] <|living_begin|> +【2025-10-31】 + --:-- [直播-关注] <|living_begin|> +【2025-11-02】 + --:-- [直播-关注] <|living_begin|> +【2025-11-05】 + --:-- [直播-关注] <|living_begin|> +【2025-11-07】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-18】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-21】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-25】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-26】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-01】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-03】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-05】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-08】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-09】 + 05:42 [广告-深度转化] <|ad_begin|> + 05:42 [广告-点击] <|ad_begin|> +【2025-12-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-11】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + 23:17 [广告-点击] <|ad_begin|> +【2025-12-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-14】 + 00:39 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 23:37 [广告-点击] <|ad_begin|> +【2025-12-16】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-18】 + --:-- [直播-关注] <|living_begin|> +【2025-12-19】 + 14:25 [广告-点击] <|ad_begin|> +【2025-12-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-22】 + 11:43 [广告-点击] <|ad_begin|> +【2025-12-24】 + 12:30 [广告-点击] <|ad_begin|> +【2025-12-26】 + 00:32 [广告-深度转化] <|ad_begin|> + 00:32 [广告-点击] <|ad_begin|> + 06:22 [广告-点击] <|ad_begin|> +【2025-12-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-28】 + --:-- [商品-加购] <|prod_begin|> +【2025-12-30】 + --:-- [商品-购买] <|prod_begin|> + 04:57 [广告-点击] <|ad_begin|> +【2025-12-31】 + --:-- [直播-关注] <|living_begin|> + 11:26 [广告-点击] <|ad_begin|> +【2026-01-02】 + 05:54 [广告-点击] <|ad_begin|> +【2026-01-06】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-09】 + 06:53 [广告-深度转化] <|ad_begin|> + 06:53 [广告-点击] <|ad_begin|> +【2026-01-11】 + --:-- [直播-关注] <|living_begin|> + 02:30 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-12】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-13】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 07:31 [广告-点击] <|ad_begin|> +【2026-01-14】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-15】 + 08:36 [广告-深度转化] <|ad_begin|> + 08:36 [广告-点击] <|ad_begin|> +【2026-01-16】 + 08:13 [广告-深度转化] <|ad_begin|> + 08:13 [广告-点击] <|ad_begin|> +【2026-01-20】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-21】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 15:57 [视频-转发/点赞/长播] <|video_begin|> +【2026-01-22】 + 00:58 [视频-评论/点赞/长播] <|video_begin|> + 01:06 [视频-转发/点赞/长播] <|video_begin|> + 01:06 [视频-关注/点赞/长播] <|video_begin|> + 01:58 [视频-转发/点赞/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + 09:23 [广告-点击] <|ad_begin|> + 09:49 [视频-转发/点赞/长播] <|video_begin|> +【2026-01-23】 + 03:22 [视频-评论/点赞/长播] <|video_begin|> + 03:26 [视频-评论/点赞/长播] <|video_begin|> + 03:41 [视频-评论/点赞/长播] <|video_begin|> + 04:42 [视频-评论/点赞/长播] <|video_begin|> + 04:42 [视频-转发/评论/点赞/长播] <|video_begin|> + 05:50 [视频-长播] <|video_begin|> +【2026-01-24】 + 03:56 [视频-长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + 08:41 [视频-长播] <|video_begin|> + 08:41 [视频-长播] <|video_begin|> + 10:06 [广告-点击] <|ad_begin|> + 15:17 [视频-长播] <|video_begin|> +【2026-01-25】 + 00:49 [视频-长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + 14:33 [广告-点击] <|ad_begin|> +【2026-01-26】 + 04:25 [视频-长播] <|video_begin|> + 04:25 [视频-长播] <|video_begin|> + 04:33 [视频-评论/点赞/长播] <|video_begin|> + 04:33 [视频-长播] <|video_begin|> +【2026-01-27】 + 00:40 [视频-长播] <|video_begin|> + 01:01 [视频-长播] <|video_begin|> + 01:02 [视频-长播] <|video_begin|> + 01:02 [视频-长播] <|video_begin|> + 01:03 [视频-长播] <|video_begin|> + 01:03 [视频-长播] <|video_begin|> + 18:26 [视频-评论/点赞/长播] <|video_begin|> + 18:30 [视频-评论/点赞/长播] <|video_begin|> +【2026-01-28】 + 03:07 [视频-长播] <|video_begin|> + 04:15 [视频-转发/点赞/长播] <|video_begin|> + 04:15 [视频-转发/点赞/长播] <|video_begin|> + 04:16 [视频-长播] <|video_begin|> + 04:20 [广告-点击] <|ad_begin|> + 04:29 [视频-转发/点赞/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-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-29】 + 01:52 [视频-评论/点赞/长播] <|video_begin|> + 05:52 [视频-转发/点赞/长播] <|video_begin|> + 05:52 [视频-长播] <|video_begin|> + 05:55 [视频-长播] <|video_begin|> + 05:59 [视频-长播] <|video_begin|> + 06:06 [视频-转发/点赞/长播] <|video_begin|> + 06:06 [视频-转发/点赞/长播] <|video_begin|> + 16:50 [视频-转发/点赞/长播] <|video_begin|> +【2026-01-30】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 04:56 [视频-长播] <|video_begin|> + 06:03 [广告-点击] <|ad_begin|> + 06:03 [视频-关注/点赞/长播] <|video_begin|> + 06:25 [视频-转发/评论/点赞/长播] <|video_begin|> + 06:36 [视频-关注/点赞/长播] <|video_begin|> + 06:42 [视频-长播] <|video_begin|> + 06:50 [视频-评论/点赞/长播] <|video_begin|> + 06:50 [视频-长播] <|video_begin|> + 06:59 [视频-长播] <|video_begin|> +【2026-01-31】 + --:-- [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|> +【2026-02-01】 + 02:32 [视频-长播] <|video_begin|> + 02:32 [视频-长播] <|video_begin|> + 02:46 [视频-长播] <|video_begin|> + 04:24 [视频-转发/点赞/长播] <|video_begin|> + 04:24 [视频-长播] <|video_begin|> + 04:24 [视频-长播] <|video_begin|> + 04:30 [视频-收藏/点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 11:39 [视频-评论/点赞/长播] <|video_begin|> + 11:39 [视频-长播] <|video_begin|> + 11:40 [视频-评论/点赞/长播] <|video_begin|> + 11:40 [视频-收藏/点赞/长播] <|video_begin|> + 16:17 [视频-收藏/点赞/长播] <|video_begin|> + 16:19 [视频-收藏/点赞/长播] <|video_begin|> + 16:25 [视频-关注/收藏/点赞/长播] <|video_begin|> + 16:34 [视频-长播] <|video_begin|> + 16:34 [视频-收藏/点赞/长播] <|video_begin|> + 16:34 [视频-收藏/点赞/长播] <|video_begin|> + 16:34 [视频-评论/收藏/点赞/长播] <|video_begin|> +【2026-02-02】 + 03:24 [视频-收藏/点赞/长播] <|video_begin|> + 04:30 [视频-长播] <|video_begin|> + 04:30 [视频-长播] <|video_begin|> + 04:30 [视频-关注/转发/收藏/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|> + 04:46 [视频-长播] <|video_begin|> + 04:46 [视频-收藏/点赞/长播] <|video_begin|> + 05:15 [视频-长播] <|video_begin|> + 05:15 [视频-长播] <|video_begin|> + 05:25 [视频-评论/点赞/长播] <|video_begin|> + 05:25 [视频-长播] <|video_begin|> + 05:25 [视频-长播] <|video_begin|> + 05:28 [视频-评论/点赞/长播] <|video_begin|> + 05:28 [视频-长播] <|video_begin|> + 05:35 [视频-长播] <|video_begin|> + 05:39 [视频-转发/收藏/点赞/长播] <|video_begin|> + 05:44 [视频-收藏/点赞/长播] <|video_begin|> + 05:44 [视频-收藏/点赞/长播] <|video_begin|> + 05:44 [视频-收藏/点赞/长播] <|video_begin|> + 05:50 [视频-长播] <|video_begin|> + 06:07 [视频-收藏/点赞/长播] <|video_begin|> + 06:10 [视频-评论/点赞/长播] <|video_begin|> + 06:59 [视频-关注/评论/点赞/长播] <|video_begin|> + 07:04 [视频-收藏/点赞/长播] <|video_begin|> + 07:19 [视频-关注/收藏/点赞/长播] <|video_begin|> +【2026-02-03】 + 05:11 [视频-转发/点赞/长播] <|video_begin|> + 05:11 [视频-转发/点赞/长播] <|video_begin|> + 05:16 [视频-长播] <|video_begin|> + 05:21 [视频-点赞/长播] <|video_begin|> + 05:21 [视频-点赞/长播] <|video_begin|> + 05:21 [视频-点赞/长播] <|video_begin|> + 05:21 [视频-点赞/长播] <|video_begin|> + 05:31 [视频-点赞/长播] <|video_begin|> + 05:31 [视频-收藏/点赞/长播] <|video_begin|> + 05:35 [视频-评论/点赞/长播] <|video_begin|> + 05:35 [视频-点赞/长播] <|video_begin|> + 05:35 [视频-点赞/长播] <|video_begin|> + 05:53 [视频-点赞/长播] <|video_begin|> + 05:53 [视频-长播] <|video_begin|> + 05:53 [视频-收藏/点赞/长播] <|video_begin|> + 15:31 [视频-点赞/长播] <|video_begin|> + 15:31 [视频-点赞/长播] <|video_begin|> + 15:47 [视频-点赞/长播] <|video_begin|> + 15:47 [视频-点赞/长播] <|video_begin|> + 15:47 [视频-点赞/长播] <|video_begin|> + 15:47 [视频-长播] <|video_begin|> + 15:49 [视频-点赞/长播] <|video_begin|> + 15:49 [视频-点赞/长播] <|video_begin|> + 15:49 [视频-点赞/长播] <|video_begin|> + 15:49 [视频-评论/点赞/长播] <|video_begin|> + 15:52 [视频-点赞/长播] <|video_begin|> + 15:52 [视频-点赞/长播] <|video_begin|> + 15:52 [视频-评论/点赞/长播] <|video_begin|> + 15:55 [视频-点赞/长播] <|video_begin|> + 15:55 [视频-点赞/长播] <|video_begin|> + 15:55 [视频-转发/点赞/长播] <|video_begin|> + 15:55 [视频-收藏/点赞/长播] <|video_begin|> + 16:02 [视频-点赞/长播] <|video_begin|> + 16:02 [视频-评论/点赞/长播] <|video_begin|> + 16:06 [视频-点赞/长播] <|video_begin|> + 16:06 [视频-点赞/长播] <|video_begin|> + 16:06 [视频-点赞/长播] <|video_begin|> + 16:14 [视频-长播] <|video_begin|> + 16:21 [视频-点赞/长播] <|video_begin|> +【2026-02-04】 + 03:45 [视频-点赞/长播] <|video_begin|> + 03:45 [视频-点赞/长播] <|video_begin|> + 04:32 [视频-点赞/长播] <|video_begin|> + 04:32 [视频-点赞/长播] <|video_begin|> + 04:32 [视频-点赞/长播] <|video_begin|> + 04:36 [视频-点赞/长播] <|video_begin|> + 04:36 [视频-点赞/长播] <|video_begin|> + 04:36 [视频-长播] <|video_begin|> + 04:36 [视频-收藏/点赞/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|> + 04:41 [视频-点赞/长播] <|video_begin|> + 07:42 [视频-点赞/长播] <|video_begin|> + 07:42 [视频-转发/点赞/长播] <|video_begin|> + 08:18 [视频-点赞/长播] <|video_begin|> + 08:23 [视频-点赞/长播] <|video_begin|> + 08:23 [视频-点赞/长播] <|video_begin|> + 08:28 [视频-点赞/长播] <|video_begin|> + 09:30 [视频-点赞/长播] <|video_begin|> + 09:30 [视频-长播] <|video_begin|> + 09:30 [视频-长播] <|video_begin|> + 09:33 [视频-点赞/长播] <|video_begin|> + 09:33 [视频-转发/点赞/长播] <|video_begin|> + 09:33 [视频-收藏/点赞/长播] <|video_begin|> + 09:41 [视频-点赞/长播] <|video_begin|> + 09:41 [视频-点赞/长播] <|video_begin|> + 09:47 [视频-长播] <|video_begin|> + 09:49 [视频-点赞/长播] <|video_begin|> + 09:49 [视频-点赞/长播] <|video_begin|> + 09:49 [视频-点赞/长播] <|video_begin|> + 09:49 [视频-长播] <|video_begin|> + 09:49 [视频-评论/长播] <|video_begin|> + 12:43 [视频-点赞/长播] <|video_begin|> + 12:46 [视频-点赞/长播] <|video_begin|> + 12:46 [视频-点赞/长播] <|video_begin|> +【2026-02-05】 + 02:01 [视频-点赞/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|> + 10:30 [视频-点赞/长播] <|video_begin|> + +角色任务：你将作为一个极端严苛的用户行为数据挖掘专家和数据格式化专家，依据以上用户交互历史，围绕目标主题，精确提取出所有相关的历史交互信息。 + +主题：从娱乐消遣到工具化投入的文玩兴趣深化 + +输出格式要求：请务必仅返回一个包含 SID 的 JSON 数组，不要附带任何额外的文字解释或冗余内容。 + +强约束：最终答案必须是一个可被 json.loads 解析的 JSON，不能包含 Markdown、解释、重复键或 JSON 外额外字符。/no_think<|im_end|> +<|im_start|>assistant +["<|living_begin|>","<|prod_begin|>","<|prod_begin|>","<|living_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|living_begin|>","<|prod_begin|>","<|living_begin|>","<|prod_begin|>","<|prod_begin|>","<|living_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|living_begin|>","<|prod_begin|>","<|living_begin|>","<|living_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>"]<|im_end|> +<|im_start|>user +【用户交互历史】： +【2023-07-16】 + --:-- [商品-购买] <|prod_begin|> +【2023-07-24】 + --:-- [商品-购买] <|prod_begin|> +【2024-02-26】 + --:-- [商品-购买] <|prod_begin|> +【2024-03-04】 + --:-- [商品-购买] <|prod_begin|> +【2024-05-29】 + --:-- [商品-购买] <|prod_begin|> +【2024-07-11】 + --:-- [商品-购买] <|prod_begin|> +【2024-08-03】 + --:-- [商品-购买] <|prod_begin|> +【2024-08-21】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-12】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-24】 + --:-- [直播-关注] <|living_begin|> +【2025-03-26】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-05】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-23】 + --:-- [直播-关注] <|living_begin|> +【2025-08-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-04】 + --:-- [直播-关注] <|living_begin|> +【2025-10-12】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-07】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-20】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-10】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-04】 + 07:28 [广告-深度转化] <|ad_begin|> + 08:28 [广告-深度转化] <|ad_begin|> +【2026-01-06】 + 04:22 [广告-深度转化] <|ad_begin|> + 05:05 [广告-深度转化] <|ad_begin|> + 07:36 [广告-深度转化] <|ad_begin|> + 18:43 [广告-深度转化] <|ad_begin|> +【2026-01-07】 + 01:49 [广告-深度转化] <|ad_begin|> +【2026-01-09】 + 07:46 [广告-深度转化] <|ad_begin|> +【2026-01-11】 + 10:43 [广告-深度转化] <|ad_begin|> + 12:50 [广告-深度转化] <|ad_begin|> +【2026-01-12】 + --:-- [直播-关注] <|living_begin|> +【2026-01-13】 + 01:20 [广告-深度转化] <|ad_begin|> + 02:07 [广告-深度转化] <|ad_begin|> + 08:04 [广告-深度转化] <|ad_begin|> + 10:05 [广告-深度转化] <|ad_begin|> + 12:34 [广告-深度转化] <|ad_begin|> + 14:10 [广告-深度转化] <|ad_begin|> + 15:50 [广告-深度转化] <|ad_begin|> +【2026-01-14】 + 09:28 [广告-深度转化] <|ad_begin|> + 11:43 [广告-深度转化] <|ad_begin|> +【2026-01-15】 + 07:18 [广告-深度转化] <|ad_begin|> + 07:39 [广告-深度转化] <|ad_begin|> +【2026-01-16】 + 03:36 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 10:17 [广告-深度转化] <|ad_begin|> +【2026-01-17】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-18】 + 04:08 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 05:57 [广告-深度转化] <|ad_begin|> + 06:27 [广告-点击] <|ad_begin|> + 11:54 [广告-深度转化] <|ad_begin|> + 11:54 [广告-点击] <|ad_begin|> + 12:15 [广告-深度转化] <|ad_begin|> + 12:15 [广告-点击] <|ad_begin|> + 12:32 [广告-深度转化] <|ad_begin|> + 12:32 [广告-点击] <|ad_begin|> + 13:45 [广告-深度转化] <|ad_begin|> + 13:45 [广告-点击] <|ad_begin|> + 14:07 [广告-深度转化] <|ad_begin|> + 14:07 [广告-点击] <|ad_begin|> + 16:14 [广告-深度转化] <|ad_begin|> + 16:14 [广告-点击] <|ad_begin|> + 16:48 [广告-深度转化] <|ad_begin|> + 16:48 [广告-点击] <|ad_begin|> + 23:34 [广告-点击] <|ad_begin|> +【2026-01-19】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 14:20 [广告-深度转化] <|ad_begin|> + 14:20 [广告-点击] <|ad_begin|> +【2026-01-20】 + 03:05 [广告-深度转化] <|ad_begin|> + 03:05 [广告-点击] <|ad_begin|> + 06:51 [广告-深度转化] <|ad_begin|> + 06:51 [广告-点击] <|ad_begin|> + 10:32 [广告-点击] <|ad_begin|> + 11:09 [广告-深度转化] <|ad_begin|> + 11:09 [广告-点击] <|ad_begin|> +【2026-01-21】 + 01:46 [广告-点击] <|ad_begin|> + 06:49 [广告-深度转化] <|ad_begin|> + 06:49 [广告-点击] <|ad_begin|> + 07:46 [广告-深度转化] <|ad_begin|> + 07:46 [广告-点击] <|ad_begin|> + 10:24 [广告-深度转化] <|ad_begin|> + 10:24 [广告-点击] <|ad_begin|> + 10:52 [广告-深度转化] <|ad_begin|> + 10:52 [广告-点击] <|ad_begin|> + 16:26 [视频-点赞/长播] <|video_begin|> + 19:56 [视频-浏览] <|video_begin|> +【2026-01-22】 + 06:25 [广告-深度转化] <|ad_begin|> + 06:25 [广告-点击] <|ad_begin|> + 09:26 [视频-点赞/长播] <|video_begin|> + 09:29 [视频-转发/长播] <|video_begin|> + 22:32 [视频-收藏/长播] <|video_begin|> +【2026-01-23】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 06:14 [广告-深度转化] <|ad_begin|> + 06:14 [广告-点击] <|ad_begin|> + 11:39 [广告-深度转化] <|ad_begin|> + 11:39 [广告-点击] <|ad_begin|> + 21:07 [广告-深度转化] <|ad_begin|> + 21:07 [广告-点击] <|ad_begin|> +【2026-01-24】 + 00:13 [广告-点击] <|ad_begin|> + 11:48 [广告-点击] <|ad_begin|> + 12:27 [广告-深度转化] <|ad_begin|> + 12:27 [广告-点击] <|ad_begin|> + 12:46 [广告-深度转化] <|ad_begin|> + 12:46 [广告-点击] <|ad_begin|> + 13:28 [广告-深度转化] <|ad_begin|> + 13:28 [广告-点击] <|ad_begin|> + 13:41 [广告-深度转化] <|ad_begin|> + 13:41 [广告-点击] <|ad_begin|> + 14:40 [广告-深度转化] <|ad_begin|> + 14:40 [广告-点击] <|ad_begin|> +【2026-01-25】 + --:-- [直播-关注] <|living_begin|> + 14:15 [广告-点击] <|ad_begin|> + 19:56 [广告-深度转化] <|ad_begin|> + 19:56 [广告-点击] <|ad_begin|> +【2026-01-26】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 19:19 [广告-深度转化] <|ad_begin|> + 19:19 [广告-点击] <|ad_begin|> + 20:30 [广告-深度转化] <|ad_begin|> + 20:30 [广告-点击] <|ad_begin|> +【2026-01-27】 + --:-- [商品-点击] <|prod_begin|> +【2026-01-28】 + 00:10 [视频-点赞/长播] <|video_begin|> + 02:22 [广告-深度转化] <|ad_begin|> + 02:22 [广告-点击] <|ad_begin|> + 05:00 [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|> + --:-- [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|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 09:40 [广告-深度转化] <|ad_begin|> + 09:40 [广告-点击] <|ad_begin|> + 09:49 [广告-深度转化] <|ad_begin|> + 09:49 [广告-点击] <|ad_begin|> + 18:29 [视频-点赞/长播] <|video_begin|> +【2026-01-29】 + 03:36 [广告-点击] <|ad_begin|> + 05:03 [视频-转发/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-购买] <|prod_begin|> + 09:08 [视频-浏览] <|video_begin|> + 09:10 [视频-收藏/长播] <|video_begin|> + 14:30 [视频-浏览] <|video_begin|> +【2026-01-30】 + 04:58 [广告-深度转化] <|ad_begin|> + 04:58 [广告-点击] <|ad_begin|> + 05:23 [广告-深度转化] <|ad_begin|> + 05:23 [广告-点击] <|ad_begin|> + 05:59 [广告-深度转化] <|ad_begin|> + 05:59 [广告-点击] <|ad_begin|> + 06:03 [广告-点击] <|ad_begin|> + 06:08 [广告-点击] <|ad_begin|> + 12:02 [广告-点击] <|ad_begin|> + 15:22 [广告-深度转化] <|ad_begin|> + 15:22 [广告-点击] <|ad_begin|> + 19:33 [广告-点击] <|ad_begin|> + 19:41 [视频-收藏/长播] <|video_begin|> + 19:50 [广告-深度转化] <|ad_begin|> + 19:50 [广告-点击] <|ad_begin|> + 20:05 [广告-点击] <|ad_begin|> + 23:19 [广告-深度转化] <|ad_begin|> + 23:19 [广告-点击] <|ad_begin|> + 23:57 [视频-收藏/长播] <|video_begin|> +【2026-01-31】 + 00:01 [广告-点击] <|ad_begin|> + 00:06 [视频-点赞/长播] <|video_begin|> + 00:08 [视频-点赞/长播] <|video_begin|> + 00:52 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 08:20 [广告-点击] <|ad_begin|> + 13:24 [广告-深度转化] <|ad_begin|> + 13:24 [广告-点击] <|ad_begin|> + 14:40 [广告-深度转化] <|ad_begin|> + 14:40 [广告-点击] <|ad_begin|> + 14:59 [广告-点击] <|ad_begin|> +【2026-02-01】 + 05:52 [视频-浏览] <|video_begin|> + 11:20 [广告-点击] <|ad_begin|> + 11:54 [广告-点击] <|ad_begin|> + 16:07 [广告-深度转化] <|ad_begin|> + 16:07 [广告-点击] <|ad_begin|> + 21:48 [视频-转发/长播] <|video_begin|> +【2026-02-02】 + 01:51 [广告-深度转化] <|ad_begin|> + 01:51 [广告-点击] <|ad_begin|> + 03:24 [视频-点赞/长播] <|video_begin|> + 05:04 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-02-03】 + 05:22 [广告-深度转化] <|ad_begin|> + 05:22 [广告-点击] <|ad_begin|> + 14:58 [广告-深度转化] <|ad_begin|> + 14:58 [广告-点击] <|ad_begin|> + 23:08 [视频-转发/长播] <|video_begin|> +【2026-02-04】 + 01:15 [广告-点击] <|ad_begin|> + 05:19 [广告-点击] <|ad_begin|> + 05:20 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 08:36 [广告-点击] <|ad_begin|> + 09:25 [视频-点赞/长播] <|video_begin|> + 09:42 [广告-点击] <|ad_begin|> + 11:49 [广告-深度转化] <|ad_begin|> + 11:49 [广告-点击] <|ad_begin|> +【2026-02-05】 + 06:17 [视频-长播] <|video_begin|> + 06:17 [视频-长播] <|video_begin|> + 08:04 [视频-长播] <|video_begin|> + 08:12 [视频-长播] <|video_begin|> + 08:12 [视频-长播] <|video_begin|> + 08:12 [视频-长播] <|video_begin|> + 08:12 [视频-长播] <|video_begin|> + 08:21 [视频-长播] <|video_begin|> + 08:21 [视频-长播] <|video_begin|> + 08:21 [视频-长播] <|video_begin|> + 08:21 [视频-长播] <|video_begin|> + 08:21 [视频-长播] <|video_begin|> + 08:24 [视频-长播] <|video_begin|> + 08:26 [视频-长播] <|video_begin|> + 08:26 [视频-长播] <|video_begin|> + 08:26 [视频-长播] <|video_begin|> + 08:30 [视频-长播] <|video_begin|> + 08:30 [视频-长播] <|video_begin|> + 09:27 [视频-长播] <|video_begin|> + 09:32 [视频-长播] <|video_begin|> + 09:33 [视频-长播] <|video_begin|> + 09:34 [视频-转发/长播] <|video_begin|> + 09:35 [视频-长播] <|video_begin|> + 09:35 [视频-长播] <|video_begin|> + 09:37 [视频-长播] <|video_begin|> + 09:37 [视频-长播] <|video_begin|> + 09:37 [视频-长播] <|video_begin|> + 09:37 [视频-长播] <|video_begin|> + 09:38 [视频-长播] <|video_begin|> + 09:38 [视频-长播] <|video_begin|> + 09:38 [视频-长播] <|video_begin|> + 09:42 [视频-长播] <|video_begin|> + 09:42 [视频-长播] <|video_begin|> + 09:43 [视频-长播] <|video_begin|> + 09:43 [视频-长播] <|video_begin|> + 09:44 [视频-长播] <|video_begin|> + 09:44 [视频-长播] <|video_begin|> + 09:44 [视频-长播] <|video_begin|> + 09:50 [视频-长播] <|video_begin|> + 09:50 [视频-长播] <|video_begin|> + 09:50 [视频-长播] <|video_begin|> + 09:51 [视频-长播] <|video_begin|> + 09:51 [视频-长播] <|video_begin|> + 09:55 [视频-长播] <|video_begin|> + 09:55 [视频-长播] <|video_begin|> + 09:55 [视频-长播] <|video_begin|> + 10:12 [视频-长播] <|video_begin|> + 10:23 [视频-长播] <|video_begin|> + 12:01 [视频-长播] <|video_begin|> + 12:15 [广告-深度转化] <|ad_begin|> + 12:15 [广告-点击] <|ad_begin|> + 12:25 [广告-深度转化] <|ad_begin|> + 12:25 [广告-点击] <|ad_begin|> + 14:44 [视频-长播] <|video_begin|> + 15:43 [视频-长播] <|video_begin|> +角色任务：你需要扮演极端严苛的用户行为数据挖掘专家以及数据格式化专家，基于以上用户交互历史，针对给定主题识别并提取具有递进逻辑和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）： 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 +核心提取逻辑 (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 与 交互 A 必须在同一生活场景、主题类目下具有极强的需求补完关系。 +2.兴趣因果递进：交互 B 是因为 交互 A 产生的副作用或新需求，或者 A 是 B 的诱因。 +3.需求深度细化：在同一主题类目下，交互 B 需要比交互 A 表达得更具体详细，但不纳入重复性的购买、点击、搜索等行为，避免形成重复节点。 +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男性保健的认知收敛 + +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男性保健的认知收敛", + "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" + } + ] + } +} + +强约束：最终答案必须是一个可被 json.loads 解析的 JSON，不能包含 Markdown、解释、重复键或 JSON 外额外字符。/think<|im_end|> +<|im_start|>assistant + +好的，这项任务要求我基于用户的交互历史，推理出他从泛化的健康焦虑到最终聚焦于男性保健品的认知收敛过程。我将以推荐分析师的视角，深入挖掘行为时间线背后的动机变化。 我注意到这位用户的消费行为在早期呈现出较为分散和日常化的特点。例如，他购买过多次货车相关的配件，如空气滤芯 <|prod_begin|>、<|prod_begin|> 和篷布修补贴 <|prod_begin|>，这让我初步判断他可能从事与货车驾驶或运输相关的职业。这个职业特点通常伴随着长时间久坐、作息不规律和身体疲劳等问题，这或许为他后来的健康需求埋下了伏笔。 在2024年8月，我观察到他首次购买了一款泛化型保健品——磷虾油凝胶糖果 <|prod_begin|>。这类产品通常宣传对心血管健康有益，适用人群广泛，购买行为本身并未显现出非常明确和紧急的健康诉求，更像是一种初步的、带有尝试性质的健康投资，可能受到了促销活动的吸引。此后，他还购买了皮肤敷料 <|prod_begin|> 和痔疮凝胶 <|prod_begin|> 等外用药品，这些消费行为反映出他存在一些具体的、但较为分散的健康困扰。 到了2025年3月，我发现了一个关键的转变。在3月12日，他购买了党参丁香猴头菇沙棘片 <|prod_begin|>。这表明他的兴趣从西式的营养补充剂转向了更偏向传统草本调理的产品，关注点可能涉及肠胃或免疫力调节，但方向依然比较宽泛。然而，仅仅一天之后，也就是3月13日，他购买了一款明确标注为“男士高浓度抗疲劳”的参灵宝胶囊 <|prod_begin|>。 + +{"logic_chain":{"name":"从泛化健康焦虑到聚焦男性保健的认知收敛","events":[{"date":"2024-08-21","action":"[商品-购买] <|prod_begin|>","logic":"初始探索：购买泛化型保健品（磷虾油），需求不明确，可能受促销活动影响。"},{"date":"2025-03-12","action":"[商品-购买] <|prod_begin|>","logic":"需求分化：转向传统草本成分的复合保健品，显示出对调理身体的兴趣，但方向仍较宽泛（如肠胃、免疫力）。"},{"date":"2025-03-13","action":"[商品-购买] <|prod_begin|>","logic":"认知跃迁与收敛：首次购买明确标注“男士”、“抗疲劳”的产品，需求从泛化健康转向精准的男性功能性保健，实现了认知上的聚焦。"},{"date":"2025-11-19","action":"[商品-购买] <|prod_begin|>","logic":"兴趣深化与验证：在“男性抗疲劳”的基础上，进一步探索更具针对性的“滋补”类成分（鹿鞭），并开始连续、密集地购买同类产品，表明前期认知已形成稳定决策。"}]}}<|im_end|> +<|im_start|>user +Solve for $x$: $$\log_2 \frac{3x+9}{5x-3} +\log_2\frac{5x-3}{x-2}=2$$/no_think<|im_end|> +<|im_start|>assistant +\[ +\log_2 \frac{3x+9}{5x-3} + \log_2 \frac{5x-3}{x-2} = 2 +\] + +Combine the logarithms: + +\[ +\log_2\left( \frac{3x+9}{5x-3} \cdot \frac{5x-3}{x-2} \right) = 2 +\] + +Cancel \(5x-3\) (provided it is non‑zero, which will be checked later): + +\[ +\log_2 \frac{3x+9}{x-2} = 2 +\] + +Exponentiate both sides with base 2: + +\[ +\frac{3x+9}{x-2} = 2^2 = 4 +\] + +Solve for \(x\): + +\[ +3x+9 = 4(x-2) \quad\Rightarrow\quad 3x+9 = 4x-8 +\] +\[ +9+8 = 4x-3x \quad\Rightarrow\quad 17 = x +\] + +Check the domain conditions: + +- Denominators: \(5x-3 \neq 0\) and \(x-2 \neq 0\) ⇒ for \(x=17\), \(5x-3=82\neq0\), \(x-2=15\neq0\). +- Arguments of the logs must be positive: + \[ + \frac{3x+9}{5x-3} = \frac{60}{82} > 0,\quad \frac{5x-3}{x-2} = \frac{82}{15} > 0. + \] + +All conditions are satisfied, so \(x=17\) is the solution. + +\[ +\boxed{17} +\]<|im_end|> +<|endoftext|><|endoftext|><|endoftext|> +label_ids: +[-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, 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config Qwen3Config { + "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 +} + + +labels: +{"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":"从泛化美妆教程到精细化局部技巧的深度学习","events":[{"date":"2026-02-05","action":"[视频-长播] <|video_begin|>","logic":"信息输入阶段：用户在当天密集观看了大量美妆相关视频，内容涵盖粉底、遮瑕、假睫毛、美甲等多个方面，属于知识的广泛摄入期。"},{"date":"2026-02-05","action":"[视频-收藏/长播] <|video_begin|>","logic":"认知分化与瓶颈识别：在广泛浏览后，用户开始对“修容”这一更具技术性的特定环节进行“收藏”，表明其识别出这是知识体系中的一个难点或重点，兴趣开始聚焦。"},{"date":"2026-02-05","action":"[视频-长播] <|video_begin|>","logic":"技能深化与工具探索：用户的学习兴趣进一步细化，从面部轮廓（修容）深化到唇部细节（唇线笔），这代表了从宏观妆效向微观技巧的认知升级，开始关注能提升妆容精致度的具体工具和方法。"}]}}<|im_end|> +["<|living_begin|>","<|prod_begin|>","<|prod_begin|>","<|living_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|living_begin|>","<|prod_begin|>","<|living_begin|>","<|prod_begin|>","<|prod_begin|>","<|living_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|living_begin|>","<|prod_begin|>","<|living_begin|>","<|living_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>"]<|im_end|> + +好的，这项任务要求我基于用户的交互历史，推理出他从泛化的健康焦虑到最终聚焦于男性保健品的认知收敛过程。我将以推荐分析师的视角，深入挖掘行为时间线背后的动机变化。 我注意到这位用户的消费行为在早期呈现出较为分散和日常化的特点。例如，他购买过多次货车相关的配件，如空气滤芯 <|prod_begin|>、<|prod_begin|> 和篷布修补贴 <|prod_begin|>，这让我初步判断他可能从事与货车驾驶或运输相关的职业。这个职业特点通常伴随着长时间久坐、作息不规律和身体疲劳等问题，这或许为他后来的健康需求埋下了伏笔。 在2024年8月，我观察到他首次购买了一款泛化型保健品——磷虾油凝胶糖果 <|prod_begin|>。这类产品通常宣传对心血管健康有益，适用人群广泛，购买行为本身并未显现出非常明确和紧急的健康诉求，更像是一种初步的、带有尝试性质的健康投资，可能受到了促销活动的吸引。此后，他还购买了皮肤敷料 <|prod_begin|> 和痔疮凝胶 <|prod_begin|> 等外用药品，这些消费行为反映出他存在一些具体的、但较为分散的健康困扰。 到了2025年3月，我发现了一个关键的转变。在3月12日，他购买了党参丁香猴头菇沙棘片 <|prod_begin|>。这表明他的兴趣从西式的营养补充剂转向了更偏向传统草本调理的产品，关注点可能涉及肠胃或免疫力调节，但方向依然比较宽泛。然而，仅仅一天之后，也就是3月13日，他购买了一款明确标注为“男士高浓度抗疲劳”的参灵宝胶囊 <|prod_begin|>。 + +{"logic_chain":{"name":"从泛化健康焦虑到聚焦男性保健的认知收敛","events":[{"date":"2024-08-21","action":"[商品-购买] <|prod_begin|>","logic":"初始探索：购买泛化型保健品（磷虾油），需求不明确，可能受促销活动影响。"},{"date":"2025-03-12","action":"[商品-购买] <|prod_begin|>","logic":"需求分化：转向传统草本成分的复合保健品，显示出对调理身体的兴趣，但方向仍较宽泛（如肠胃、免疫力）。"},{"date":"2025-03-13","action":"[商品-购买] <|prod_begin|>","logic":"认知跃迁与收敛：首次购买明确标注“男士”、“抗疲劳”的产品，需求从泛化健康转向精准的男性功能性保健，实现了认知上的聚焦。"},{"date":"2025-11-19","action":"[商品-购买] <|prod_begin|>","logic":"兴趣深化与验证：在“男性抗疲劳”的基础上，进一步探索更具针对性的“滋补”类成分（鹿鞭），并开始连续、密集地购买同类产品，表明前期认知已形成稳定决策。"}]}}<|im_end|> +\[ +\log_2 \frac{3x+9}{5x-3} + \log_2 \frac{5x-3}{x-2} = 2 +\] + +Combine the logarithms: + +\[ +\log_2\left( \frac{3x+9}{5x-3} \cdot \frac{5x-3}{x-2} \right) = 2 +\] + +Cancel \(5x-3\) (provided it is non‑zero, which will be checked later): + +\[ +\log_2 \frac{3x+9}{x-2} = 2 +\] + +Exponentiate both sides with base 2: + +\[ +\frac{3x+9}{x-2} = 2^2 = 4 +\] + +Solve for \(x\): + +\[ +3x+9 = 4(x-2) \quad\Rightarrow\quad 3x+9 = 4x-8 +\] +\[ +9+8 = 4x-3x \quad\Rightarrow\quad 17 = x +\] + +Check the domain conditions: + +- Denominators: \(5x-3 \neq 0\) and \(x-2 \neq 0\) ⇒ for \(x=17\), \(5x-3=82\neq0\), \(x-2=15\neq0\). +- Arguments of the logs must be positive: + \[ + \frac{3x+9}{5x-3} = \frac{60}{82} > 0,\quad \frac{5x-3}{x-2} = \frac{82}{15} > 0. + \] + +All conditions are satisfied, so \(x=17\) is the solution. + +\[ +\boxed{17} +\]<|im_end|> + +[INFO|2026-07-16 01:39:59] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +[INFO|2026-07-16 01:39:59] llamafactory.model.model_utils.liger_kernel:144 >> Liger kernel has been applied to the model. +[INFO|modeling_utils.py:748] 2026-07-16 01:40:00,128 >> loading weights file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/model.safetensors +[INFO|modeling_utils.py:821] 2026-07-16 01:40:00,128 >> Will use dtype=torch.bfloat16 as defined in model's config object +[INFO|configuration_utils.py:1027] 2026-07-16 01:40:00,179 >> Generate config GenerationConfig { + "eos_token_id": 151645, + "output_attentions": false, + "output_hidden_states": false, + "pad_token_id": 151643, + "use_cache": false +} + + Loading weights: 0%| | 0/311 [00:00> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/generation_config.json +[INFO|configuration_utils.py:1027] 2026-07-16 01:40:00,474 >> Generate config GenerationConfig { + "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] 2026-07-16 01:40:00,474 >> Could not locate the custom_generate/generate.py inside /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018. +[INFO|2026-07-16 01:40:00] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-07-16 01:40:00] llamafactory.model.model_utils.attention:144 >> Using FlashAttention-2 for faster training and inference. +[INFO|2026-07-16 01:40:00] llamafactory.model.adapter:144 >> Pure bf16 / BAdam detected, remaining trainable params in half precision. +[INFO|2026-07-16 01:40:00] llamafactory.model.adapter:144 >> Fine-tuning method: Full +[INFO|2026-07-16 01:40:00] llamafactory.model.loader:144 >> trainable params: 801,433,600 || all params: 801,433,600 || trainable%: 100.0000 +[INFO|trainer.py:1469] 2026-07-16 01:40:00,602 >> ***** Running training ***** +[INFO|trainer.py:1470] 2026-07-16 01:40:00,602 >> Num examples = 315 +[INFO|trainer.py:1471] 2026-07-16 01:40:00,602 >> Num Epochs = 1 +[INFO|trainer.py:1472] 2026-07-16 01:40:00,602 >> Num update steps per epoch = 79 +[INFO|trainer.py:1473] 2026-07-16 01:40:00,602 >> Instantaneous batch size per device = 1 +[INFO|trainer.py:1476] 2026-07-16 01:40:00,602 >> Total train batch size (w. parallel, distributed & accumulation) = 4 +[INFO|trainer.py:1477] 2026-07-16 01:40:00,602 >> Gradient Accumulation steps = 4 +[INFO|trainer.py:1478] 2026-07-16 01:40:00,602 >> Total optimization steps = 32 +[INFO|trainer.py:1479] 2026-07-16 01:40:00,602 >> Number of trainable parameters = 801,433,600 + 0%| | 0/32 [00:00> `use_return_dict` is deprecated! Use `return_dict` instead! + 3%|▎ | 1/32 [00:08<04:25, 8.56s/it] 6%|▋ | 2/32 [00:16<04:14, 8.47s/it] 9%|▉ | 3/32 [00:24<03:50, 7.93s/it] 12%|█▎ | 4/32 [00:31<03:30, 7.52s/it] 16%|█▌ | 5/32 [00:39<03:31, 7.82s/it] 19%|█▉ | 6/32 [00:47<03:25, 7.91s/it] 22%|██▏ | 7/32 [00:56<03:25, 8.22s/it] 25%|██▌ | 8/32 [01:04<03:15, 8.16s/it] 28%|██▊ | 9/32 [01:11<03:01, 7.88s/it] 31%|███▏ | 10/32 [01:19<02:52, 7.82s/it] 31%|███▏ | 10/32 [01:19<02:52, 7.82s/it] 34%|███▍ | 11/32 [01:28<02:49, 8.06s/it] 38%|███▊ | 12/32 [01:35<02:39, 7.95s/it] 41%|████ | 13/32 [01:44<02:33, 8.09s/it] 44%|████▍ | 14/32 [01:52<02:29, 8.29s/it] 47%|████▋ | 15/32 [02:01<02:22, 8.35s/it] 50%|█████ | 16/32 [02:09<02:12, 8.29s/it] 53%|█████▎ | 17/32 [02:17<02:04, 8.33s/it] 56%|█████▋ | 18/32 [02:26<01:56, 8.35s/it] 59%|█████▉ | 19/32 [02:33<01:45, 8.08s/it] 62%|██████▎ | 20/32 [02:41<01:36, 8.04s/it] 62%|██████▎ | 20/32 [02:41<01:36, 8.04s/it] 66%|██████▌ | 21/32 [02:49<01:28, 8.02s/it] 69%|██████▉ | 22/32 [02:56<01:17, 7.71s/it] 72%|███████▏ | 23/32 [03:04<01:10, 7.82s/it] 75%|███████▌ | 24/32 [03:12<01:01, 7.71s/it] 78%|███████▊ | 25/32 [03:20<00:54, 7.86s/it] 81%|████████▏ | 26/32 [03:28<00:48, 8.05s/it] 84%|████████▍ | 27/32 [03:37<00:41, 8.31s/it] 88%|████████▊ | 28/32 [03:46<00:33, 8.36s/it] 91%|█████████ | 29/32 [03:54<00:25, 8.39s/it] 94%|█████████▍| 30/32 [04:02<00:16, 8.27s/it] 94%|█████████▍| 30/32 [04:02<00:16, 8.27s/it] 97%|█████████▋| 31/32 [04:11<00:08, 8.30s/it] 100%|██████████| 32/32 [04:18<00:00, 8.11s/it][INFO|trainer.py:1823] 2026-07-16 01:44:19,452 >> + +Training completed. Do not forget to share your model on huggingface.co/models =) + + + 100%|██████████| 32/32 [04:18<00:00, 8.11s/it] 100%|██████████| 32/32 [04:18<00:00, 8.09s/it] +[INFO|trainer.py:3815] 2026-07-16 01:44:19,467 >> Saving model checkpoint to /home/ll/llm4rec/experiments/outputs/v48_v29_clean_fused_lr45e7_ep040 +[INFO|configuration_utils.py:533] 2026-07-16 01:44:19,468 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v48_v29_clean_fused_lr45e7_ep040/config.json +[INFO|configuration_utils.py:816] 2026-07-16 01:44:19,469 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v48_v29_clean_fused_lr45e7_ep040/generation_config.json +{'loss': '2.188', 'grad_norm': '11.06', 'learning_rate': '3.8e-07', 'epoch': '0.127'} +{'loss': '2.155', 'grad_norm': '11.19', 'learning_rate': '1.686e-07', 'epoch': '0.254'} +{'loss': '2.012', 'grad_norm': '13.44', 'learning_rate': '1.032e-08', 'epoch': '0.381'} +{'train_runtime': '258.9', 'train_samples_per_second': '0.487', 'train_steps_per_second': '0.124', 'train_loss': '2.148', 'epoch': '0.4063'} + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/outputs/v48_v29_clean_fused_lr45e7_ep040/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-16 01:44:20,452 >> chat template saved in /home/ll/llm4rec/experiments/outputs/v48_v29_clean_fused_lr45e7_ep040/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-16 01:44:20,473 >> tokenizer config file saved in /home/ll/llm4rec/experiments/outputs/v48_v29_clean_fused_lr45e7_ep040/tokenizer_config.json +***** train metrics ***** + epoch = 0.4063 + total_flos = 14142439GF + train_loss = 2.1477 + train_runtime = 0:04:18.86 + train_samples_per_second = 0.487 + train_steps_per_second = 0.124 +Figure saved at: /home/ll/llm4rec/experiments/outputs/v48_v29_clean_fused_lr45e7_ep040/training_loss.png +[WARNING|2026-07-16 01:44:20] llamafactory.extras.ploting:149 >> No metric eval_loss to plot. +[WARNING|2026-07-16 01:44:20] llamafactory.extras.ploting:149 >> No metric eval_accuracy to plot. +[INFO|modelcard.py:263] 2026-07-16 01:44:20,571 >> 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..b732e94 --- /dev/null +++ b/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 0.40634920634920635, + "total_flos": 1.5185329017526272e+16, + "train_loss": 2.147665500640869, + "train_runtime": 258.8642, + "train_samples_per_second": 0.487, + "train_steps_per_second": 0.124 +} \ No newline at end of file diff --git a/trainer_log.jsonl b/trainer_log.jsonl new file mode 100644 index 0000000..ca80fd1 --- /dev/null +++ b/trainer_log.jsonl @@ -0,0 +1,4 @@ +{"current_steps": 10, "total_steps": 32, "loss": 2.188013458251953, "lr": 3.800175567920295e-07, "epoch": 0.12698412698412698, "percentage": 31.25, 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