From 12bf328a3f2e55b2de5bddf659dafe70640d2811 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Sun, 19 Jul 2026 02:01:10 +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/v43_v29_paper_mix_low_lr25e7_ep15 Source: Original Platform --- .gitattributes | 36 + README.md | 58 + all_results.json | 8 + chat_template.jinja | 89 + config.json | 63 + experiment_recipe.json | 72 + generation_config.json | 12 + model.safetensors | 3 + ...68423.ubuntu-System-Product-Name.1387087.0 | 3 + tokenizer.json | 3 + tokenizer_config.json | 16 + train.log | 2324 +++++++++++++++++ train_results.json | 8 + trainer_log.jsonl | 72 + trainer_state.json | 540 ++++ training_args.bin | 3 + training_loss.png | Bin 0 -> 65642 bytes 17 files changed, 3310 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/Jul15_06-33-43_ubuntu-System-Product-Name/events.out.tfevents.1784068423.ubuntu-System-Product-Name.1387087.0 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 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..ca6b5c7 --- /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: v43_v29_paper_mix_low_lr25e7_ep15 + results: [] +--- + + + +# v43_v29_paper_mix_low_lr25e7_ep15 + +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_v43_paper_mix_low 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: 2.5e-07 +- train_batch_size: 1 +- eval_batch_size: 8 +- seed: 202607193 +- 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: 1.5 + +### 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..aa6dc3e --- /dev/null +++ b/all_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 1.5002627430373097, + "total_flos": 3.414600284463329e+17, + "train_loss": 1.907181319736299, + "train_runtime": 5553.447, + "train_samples_per_second": 0.514, + "train_steps_per_second": 0.129 +} \ 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", + 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"requested_components": { + "general": 30400, + "r0": 19000, + "r3_think": 6000, + "r3_direct": 12000, + "old_rec": 5000, + "old_user": 3800, + "old_item": 3800 + }, + "groups": { + "general": 30400, + "old_rec": 5000, + "r3": 18000, + "r0": 19000, + "old_item": 3800, + "old_user": 3800 + }, + "variants": { + "official_general_cot": 15121, + "rec_no_think_direct_final": 2520, + "profile_goods_direct": 2647, + "caption_to_sid_direct": 9502, + "profile_live_cot": 1414, + "official_general_direct": 15279, + "profile_video/video_cot": 1876, + "sid_to_caption_cot": 4746, + "item_short_think": 699, + "profile_video/video_direct": 3676, + "original_competition": 6105, + "profile_goods_cot": 1350, + "profile_live_direct": 2834, + "profile_video/ad_direct": 2843, + "sid_to_caption_direct": 4752, + "user_strict_array": 666, + "item_no_think_direct_final": 670, + "profile_video/ad_cot": 1360, + "user_strict_logic": 542, + "user_extra_no_think_logic": 228, + "user_logic_json_strict": 529, + "user_array_json_strict": 641 + }, + "routes": { + "think": 31005, + "no_think": 48995 + }, + "target_leakage": 0, + "seed": 202607151 + }, + "learning_rate": "2.5e-7", + "epochs": 1.5, + "stages": null, + "note": "Controlled long, low-LR exposure on exactly the same data as v34.", + "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_v34_v43.py", + "script_sha256": "fbf01773c61579cf6159f64442fc0c8902714c84ed23b488bc9a6236a154f402", + "configs": [ + "/home/ll/llm4rec/experiments/configs/v43_v29_paper_mix_low_lr25e7_ep15.yaml" + ], + "reproduce": "/home/ll/llm4rec/demo/LLaMA-Factory/.venv/bin/python /home/ll/llm4rec/experiments/run_v34_v43.py --single v43_v29_paper_mix_low_lr25e7_ep15 --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, + "transformers_version": "5.6.0" +} diff --git a/model.safetensors b/model.safetensors new file mode 100644 index 0000000..b669940 --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5315105b7f0339eef45a7ef8bf071004513786a2bcdee4b2a8e5d0747ddb8666 +size 1602902832 diff --git a/runs/Jul15_06-33-43_ubuntu-System-Product-Name/events.out.tfevents.1784068423.ubuntu-System-Product-Name.1387087.0 b/runs/Jul15_06-33-43_ubuntu-System-Product-Name/events.out.tfevents.1784068423.ubuntu-System-Product-Name.1387087.0 new file mode 100644 index 0000000..43cc0e0 --- /dev/null +++ b/runs/Jul15_06-33-43_ubuntu-System-Product-Name/events.out.tfevents.1784068423.ubuntu-System-Product-Name.1387087.0 @@ -0,0 +1,3 @@ +version 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b/train.log new file mode 100644 index 0000000..87101fd --- /dev/null +++ b/train.log @@ -0,0 +1,2324 @@ +[2026-07-15 06:33:36] stage 1/1: /home/ll/llm4rec/experiments/configs/v43_v29_paper_mix_low_lr25e7_ep15.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|2026-07-15 06:33:39] 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] 2026-07-15 06:33:39,235 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-15 06:33:39,238 >> 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] 2026-07-15 06:33:39,905 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-15 06:33:39,906 >> 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] 2026-07-15 06:33:39,906 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-15 06:33:39,906 >> 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, + 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1124, 1318, 90, 41, 27275, 59, 2533, 44500, 11, 279, 4802, 44373, 374, 1124, 11520, 79075, 90, 18, 13, 22, 16, 1124, 15136, 220, 16, 15, 87210, 17, 18, 92, 1124, 11, 1124, 1318, 90, 41, 3417, 57758, 320, 43991, 311, 2326, 5089, 12396, 568, 151645, 198, 151643, 151643, 151643, 151643, 151643] +inputs: +<|im_start|>user +【用户交互历史】： +【2025-01-03】 + --:-- [直播-打赏] <|living_begin|> +【2025-01-05】 + --:-- [直播-关注] <|living_begin|> +【2025-02-15】 + --:-- [直播-关注] <|living_begin|> +【2025-03-14】 + --:-- [直播-关注] <|living_begin|> +【2025-04-22】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-04】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-05-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-25】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-05-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-02】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-06-14】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-06-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-29】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-07-03】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-07-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-31】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-15】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-17】 + --:-- [直播-关注] <|living_begin|> +【2025-08-31】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-04】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-12】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-15】 + --:-- [直播-关注] <|living_begin|> +【2025-09-16】 + --:-- [直播-打赏] <|living_begin|> +【2025-09-17】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-09-29】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-12】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-20】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-05】 + --:-- [直播-打赏] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-07】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-08】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-打赏] <|living_begin|> +【2025-11-17】 + --:-- [直播-关注] <|living_begin|> +【2025-11-19】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-20】 + --:-- [直播-关注] <|living_begin|> +【2025-11-21】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-11-22】 + --:-- [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|> +【2025-11-23】 + --:-- [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|> +【2025-11-25】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-28】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-30】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-02】 + --:-- [直播-关注] <|living_begin|> +【2025-12-03】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-06】 + --:-- [直播-关注] <|living_begin|> +【2025-12-07】 + --:-- [直播-关注] <|living_begin|> +【2025-12-14】 + --:-- [直播-关注] <|living_begin|> +【2025-12-15】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-16】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-17】 + --:-- [直播-首次打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-18】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-20】 + --:-- [直播-打赏] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-12-22】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-28】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-31】 + --:-- [直播-首次打赏] <|living_begin|> +【2026-01-01】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2026-01-02】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-04】 + 06:40 [广告-深度转化] <|ad_begin|> + 09:08 [广告-深度转化] <|ad_begin|> + 18:55 [广告-深度转化] <|ad_begin|> +【2026-01-05】 + 03:43 [广告-深度转化] <|ad_begin|> + 04:23 [广告-深度转化] <|ad_begin|> + 05:08 [广告-深度转化] <|ad_begin|> + 13:13 [广告-深度转化] <|ad_begin|> + 15:30 [广告-深度转化] <|ad_begin|> + 17:25 [广告-深度转化] <|ad_begin|> + 17:40 [广告-深度转化] <|ad_begin|> +【2026-01-06】 + 04:53 [广告-深度转化] <|ad_begin|> + 23:19 [广告-深度转化] <|ad_begin|> +【2026-01-07】 + --:-- [直播-首次打赏] <|living_begin|> + 10:27 [广告-深度转化] <|ad_begin|> + 17:25 [广告-深度转化] <|ad_begin|> + 17:40 [广告-深度转化] <|ad_begin|> +【2026-01-08】 + 03:27 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 16:22 [广告-深度转化] <|ad_begin|> + 16:32 [广告-点击] <|ad_begin|> + 16:42 [广告-深度转化] <|ad_begin|> + 16:42 [广告-点击] <|ad_begin|> + 19:38 [广告-深度转化] <|ad_begin|> + 19:38 [广告-点击] <|ad_begin|> +【2026-01-09】 + --:-- [直播-关注] <|living_begin|> + 02:53 [广告-深度转化] <|ad_begin|> + 02:53 [广告-点击] <|ad_begin|> + 09:06 [广告-深度转化] <|ad_begin|> + 09:06 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-10】 + 03:02 [广告-深度转化] <|ad_begin|> + 03:02 [广告-点击] <|ad_begin|> + 03:13 [广告-点击] <|ad_begin|> + 05:07 [广告-点击] <|ad_begin|> + 05:41 [广告-深度转化] <|ad_begin|> + 05:41 [广告-点击] <|ad_begin|> + 08:07 [广告-深度转化] <|ad_begin|> + 08:07 [广告-点击] <|ad_begin|> + 08:15 [广告-深度转化] <|ad_begin|> + 08:15 [广告-点击] <|ad_begin|> + 08:31 [广告-深度转化] <|ad_begin|> + 08:31 [广告-点击] <|ad_begin|> + 09:01 [广告-深度转化] <|ad_begin|> + 09:01 [广告-点击] <|ad_begin|> + 16:06 [广告-深度转化] <|ad_begin|> + 16:06 [广告-点击] <|ad_begin|> + 17:42 [广告-深度转化] <|ad_begin|> + 17:42 [广告-点击] <|ad_begin|> + 17:52 [广告-点击] <|ad_begin|> + 18:04 [广告-深度转化] <|ad_begin|> + 18:04 [广告-点击] <|ad_begin|> +【2026-01-11】 + 04:38 [广告-深度转化] <|ad_begin|> + 04:38 [广告-点击] <|ad_begin|> +【2026-01-12】 + --:-- [直播-打赏] <|living_begin|> + 06:35 [广告-深度转化] <|ad_begin|> + 06:35 [广告-点击] <|ad_begin|> + 15:36 [广告-点击] <|ad_begin|> +【2026-01-13】 + 03:09 [广告-深度转化] <|ad_begin|> + 03:09 [广告-点击] <|ad_begin|> + 04:48 [广告-深度转化] <|ad_begin|> + 04:48 [广告-点击] <|ad_begin|> + 08:28 [广告-深度转化] <|ad_begin|> + 08:28 [广告-点击] <|ad_begin|> + 09:54 [广告-深度转化] <|ad_begin|> + 09:54 [广告-点击] <|ad_begin|> + 11:11 [广告-深度转化] <|ad_begin|> + 11:11 [广告-点击] <|ad_begin|> + 13:18 [广告-深度转化] <|ad_begin|> + 13:18 [广告-点击] <|ad_begin|> +【2026-01-14】 + --:-- [直播-首次打赏] <|living_begin|> + 04:22 [广告-深度转化] <|ad_begin|> + 04:22 [广告-点击] <|ad_begin|> + 10:23 [广告-深度转化] <|ad_begin|> + 10:23 [广告-点击] <|ad_begin|> + 17:10 [广告-深度转化] <|ad_begin|> + 17:10 [广告-点击] <|ad_begin|> + 20:10 [广告-深度转化] <|ad_begin|> + 20:10 [广告-点击] <|ad_begin|> +【2026-01-16】 + 03:13 [广告-深度转化] <|ad_begin|> + 03:13 [广告-点击] <|ad_begin|> +【2026-01-17】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-18】 + 03:11 [广告-点击] <|ad_begin|> + 04:23 [广告-深度转化] <|ad_begin|> + 04:23 [广告-点击] <|ad_begin|> + 18:31 [广告-深度转化] <|ad_begin|> + 18:31 [广告-点击] <|ad_begin|> +【2026-01-19】 + --:-- [直播-关注/打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-20】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 04:13 [广告-点击] <|ad_begin|> + 18:02 [广告-深度转化] <|ad_begin|> + 18:02 [广告-点击] <|ad_begin|> +【2026-01-21】 + 03:26 [广告-深度转化] <|ad_begin|> + 03:26 [广告-点击] <|ad_begin|> + 03:41 [广告-深度转化] <|ad_begin|> + 03:41 [广告-点击] <|ad_begin|> + 05:41 [广告-点击] <|ad_begin|> + 05:47 [广告-深度转化] <|ad_begin|> + 05:47 [广告-点击] <|ad_begin|> + 09:19 [广告-深度转化] <|ad_begin|> + 09:19 [广告-点击] <|ad_begin|> + 09:29 [广告-深度转化] <|ad_begin|> + 09:29 [广告-点击] <|ad_begin|> + 09:43 [广告-深度转化] <|ad_begin|> + 09:43 [广告-点击] <|ad_begin|> + 17:29 [广告-深度转化] <|ad_begin|> + 17:29 [广告-点击] <|ad_begin|> +【2026-01-22】 + 11:11 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 14:08 [视频-点赞/长播] <|video_begin|> + 18:03 [广告-点击] <|ad_begin|> + 18:10 [视频-点赞/长播] <|video_begin|> +【2026-01-23】 + 04:00 [视频-点赞/长播] <|video_begin|> + 07:08 [广告-深度转化] <|ad_begin|> + 07:08 [广告-点击] <|ad_begin|> + 11:17 [视频-点赞/长播] <|video_begin|> + 11:17 [视频-点赞/长播] <|video_begin|> + 11:17 [视频-点赞/长播] <|video_begin|> + 11:35 [广告-深度转化] <|ad_begin|> + 11:35 [广告-点击] <|ad_begin|> + 14:48 [广告-点击] <|ad_begin|> + 15:48 [视频-点赞/长播] <|video_begin|> +【2026-01-24】 + 03:05 [广告-深度转化] <|ad_begin|> + 03:05 [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-购买] <|prod_begin|> + 15:16 [视频-点赞/长播] <|video_begin|> + 15:16 [视频-点赞/长播] <|video_begin|> +【2026-01-25】 + --:-- [直播-关注/首次打赏] <|living_begin|> + 06:42 [视频-点赞/长播] <|video_begin|> + 09:07 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 15:37 [视频-浏览] <|video_begin|> + 17:17 [广告-深度转化] <|ad_begin|> + 17:17 [广告-点击] <|ad_begin|> + 21:19 [广告-点击] <|ad_begin|> +【2026-01-26】 + 07:11 [广告-点击] <|ad_begin|> + 10:08 [广告-深度转化] <|ad_begin|> + 10:08 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 15:55 [视频-转发/长播] <|video_begin|> + 18:08 [视频-点赞/长播] <|video_begin|> + 18:10 [视频-点赞/长播] <|video_begin|> + 18:14 [视频-评论/点赞/长播] <|video_begin|> + 19:26 [广告-深度转化] <|ad_begin|> + 19:26 [广告-点击] <|ad_begin|> +【2026-01-27】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 03:35 [视频-浏览] <|video_begin|> + 09:29 [广告-点击] <|ad_begin|> + 15:10 [视频-点赞/长播] <|video_begin|> + 15:10 [视频-点赞/长播] <|video_begin|> + 16:20 [视频-点赞/长播] <|video_begin|> +【2026-01-28】 + 03:29 [视频-浏览] <|video_begin|> + 03:54 [广告-深度转化] <|ad_begin|> + 03:54 [广告-点击] <|ad_begin|> + 04:05 [广告-深度转化] <|ad_begin|> + 04:05 [广告-点击] <|ad_begin|> + 07:21 [广告-深度转化] <|ad_begin|> + 07:21 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 14:19 [视频-点赞/长播] <|video_begin|> +【2026-01-29】 + 03:06 [视频-点赞/长播] <|video_begin|> + 03:24 [视频-点赞/长播] <|video_begin|> + 10:36 [视频-关注/长播] <|video_begin|> + 10:47 [视频-点赞/长播] <|video_begin|> + 10:47 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 13:23 [视频-点赞/长播] <|video_begin|> + 15:52 [视频-点赞/长播] <|video_begin|> + 17:53 [视频-点赞/长播] <|video_begin|> + 20:59 [视频-点赞/长播] <|video_begin|> + 21:13 [视频-点赞/长播] <|video_begin|> + 22:48 [视频-点赞/长播] <|video_begin|> + 23:03 [视频-点赞/长播] <|video_begin|> + 23:17 [视频-点赞/长播] <|video_begin|> +【2026-01-30】 + --:-- [直播-关注] <|living_begin|> + 04:00 [视频-点赞/长播] <|video_begin|> + 04:11 [视频-点赞/长播] <|video_begin|> + 04:26 [广告-点击] <|ad_begin|> + 10:51 [视频-点赞/长播] <|video_begin|> + 10:55 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 17:48 [视频-点赞/长播] <|video_begin|> + 17:58 [广告-深度转化] <|ad_begin|> + 17:58 [广告-点击] <|ad_begin|> +【2026-01-31】 + --:-- [直播-首次打赏] <|living_begin|> + 03:37 [广告-点击] <|ad_begin|> + 03:52 [广告-深度转化] <|ad_begin|> + 03:52 [广告-点击] <|ad_begin|> + 12:35 [视频-点赞/长播] <|video_begin|> + 12:35 [视频-点赞/长播] <|video_begin|> + 18:20 [广告-深度转化] <|ad_begin|> + 18:20 [广告-点击] <|ad_begin|> + 18:39 [视频-点赞/长播] <|video_begin|> + 18:50 [广告-深度转化] <|ad_begin|> + 18:50 [广告-点击] <|ad_begin|> + 19:31 [视频-点赞/长播] <|video_begin|> + 19:40 [视频-点赞/长播] <|video_begin|> +【2026-02-01】 + 06:11 [广告-深度转化] <|ad_begin|> + 06:11 [广告-点击] <|ad_begin|> + 15:14 [广告-深度转化] <|ad_begin|> + 15:14 [广告-点击] <|ad_begin|> + 15:44 [广告-点击] <|ad_begin|> + 16:27 [视频-点赞/长播] <|video_begin|> + 16:27 [视频-点赞/长播] <|video_begin|> + 18:07 [视频-点赞/长播] <|video_begin|> + 18:22 [广告-深度转化] <|ad_begin|> + 18:22 [广告-点击] <|ad_begin|> + 21:04 [广告-点击] <|ad_begin|> +【2026-02-02】 + 18:11 [视频-点赞/长播] <|video_begin|> + 19:11 [视频-点赞/长播] <|video_begin|> +【2026-02-03】 + 02:35 [广告-深度转化] <|ad_begin|> + 02:35 [广告-点击] <|ad_begin|> + 03:17 [视频-点赞/长播] <|video_begin|> + 03:20 [视频-点赞/长播] <|video_begin|> + 08:46 [视频-长播] <|video_begin|> + 08:46 [视频-长播] <|video_begin|> + 09:27 [视频-长播] <|video_begin|> + 12:34 [视频-长播] <|video_begin|> + 13:31 [视频-长播] <|video_begin|> + 13:31 [视频-长播] <|video_begin|> + 13:31 [视频-长播] <|video_begin|> + 13:36 [视频-长播] <|video_begin|> + 13:36 [视频-长播] <|video_begin|> + 13:36 [视频-长播] <|video_begin|> + 13:39 [视频-长播] <|video_begin|> + 13:39 [视频-长播] <|video_begin|> + 14:58 [视频-长播] <|video_begin|> + 14:58 [视频-长播] <|video_begin|> + 15:12 [视频-点赞/长播] <|video_begin|> +【2026-02-04】 + --:-- [直播-首次打赏] <|living_begin|> + 01:56 [视频-长播] <|video_begin|> + 04:09 [视频-长播] <|video_begin|> + 10:50 [视频-长播] <|video_begin|> + 10:50 [视频-长播] <|video_begin|> + 20:36 [视频-长播] <|video_begin|> + 20:38 [视频-长播] <|video_begin|> + 20:38 [视频-长播] <|video_begin|> + 22:20 [视频-长播] <|video_begin|> + 22:28 [视频-长播] <|video_begin|> + 22:29 [视频-长播] <|video_begin|> + 22:29 [视频-长播] <|video_begin|> + 22:29 [视频-长播] <|video_begin|> + 22:38 [视频-长播] <|video_begin|> + 22:45 [视频-长播] <|video_begin|> + 23:03 [视频-长播] <|video_begin|> + 23:09 [视频-长播] <|video_begin|> + 23:09 [视频-长播] <|video_begin|> + 23:09 [视频-长播] <|video_begin|> + 23:17 [视频-长播] <|video_begin|> + 23:22 [视频-长播] <|video_begin|> + 23:22 [视频-长播] <|video_begin|> + 23:28 [视频-长播] <|video_begin|> + 23:28 [视频-长播] <|video_begin|> + 23:30 [视频-长播] <|video_begin|> + 23:30 [视频-长播] <|video_begin|> + 23:30 [视频-长播] <|video_begin|> +【2026-02-05】 + 04:48 [视频-长播] <|video_begin|> + 05:02 [视频-长播] <|video_begin|> + 11:21 [视频-点赞/长播] <|video_begin|> + 13:00 [视频-长播] <|video_begin|> + 13:00 [视频-长播] <|video_begin|> + 14:23 [视频-长播] <|video_begin|> + 14:24 [视频-长播] <|video_begin|> + 14:24 [视频-长播] <|video_begin|> + 14:24 [视频-长播] <|video_begin|> + 14:25 [视频-长播] <|video_begin|> + 15:48 [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，同时也是数据格式化专家，请结合以上用户交互历史，面向给定主题提取带有递进行为逻辑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 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 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：从地域偏好到特定风物特产的兴趣收敛 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从地域偏好到特定风物特产的兴趣收敛", + "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" + } + ] + } +}/no_think<|im_end|> +<|im_start|>assistant + + +{"logic_chain": {"name": "从地域偏好到特定风物特产的兴趣收敛", "events": [{"date": "2025-11-21", "action": "[直播-关注] <|living_begin|>", "logic": "兴趣沉浸：在长达数月对云南主播（尤其是临沧地区）的持续关注和互动中，不断被动接收地域文化和生活方式信息，形成对“云南”的深度情感连接和认知背景。"}, {"date": "2025-10-27", "action": "[商品-购买] <|prod_begin|>", "logic": "行为转化：基于对云南文化的长期兴趣，首次将线上情感消费（打赏/关注）转化为对具体地域风物（傣味腌菜）的实体消费尝试，实现了从“云体验”到“实体感知”的跨越。"}, {"date": "2026-01-17", "action": "[商品-点击] <|prod_begin|>", "logic": "兴趣聚焦与收敛：用户的兴趣点从泛化的“云南特产”进一步收敛到更具体的地理单元“永德县”（临沧市下辖县）。这表明用户不再满足于宽泛的地域标签，而是基于已有的认知（可能源于直播内容），开始对特定子区域的标志性产品进行精准探索，体现了认知地图的精细化。"}]}}<|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" + } + ] + } +} + +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】： +【2024-04-15】 + --:-- [商品-购买] <|prod_begin|> +【2024-04-24】 + --:-- [商品-购买] <|prod_begin|> +【2024-04-25】 + --:-- [商品-购买] <|prod_begin|> +【2024-04-27】 + --:-- [商品-购买] <|prod_begin|> +【2024-06-06】 + --:-- [商品-购买] <|prod_begin|> +【2024-06-16】 + --:-- [商品-购买] <|prod_begin|> +【2024-08-30】 + --:-- [商品-购买] <|prod_begin|> +【2024-09-16】 + --:-- [商品-购买] <|prod_begin|> +【2024-09-29】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-08】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-11】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-24】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-02】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-20】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-15】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-21】 + --:-- [商品-购买] <|prod_begin|> +【2024-12-22】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-02-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-26】 + --:-- [商品-购买] <|prod_begin|> +【2025-04-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-05-21】 + --:-- [商品-购买] <|prod_begin|> +【2025-06-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-21】 + --:-- [直播-关注] <|living_begin|> +【2025-08-31】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-03】 + --:-- [直播-关注] <|living_begin|> +【2025-09-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-14】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-16】 + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-10-04】 + --:-- [直播-关注] <|living_begin|> +【2025-10-08】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-17】 + --:-- [直播-关注] <|living_begin|> +【2025-10-18】 + --:-- [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|> +【2025-10-19】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-20】 + --:-- [直播-关注] <|living_begin|> +【2025-10-21】 + --:-- [直播-关注] <|living_begin|> +【2025-10-25】 + --:-- [直播-关注] <|living_begin|> +【2025-10-26】 + --:-- [直播-关注] <|living_begin|> +【2025-10-27】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-01】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-02】 + --:-- [直播-关注] <|living_begin|> +【2025-11-03】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-04】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-11-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-08】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-10】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-01】 + --:-- [直播-关注] <|living_begin|> +【2025-12-06】 + --:-- [直播-关注] <|living_begin|> +【2025-12-08】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> +【2025-12-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-22】 + --:-- [直播-关注] <|living_begin|> +【2025-12-23】 + --:-- [直播-关注] <|living_begin|> +【2025-12-25】 + --:-- [直播-关注] <|living_begin|> +【2025-12-28】 + --:-- [直播-关注] <|living_begin|> +【2026-01-03】 + --:-- [直播-关注] <|living_begin|> +【2026-01-05】 + --:-- [直播-关注] <|living_begin|> +【2026-01-12】 + --:-- [直播-关注] <|living_begin|> +【2026-01-14】 + 08:11 [广告-深度转化] <|ad_begin|> +【2026-01-15】 + 10:31 [广告-深度转化] <|ad_begin|> + 12:30 [广告-深度转化] <|ad_begin|> + 13:04 [广告-深度转化] <|ad_begin|> +【2026-01-16】 + 01:48 [广告-深度转化] <|ad_begin|> + 07:52 [广告-深度转化] <|ad_begin|> + 11:51 [广告-深度转化] <|ad_begin|> +【2026-01-17】 + 08:58 [广告-深度转化] <|ad_begin|> + 18:26 [广告-深度转化] <|ad_begin|> +【2026-01-18】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 01:12 [广告-深度转化] <|ad_begin|> + 03:55 [广告-深度转化] <|ad_begin|> + 05:08 [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|> + --:-- [商品-点击] <|prod_begin|> + 10:35 [广告-深度转化] <|ad_begin|> +【2026-01-19】 + --:-- [商品-点击] <|prod_begin|> + 18:27 [广告-深度转化] <|ad_begin|> + 18:28 [广告-深度转化] <|ad_begin|> + 18:33 [广告-深度转化] <|ad_begin|> + 19:14 [广告-深度转化] <|ad_begin|> + 21:20 [广告-深度转化] <|ad_begin|> +【2026-01-20】 + 02:02 [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|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 06:01 [广告-深度转化] <|ad_begin|> +【2026-01-21】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + 04:07 [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|> + --:-- [商品-点击] <|prod_begin|> + 06:36 [广告-深度转化] <|ad_begin|> + 13:24 [广告-深度转化] <|ad_begin|> + 13:56 [广告-深度转化] <|ad_begin|> + 17:30 [广告-深度转化] <|ad_begin|> + 17:47 [广告-深度转化] <|ad_begin|> + 19:16 [广告-深度转化] <|ad_begin|> +【2026-01-22】 + 00:07 [广告-深度转化] <|ad_begin|> + 03:06 [广告-深度转化] <|ad_begin|> + 05:04 [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|> + --:-- [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|> + 07:00 [广告-深度转化] <|ad_begin|> + 16:48 [广告-深度转化] <|ad_begin|> + 17:55 [广告-深度转化] <|ad_begin|> + 21:05 [视频-关注/长播] <|video_begin|> + 21:40 [广告-深度转化] <|ad_begin|> +【2026-01-24】 + 01:14 [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|> + --:-- [商品-购买] <|prod_begin|> + 06:11 [广告-深度转化] <|ad_begin|> + 07:32 [广告-深度转化] <|ad_begin|> + 20:20 [视频-点赞/长播] <|video_begin|> +【2026-01-25】 + 04:39 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 08:30 [广告-深度转化] <|ad_begin|> + 11:38 [广告-深度转化] <|ad_begin|> + 16:38 [广告-深度转化] <|ad_begin|> + 21:16 [广告-深度转化] <|ad_begin|> +【2026-01-26】 + --:-- [直播-关注] <|living_begin|> + 00:18 [广告-深度转化] <|ad_begin|> + 04:30 [视频-点赞/长播] <|video_begin|> + 04:52 [广告-深度转化] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 05:42 [广告-深度转化] <|ad_begin|> + 05:59 [广告-深度转化] <|ad_begin|> + 06:54 [广告-点击] <|ad_begin|> + 06:58 [广告-点击] <|ad_begin|> + 07:12 [视频-收藏/长播] <|video_begin|> + 10:26 [广告-深度转化] <|ad_begin|> + 10:26 [广告-点击] <|ad_begin|> + 13:24 [广告-深度转化] <|ad_begin|> + 13:24 [广告-点击] <|ad_begin|> + 19:44 [广告-点击] <|ad_begin|> + 19:57 [广告-深度转化] <|ad_begin|> + 19:57 [广告-点击] <|ad_begin|> + 20:00 [广告-点击] <|ad_begin|> + 20:05 [广告-点击] <|ad_begin|> + 20:06 [广告-点击] <|ad_begin|> + 20:15 [广告-点击] <|ad_begin|> + 20:19 [广告-点击] <|ad_begin|> + 22:27 [广告-深度转化] <|ad_begin|> + 22:27 [广告-点击] <|ad_begin|> + 22:51 [广告-深度转化] <|ad_begin|> + 22:51 [广告-点击] <|ad_begin|> + 23:10 [视频-收藏/长播] <|video_begin|> +【2026-01-27】 + 01:11 [广告-深度转化] <|ad_begin|> + 01:11 [广告-点击] <|ad_begin|> + 02:21 [广告-点击] <|ad_begin|> + 02:28 [广告-点击] <|ad_begin|> + 03:01 [广告-点击] <|ad_begin|> + 04:39 [广告-点击] <|ad_begin|> + 05:06 [广告-深度转化] <|ad_begin|> + 05:06 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 06:00 [广告-深度转化] <|ad_begin|> + 06:00 [广告-点击] <|ad_begin|> + 09:41 [广告-点击] <|ad_begin|> + 10:15 [广告-点击] <|ad_begin|> + 10:16 [广告-点击] <|ad_begin|> + 15:48 [广告-点击] <|ad_begin|> + 21:44 [广告-深度转化] <|ad_begin|> + 21:44 [广告-点击] <|ad_begin|> + 22:25 [广告-点击] <|ad_begin|> + 22:25 [广告-点击] <|ad_begin|> + 22:26 [广告-点击] <|ad_begin|> + 22:56 [广告-点击] <|ad_begin|> + 23:17 [广告-点击] <|ad_begin|> +【2026-01-28】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 14:02 [广告-深度转化] <|ad_begin|> + 14:02 [广告-点击] <|ad_begin|> + 19:43 [广告-深度转化] <|ad_begin|> + 19:43 [广告-点击] <|ad_begin|> + 20:00 [广告-点击] <|ad_begin|> + 23:23 [广告-点击] <|ad_begin|> +【2026-01-29】 + 03:48 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 19:53 [广告-点击] <|ad_begin|> + 19:55 [广告-点击] <|ad_begin|> + 19:56 [广告-点击] <|ad_begin|> +【2026-01-30】 + 02:23 [广告-点击] <|ad_begin|> + 05:13 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 06:13 [广告-深度转化] <|ad_begin|> + 06:13 [广告-点击] <|ad_begin|> + 12:26 [广告-深度转化] <|ad_begin|> + 12:26 [广告-点击] <|ad_begin|> +【2026-01-31】 + --:-- [直播-关注] <|living_begin|> + 01:52 [视频-转发/长播] <|video_begin|> + 04:53 [视频-点赞/长播] <|video_begin|> + 05:16 [广告-深度转化] <|ad_begin|> + 05:16 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 14:18 [广告-深度转化] <|ad_begin|> + 14:18 [广告-点击] <|ad_begin|> + 22:33 [广告-点击] <|ad_begin|> + 23:15 [广告-深度转化] <|ad_begin|> + 23:15 [广告-点击] <|ad_begin|> + 23:45 [广告-点击] <|ad_begin|> + 23:45 [广告-点击] <|ad_begin|> +【2026-02-01】 + 01:18 [广告-点击] <|ad_begin|> + 03:09 [视频-长播] <|video_begin|> + 03:09 [视频-长播] <|video_begin|> + 03:26 [广告-深度转化] <|ad_begin|> + 03:26 [广告-点击] <|ad_begin|> + 11:42 [广告-点击] <|ad_begin|> + 13:11 [视频-长播] <|video_begin|> + 13:43 [广告-深度转化] <|ad_begin|> + 13:43 [广告-点击] <|ad_begin|> + 15:09 [视频-长播] <|video_begin|> + 15:13 [视频-长播] <|video_begin|> + 15:13 [视频-长播] <|video_begin|> + 15:23 [视频-长播] <|video_begin|> + 15:44 [视频-长播] <|video_begin|> + 15:44 [视频-长播] <|video_begin|> + 16:01 [视频-长播] <|video_begin|> + 16:01 [视频-长播] <|video_begin|> + 16:48 [视频-长播] <|video_begin|> + 17:12 [视频-长播] <|video_begin|> + 17:19 [广告-点击] <|ad_begin|> + 18:46 [视频-长播] <|video_begin|> + 18:57 [视频-长播] <|video_begin|> + 18:57 [视频-长播] <|video_begin|> + 19:10 [视频-长播] <|video_begin|> + 20:44 [视频-长播] <|video_begin|> + 20:52 [视频-长播] <|video_begin|> + 20:52 [视频-长播] <|video_begin|> + 21:06 [视频-长播] <|video_begin|> + 21:08 [视频-长播] <|video_begin|> + 21:08 [视频-长播] <|video_begin|> + 23:28 [视频-长播] <|video_begin|> +【2026-02-02】 + 01:44 [广告-深度转化] <|ad_begin|> + 01:44 [广告-点击] <|ad_begin|> + 08:51 [视频-长播] <|video_begin|> + 09:03 [视频-长播] <|video_begin|> + 09:03 [视频-长播] <|video_begin|> +【2026-02-03】 + 04:09 [视频-长播] <|video_begin|> + 04:21 [视频-长播] <|video_begin|> + 04:46 [视频-长播] <|video_begin|> + 05:27 [视频-长播] <|video_begin|> + 09:12 [视频-长播] <|video_begin|> + 10:50 [广告-点击] <|ad_begin|> + 11:23 [广告-点击] <|ad_begin|> + 12:15 [视频-长播] <|video_begin|> + 13:43 [视频-长播] <|video_begin|> + 15:28 [视频-长播] <|video_begin|> + 15:28 [视频-长播] <|video_begin|> + 15:28 [视频-长播] <|video_begin|> + 15:28 [视频-点赞/长播] <|video_begin|> + 15:35 [视频-长播] <|video_begin|> + 15:35 [视频-长播] <|video_begin|> + 15:42 [视频-长播] <|video_begin|> + 15:44 [视频-关注/长播] <|video_begin|> + 16:30 [视频-关注/长播] <|video_begin|> + 16:42 [广告-深度转化] <|ad_begin|> + 16:42 [广告-点击] <|ad_begin|> + 17:10 [广告-点击] <|ad_begin|> + 17:13 [广告-点击] <|ad_begin|> + 17:41 [广告-点击] <|ad_begin|> + 17:48 [广告-点击] <|ad_begin|> + 17:56 [广告-点击] <|ad_begin|> + 17:59 [视频-长播] <|video_begin|> + 18:31 [广告-深度转化] <|ad_begin|> + 18:31 [广告-点击] <|ad_begin|> + 19:25 [视频-长播] <|video_begin|> + 23:04 [广告-点击] <|ad_begin|> +【2026-02-04】 + 00:27 [视频-长播] <|video_begin|> + 00:54 [广告-深度转化] <|ad_begin|> + 00:54 [广告-点击] <|ad_begin|> + 03:15 [广告-点击] <|ad_begin|> + 03:21 [广告-点击] <|ad_begin|> + 19:44 [视频-长播] <|video_begin|> + 22:55 [广告-深度转化] <|ad_begin|> + 22:55 [广告-点击] <|ad_begin|> +【2026-02-05】 + 02:36 [视频-长播] <|video_begin|> + 02:36 [视频-长播] <|video_begin|> + 02:43 [视频-长播] <|video_begin|> + 02:43 [视频-长播] <|video_begin|> + 03:36 [视频-长播] <|video_begin|> + 10:05 [广告-深度转化] <|ad_begin|> + 10:05 [广告-点击] <|ad_begin|> + 12:01 [视频-长播] <|video_begin|> + 12:49 [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的身份，基于以上交互历史，针对给定主题筛选并提取出全部相关的历史交互行为。 + +主题：从泛化养生到聚焦血糖管理的认知收敛 + +输出格式要求：请仅以包含 SID 的 JSON 数组形式返回结果，切勿输出任何额外的解释说明或无关字符。 + +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 +["<|prod_begin|>","<|prod_begin|>","<|living_begin|>","<|prod_begin|>","<|living_begin|>","<|living_begin|>","<|living_begin|>","<|prod_begin|>","<|prod_begin|>","<|living_begin|>","<|prod_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>"]<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-04-04】 + --:-- [直播-关注] <|living_begin|> +【2025-04-15】 + --:-- [直播-关注] <|living_begin|> +【2025-04-21】 + --:-- [直播-关注] <|living_begin|> +【2025-04-22】 + --:-- [直播-关注] <|living_begin|> +【2025-04-29】 + --:-- [直播-关注] <|living_begin|> +【2025-05-07】 + --:-- [直播-关注] <|living_begin|> +【2025-06-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-11】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-14】 + --:-- [商品-购买] <|prod_begin|> +【2025-07-16】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-07】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-13】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-14】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-15】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-17】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-25】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-29】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-31】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-04】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-24】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-20】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-23】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-12】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-15】 + --:-- [直播-关注] <|living_begin|> +【2025-11-19】 + --:-- [直播-关注] <|living_begin|> +【2026-01-05】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-06】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-09】 + 10:36 [广告-深度转化] <|ad_begin|> + 12:18 [广告-深度转化] <|ad_begin|> + 13:17 [广告-深度转化] <|ad_begin|> + 15:18 [广告-深度转化] <|ad_begin|> +【2026-01-10】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-15】 + 14:24 [广告-深度转化] <|ad_begin|> + 14:24 [广告-点击] <|ad_begin|> + 14:43 [广告-深度转化] <|ad_begin|> + 14:43 [广告-点击] <|ad_begin|> +【2026-01-16】 + 06:49 [广告-深度转化] <|ad_begin|> + 06:49 [广告-点击] <|ad_begin|> + 09:23 [广告-深度转化] <|ad_begin|> + 09:23 [广告-点击] <|ad_begin|> + 10:15 [广告-深度转化] <|ad_begin|> + 10:15 [广告-点击] <|ad_begin|> + 11:23 [广告-深度转化] <|ad_begin|> + 11:23 [广告-点击] <|ad_begin|> + 11:57 [广告-深度转化] <|ad_begin|> + 11:57 [广告-点击] <|ad_begin|> + 13:24 [广告-深度转化] <|ad_begin|> + 13:24 [广告-点击] <|ad_begin|> + 16:15 [广告-深度转化] <|ad_begin|> + 16:15 [广告-点击] <|ad_begin|> +【2026-01-17】 + 06:18 [广告-深度转化] <|ad_begin|> + 06:18 [广告-点击] <|ad_begin|> +【2026-01-18】 + 13:50 [广告-深度转化] <|ad_begin|> + 13:50 [广告-点击] <|ad_begin|> + 15:02 [广告-深度转化] <|ad_begin|> + 15:02 [广告-点击] <|ad_begin|> + 16:31 [广告-深度转化] <|ad_begin|> + 16:31 [广告-点击] <|ad_begin|> + 16:50 [广告-深度转化] <|ad_begin|> + 16:50 [广告-点击] <|ad_begin|> +【2026-01-19】 + 02:23 [广告-深度转化] <|ad_begin|> + 02:23 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-20】 + 06:19 [广告-点击] <|ad_begin|> + 07:19 [广告-深度转化] <|ad_begin|> + 07:19 [广告-点击] <|ad_begin|> + 08:43 [广告-深度转化] <|ad_begin|> + 08:43 [广告-点击] <|ad_begin|> + 12:57 [广告-深度转化] <|ad_begin|> + 12:57 [广告-点击] <|ad_begin|> + 15:11 [广告-深度转化] <|ad_begin|> + 15:11 [广告-点击] <|ad_begin|> + 15:45 [广告-深度转化] <|ad_begin|> + 15:45 [广告-点击] <|ad_begin|> + 15:55 [广告-深度转化] <|ad_begin|> + 15:55 [广告-点击] <|ad_begin|> +【2026-01-21】 + 09:21 [广告-深度转化] <|ad_begin|> + 09:21 [广告-点击] <|ad_begin|> + 09:39 [广告-深度转化] <|ad_begin|> + 09:39 [广告-点击] <|ad_begin|> + 13:19 [广告-深度转化] <|ad_begin|> + 13:19 [广告-点击] <|ad_begin|> + 14:07 [广告-深度转化] <|ad_begin|> + 14:07 [广告-点击] <|ad_begin|> + 14:38 [广告-深度转化] <|ad_begin|> + 14:38 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 15:11 [广告-深度转化] <|ad_begin|> + 15:11 [广告-点击] <|ad_begin|> +【2026-01-22】 + 04:12 [广告-深度转化] <|ad_begin|> + 04:12 [广告-点击] <|ad_begin|> +【2026-01-23】 + 04:00 [广告-深度转化] <|ad_begin|> + 04:00 [广告-点击] <|ad_begin|> + 07:57 [广告-深度转化] <|ad_begin|> + 07:57 [广告-点击] <|ad_begin|> + 09:07 [广告-深度转化] <|ad_begin|> + 09:07 [广告-点击] <|ad_begin|> + 14:40 [广告-深度转化] <|ad_begin|> + 14:40 [广告-点击] <|ad_begin|> + 15:41 [广告-深度转化] <|ad_begin|> + 15:41 [广告-点击] <|ad_begin|> +【2026-01-24】 + 05:24 [视频-点赞/长播] <|video_begin|> + 06:10 [广告-深度转化] <|ad_begin|> + 06:10 [广告-点击] <|ad_begin|> +【2026-01-25】 + 05:56 [广告-深度转化] <|ad_begin|> + 05:56 [广告-点击] <|ad_begin|> + 08:08 [广告-深度转化] <|ad_begin|> + 08:08 [广告-点击] <|ad_begin|> + 09:07 [广告-深度转化] <|ad_begin|> + 09:07 [广告-点击] <|ad_begin|> + 09:41 [广告-深度转化] <|ad_begin|> + 09:41 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-26】 + 07:06 [广告-深度转化] <|ad_begin|> + 07:06 [广告-点击] <|ad_begin|> + 07:36 [广告-深度转化] <|ad_begin|> + 07:36 [广告-点击] <|ad_begin|> + 08:44 [广告-深度转化] <|ad_begin|> + 08:44 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-01-27】 + 09:58 [广告-深度转化] <|ad_begin|> + 09:58 [广告-点击] <|ad_begin|> + 13:19 [广告-深度转化] <|ad_begin|> + 13:19 [广告-点击] <|ad_begin|> + 14:21 [广告-深度转化] <|ad_begin|> + 14:21 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 16:02 [广告-深度转化] <|ad_begin|> + 16:02 [广告-点击] <|ad_begin|> +【2026-01-28】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 15:29 [视频-长播] <|video_begin|> + 16:01 [广告-深度转化] <|ad_begin|> + 16:01 [广告-点击] <|ad_begin|> + 16:27 [广告-深度转化] <|ad_begin|> + 16:27 [广告-点击] <|ad_begin|> +【2026-01-29】 + 06:07 [广告-深度转化] <|ad_begin|> + 06:07 [广告-点击] <|ad_begin|> + 07:07 [广告-深度转化] <|ad_begin|> + 07:07 [广告-点击] <|ad_begin|> + 07:37 [视频-长播] <|video_begin|> + 07:37 [视频-长播] <|video_begin|> + 10:00 [广告-深度转化] <|ad_begin|> + 10:00 [广告-点击] <|ad_begin|> + 13:04 [视频-长播] <|video_begin|> + 13:04 [视频-长播] <|video_begin|> + 13:04 [视频-长播] <|video_begin|> + 13:23 [视频-长播] <|video_begin|> + 13:38 [视频-收藏/长播] <|video_begin|> + 13:50 [视频-长播] <|video_begin|> + 13:50 [视频-长播] <|video_begin|> + 13:50 [视频-长播] <|video_begin|> + 14:28 [广告-深度转化] <|ad_begin|> + 14:28 [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-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 15:21 [视频-长播] <|video_begin|> + 15:21 [视频-长播] <|video_begin|> + 15:21 [视频-长播] <|video_begin|> +【2026-01-30】 + 00:40 [视频-长播] <|video_begin|> + 00:40 [视频-长播] <|video_begin|> + 03:50 [广告-深度转化] <|ad_begin|> + 03:50 [广告-点击] <|ad_begin|> + 04:31 [广告-深度转化] <|ad_begin|> + 04:31 [广告-点击] <|ad_begin|> + 07:19 [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|> +【2026-01-31】 + 03:39 [视频-长播] <|video_begin|> + 03:55 [广告-深度转化] <|ad_begin|> + 03:55 [广告-点击] <|ad_begin|> + 05:21 [广告-深度转化] <|ad_begin|> + 05:21 [广告-点击] <|ad_begin|> + 08:40 [视频-长播] <|video_begin|> + 08:40 [视频-长播] <|video_begin|> + 09:25 [视频-长播] <|video_begin|> + 09:52 [广告-深度转化] <|ad_begin|> + 09:52 [广告-点击] <|ad_begin|> + 10:24 [广告-深度转化] <|ad_begin|> + 10:24 [广告-点击] <|ad_begin|> + 12:03 [广告-深度转化] <|ad_begin|> + 12:03 [广告-点击] <|ad_begin|> + 14:27 [视频-长播] <|video_begin|> + 14:29 [视频-长播] <|video_begin|> + 15:36 [广告-深度转化] <|ad_begin|> + 15:36 [广告-点击] <|ad_begin|> + 16:17 [视频-长播] <|video_begin|> + 16:30 [视频-长播] <|video_begin|> + 16:47 [视频-长播] <|video_begin|> + 17:03 [视频-长播] <|video_begin|> + 17:03 [视频-长播] <|video_begin|> + 17:11 [视频-长播] <|video_begin|> + 17:11 [视频-长播] <|video_begin|> + 17:11 [视频-长播] <|video_begin|> + 17:19 [视频-长播] <|video_begin|> + 17:19 [视频-长播] <|video_begin|> + 17:26 [视频-长播] <|video_begin|> + 17:26 [视频-长播] <|video_begin|> + 17:33 [视频-长播] <|video_begin|> + 17:33 [视频-长播] <|video_begin|> + 17:33 [视频-长播] <|video_begin|> + 17:46 [视频-长播] <|video_begin|> +【2026-02-01】 + 09:30 [广告-点击] <|ad_begin|> + 09:54 [广告-深度转化] <|ad_begin|> + 09:54 [广告-点击] <|ad_begin|> + 12:29 [广告-深度转化] <|ad_begin|> + 12:29 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 14:52 [广告-深度转化] <|ad_begin|> + 14:52 [广告-点击] <|ad_begin|> + 15:46 [视频-长播] <|video_begin|> +【2026-02-02】 + 13:13 [广告-深度转化] <|ad_begin|> + 13:13 [广告-点击] <|ad_begin|> + 14:26 [广告-深度转化] <|ad_begin|> + 14:26 [广告-点击] <|ad_begin|> + 14:43 [视频-长播] <|video_begin|> + 15:35 [视频-长播] <|video_begin|> + 15:35 [视频-长播] <|video_begin|> + 15:35 [视频-长播] <|video_begin|> + 15:35 [视频-长播] <|video_begin|> + 15:43 [视频-长播] <|video_begin|> + 15:43 [视频-长播] <|video_begin|> + 16:24 [广告-深度转化] <|ad_begin|> + 16:24 [广告-点击] <|ad_begin|> +【2026-02-03】 + 14:08 [广告-点击] <|ad_begin|> + 14:11 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 15:45 [视频-长播] <|video_begin|> + 15:45 [视频-长播] <|video_begin|> + 15:45 [视频-长播] <|video_begin|> + 16:38 [广告-深度转化] <|ad_begin|> + 16:38 [广告-点击] <|ad_begin|> +【2026-02-05】 + 03:12 [视频-长播] <|video_begin|> +【2026-02-06】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +角色任务：你是极端严苛的用户行为数据挖掘专家，同时也是数据格式化专家，请结合以上用户交互历史，面向给定主题提取带有递进行为逻辑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）： 商品-点击、商品-购买、搜索-搜索、直播-关注、直播-首次打赏、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）： +按时间先后提取不超过 5 步的行为链，要求后项交互B必须对前项交互A形成场景需求协同补充、副作用诱因或兴趣细化升级——即 B 必须是由 A 延展出来的需求，严禁提取任何品类平级、内容重复或仅因天气/促销造成的并列凑单行为。 +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 需要体现为交互 A 之后衍生出的副作用需求或新需求，或者 A 是促使 B 出现的诱因。 +3.需求深度细化：在同一主题类目中，交互 B 相比交互 A 应体现出更具体、更详细的需求层次，但不得包含多次重复的购买、点击、搜索等行为，以避免重复节点内容。 +请针对以下主题提取行为逻辑链： +主题：从被动学习到主动实践的烹饪技能闭环 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从被动学习到主动实践的烹饪技能闭环", + "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" + } + ] + } +}/think<|im_end|> +<|im_start|>assistant + +好的，我来分析一下这个任务。任务要求我基于用户的交互历史，挖掘出一条“从被动学习到主动实践的烹饪技能闭环”的行为逻辑链。这意味着我需要找到用户从产生烹饪兴趣、学习具体做法，到最终购买相关食材或半成品，形成一个完整闭环的证据。 + +我首先通览了用户的全部行为时间线，发现其消费行为主要集中在家庭日用品、母婴用品以及大量的食品生鲜上，比如多次购买大米、洗衣液、儿童服饰等，这描绘出一个注重家庭生活、有孩童的居家型用户画像。在内容偏好上，早期用户关注了许多接地气、生活化的直播，而近期则密集地点击和观看了大量以情感纠葛、家庭伦理为主题的短剧广告，这表明用户在内容消费上偏向于剧情化、有冲突感的故事。 + +在这样的背景下，我要寻找烹饪相关的行为链。我注意到在2026年1月29日这一天，用户的行为出现了非常集中的主题性。在13:38，我看到用户有一个关键的动作：收藏并长时间观看了金汤酸菜鱼的制作教学视频 <|video_begin|>。这个“收藏”行为非常重要，它不仅仅是简单的观看，而是代表了用户对这个内容有强烈的认可和后续实践的意图。这可以看作是烹饪兴趣被明确触发的起点。 + +紧接着，我发现用户的行为立刻从内容消费转向了电商消费。就在观看完教学视频的同一天，用户开始密集点击与“酸菜鱼”相关的商品。我看到了一系列的点击行为，比如对天海藏黑鱼金汤酸菜鱼 <|prod_begin|> 的点击。这清晰地表明，用户在学习了如何制作这道菜之后，并没有选择去购买鱼、酸菜、调料等原生食材从头做起，而是立刻开始寻找便捷的半成品解决方案。这是一种非常典型的从“理论学习”到“实践捷径”的思路转变，用户希望将学到的知识快速应用，但又倾向于选择更省时省力的方式。 + +最终，用户的决策迅速闭环。在浏览了多个酸菜鱼半成品后，用户最终购买了之前点击过的天海藏黑鱼金汤酸菜鱼 <|prod_begin|>。这个购买行为直接印证了之前观看和收藏视频 <|video_begin|> 的动机。从学习菜谱，到寻找并筛选合适的半成品，再到最终下单购买，这一系列行为在同一天内完成，形成了一条非常流畅且逻辑严密的“学以致用”链路。用户的需求从一个模糊的“想吃酸菜鱼”或者“想学做菜”，通过一个教学视频被激发并具体化，然后迅速转化为一个明确的购买行为，完成了从兴趣到实践的完整闭环。 + +因此，我倾向于认为，用户的烹饪技能提升路径展现出一种务实的特点：通过观看视频内容激发兴趣并储备知识，然后通过购买半成品来降低实践门槛，快速获得烹饪成就感。基于此，我构建的行为逻辑链如下：首先，用户通过收藏并观看金汤酸菜鱼的教学视频 <|video_begin|> 产生了明确的烹饪意图；接着，在学习后，用户立即点击了相关的半成品 <|prod_begin|>，寻求更便捷的实践方案；最后，用户完成了对该半成品的购买 <|prod_begin|>，实现了从学习到行动的转化。这一系列行为清晰地展示了用户从被动接收信息到主动寻求实践的完整过程，未来向其推荐类似的快手菜或方便料理包，有较大概率被吸引。 +{"logic_chain": {"name": "从被动学习到主动实践的烹饪技能闭环", "events": [{"date": "2026-01-29", "action": "[视频-收藏/长播] <|video_begin|>", "logic": "兴趣触发与知识储备：用户通过观看并收藏烹饪教学视频，主动学习“金汤酸菜鱼”的具体制作方法，表明其产生了制作该菜品的明确意图。"}, {"date": "2026-01-29", "action": "[商品-点击] <|prod_begin|>", "logic": "决策收敛与方案筛选：在学习了完整制作流程后，用户并未选择购买原生食材从零开始，而是立即转向搜索并点击“金汤酸菜鱼”半成品，体现了从“学习复杂流程”到“寻求便捷方案”的认知转变和行为收敛。"}, {"date": "2026-01-29", "action": "[商品-购买] <|prod_begin|>", "logic": "行为闭环：最终购买了与学习内容高度相关的半成品食材，完成了从“学做菜”到“买菜准备做”的完整认知与行动闭环，验证了前序学习行为的直接转化效果。"}]}}<|im_end|> +<|im_start|>user +For a non-relativistic electron in a magnetic field $B=2 \, \text{T}$ oriented along z-axis, calculate the Zeeman energy splitting $\Delta E$ when the magnetic moment couples to spin angular momentum. Use $\mu_B=9.27\times10^{-24} \, \text{J/T}$ and $g_s=2$ for electron g-factor./think<|im_end|> +<|im_start|>assistant + +We are asked: "For a non-relativistic electron in a magnetic field B=2 T oriented along z-axis, calculate the Zeeman energy splitting ΔE when the magnetic moment couples to spin angular momentum. Use μ_B=9.27×10^{-24} J/T and g_s=2 for electron g-factor." + +Interpretation: The Zeeman splitting for an electron in a magnetic field due to its spin: The electron has a magnetic moment μ = -g_s μ_B S/ħ? Actually the spin magnetic moment is μ_s = -g_s μ_B S/ħ? Wait careful: The spin magnetic moment operator is: μ_s = -g_s μ_B S/ħ? Actually the magnetic moment due to spin is μ_s = -g_s μ_B (S/ħ). Since S has units of angular momentum (ħ). Typically we have μ_s = -g_s μ_B S/ħ? But in many contexts we write μ_s = -g_s μ_B (S/ħ). However the energy is E = -μ·B. So for spin: E = - μ_s · B = -(-g_s μ_B (S/ħ))·B = g_s μ_B (S·B)/ħ. Since B is along z, S_z = m_s ħ, with m_s = ±1/2. So E = g_s μ_B m_s (since S·B = S_z B, and dividing by ħ gives m_s B). Actually let's derive: + +μ_s = -g_s μ_B (S/ħ). So -μ_s·B = -(-g_s μ_B/ħ) S·B = (g_s μ_B/ħ) S·B. For B = B z-hat, S·B = S_z B. So E = (g_s μ_B/ħ) S_z B = g_s μ_B (S_z/ħ) B = g_s μ_B m_s B. + +Since S_z eigenvalues: m_s ħ, so m_s = ±1/2. So energies: E_up (m_s=+1/2) = (1/2) g_s μ_B B; E_down (m_s=-1/2) = -(1/2) g_s μ_B B. Then ΔE = E_up - E_down = g_s μ_B B. + +Alternatively sometimes they take g_s=2, so ΔE = 2 μ_B B. With B=2 T, μ_B=9.27e-24 J/T, so ΔE = 2 * 9.27e-24 * 2 = 4 * 9.27e-24? Wait careful: ΔE = g_s μ_B B = 2 * 9.27e-24 J/T * 2 T = 4 * 9.27e-24 J = 3.708e-23 J. But check: 2*2 =4, so 4*9.27e-24 = 37.08e-24? Actually 9.27e-24 * 4 = 3.708e-23. Yes. + +But we could also consider splitting between m_s=±1/2 is g_s μ_B B, which is correct. + +The problem statement says "Zeeman energy splitting ΔE when the magnetic moment couples to spin angular momentum." So they want the difference between the two energy levels. + +Thus answer: ΔE = g_s μ_B B = 2 * 9.27×10^{-24} J/T * 2 T = 3.708×10^{-23} J. + +We could also express in eV if needed? But the question uses J/T, so likely answer in J. They didn't ask for eV. + +Thus final answer: ΔE = 3.71×10^{-23} J (approx). But we can keep full: 3.708×10^{-23} J. + +But maybe they want in eV: 1 eV = 1.602×10^{-19} J, so ΔE ≈ 3.708e-23 / 1.602e-19 = 2.31e-4 eV. But since they gave μ_B in J/T, they expect J. + +We'll provide: ΔE = 3.71×10^{-23} J (maybe rounding). Actually 9.27×10^{-24} * 4 = 3.708×10^{-23}. Keep three significant figures? μ_B given as 9.27×10^{-24} has three significant figures, B=2 T is exact? Possibly 2.0? Might be considered exact as 2. So we can give ΔE = 3.71×10^{-23} J. + +But they might want exact expression: ΔE = g_s μ_B B = 2 * 9.27e-24 * 2 = 3.708e-23 J. + +Thus final answer. + +The Zeeman energy splitting for an electron due to its spin is given by: + +\[ +\Delta E = g_s \mu_B B +\] + +Given \(g_s = 2\), \(\mu_B = 9.27 \times 10^{-24} \, \text{J/T}\), and \(B = 2 \, \text{T}\), + +\[ +\Delta E = 2 \times (9.27 \times 10^{-24} \, \text{J/T}) \times 2 \, \text{T} = 4 \times 9.27 \times 10^{-24} \, \text{J} = 3.708 \times 10^{-23} \, \text{J}. +\] + +Thus, the energy splitting is \(\boxed{3.71 \times 10^{-23} \, \text{J}}\) (rounded to three significant figures).<|im_end|> +<|endoftext|><|endoftext|><|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, -100, 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+{"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|>","<|living_begin|>","<|prod_begin|>","<|living_begin|>","<|living_begin|>","<|living_begin|>","<|prod_begin|>","<|prod_begin|>","<|living_begin|>","<|prod_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>"]<|im_end|> + +好的，我来分析一下这个任务。任务要求我基于用户的交互历史，挖掘出一条“从被动学习到主动实践的烹饪技能闭环”的行为逻辑链。这意味着我需要找到用户从产生烹饪兴趣、学习具体做法，到最终购买相关食材或半成品，形成一个完整闭环的证据。 + +我首先通览了用户的全部行为时间线，发现其消费行为主要集中在家庭日用品、母婴用品以及大量的食品生鲜上，比如多次购买大米、洗衣液、儿童服饰等，这描绘出一个注重家庭生活、有孩童的居家型用户画像。在内容偏好上，早期用户关注了许多接地气、生活化的直播，而近期则密集地点击和观看了大量以情感纠葛、家庭伦理为主题的短剧广告，这表明用户在内容消费上偏向于剧情化、有冲突感的故事。 + +在这样的背景下，我要寻找烹饪相关的行为链。我注意到在2026年1月29日这一天，用户的行为出现了非常集中的主题性。在13:38，我看到用户有一个关键的动作：收藏并长时间观看了金汤酸菜鱼的制作教学视频 <|video_begin|>。这个“收藏”行为非常重要，它不仅仅是简单的观看，而是代表了用户对这个内容有强烈的认可和后续实践的意图。这可以看作是烹饪兴趣被明确触发的起点。 + +紧接着，我发现用户的行为立刻从内容消费转向了电商消费。就在观看完教学视频的同一天，用户开始密集点击与“酸菜鱼”相关的商品。我看到了一系列的点击行为，比如对天海藏黑鱼金汤酸菜鱼 <|prod_begin|> 的点击。这清晰地表明，用户在学习了如何制作这道菜之后，并没有选择去购买鱼、酸菜、调料等原生食材从头做起，而是立刻开始寻找便捷的半成品解决方案。这是一种非常典型的从“理论学习”到“实践捷径”的思路转变，用户希望将学到的知识快速应用，但又倾向于选择更省时省力的方式。 + +最终，用户的决策迅速闭环。在浏览了多个酸菜鱼半成品后，用户最终购买了之前点击过的天海藏黑鱼金汤酸菜鱼 <|prod_begin|>。这个购买行为直接印证了之前观看和收藏视频 <|video_begin|> 的动机。从学习菜谱，到寻找并筛选合适的半成品，再到最终下单购买，这一系列行为在同一天内完成，形成了一条非常流畅且逻辑严密的“学以致用”链路。用户的需求从一个模糊的“想吃酸菜鱼”或者“想学做菜”，通过一个教学视频被激发并具体化，然后迅速转化为一个明确的购买行为，完成了从兴趣到实践的完整闭环。 + +因此，我倾向于认为，用户的烹饪技能提升路径展现出一种务实的特点：通过观看视频内容激发兴趣并储备知识，然后通过购买半成品来降低实践门槛，快速获得烹饪成就感。基于此，我构建的行为逻辑链如下：首先，用户通过收藏并观看金汤酸菜鱼的教学视频 <|video_begin|> 产生了明确的烹饪意图；接着，在学习后，用户立即点击了相关的半成品 <|prod_begin|>，寻求更便捷的实践方案；最后，用户完成了对该半成品的购买 <|prod_begin|>，实现了从学习到行动的转化。这一系列行为清晰地展示了用户从被动接收信息到主动寻求实践的完整过程，未来向其推荐类似的快手菜或方便料理包，有较大概率被吸引。 +{"logic_chain": {"name": "从被动学习到主动实践的烹饪技能闭环", "events": [{"date": "2026-01-29", "action": "[视频-收藏/长播] <|video_begin|>", "logic": "兴趣触发与知识储备：用户通过观看并收藏烹饪教学视频，主动学习“金汤酸菜鱼”的具体制作方法，表明其产生了制作该菜品的明确意图。"}, {"date": "2026-01-29", "action": "[商品-点击] <|prod_begin|>", "logic": "决策收敛与方案筛选：在学习了完整制作流程后，用户并未选择购买原生食材从零开始，而是立即转向搜索并点击“金汤酸菜鱼”半成品，体现了从“学习复杂流程”到“寻求便捷方案”的认知转变和行为收敛。"}, {"date": "2026-01-29", "action": "[商品-购买] <|prod_begin|>", "logic": "行为闭环：最终购买了与学习内容高度相关的半成品食材，完成了从“学做菜”到“买菜准备做”的完整认知与行动闭环，验证了前序学习行为的直接转化效果。"}]}}<|im_end|> + +We are asked: "For a non-relativistic electron in a magnetic field B=2 T oriented along z-axis, calculate the Zeeman energy splitting ΔE when the magnetic moment couples to spin angular momentum. Use μ_B=9.27×10^{-24} J/T and g_s=2 for electron g-factor." + +Interpretation: The Zeeman splitting for an electron in a magnetic field due to its spin: The electron has a magnetic moment μ = -g_s μ_B S/ħ? Actually the spin magnetic moment is μ_s = -g_s μ_B S/ħ? Wait careful: The spin magnetic moment operator is: μ_s = -g_s μ_B S/ħ? Actually the magnetic moment due to spin is μ_s = -g_s μ_B (S/ħ). Since S has units of angular momentum (ħ). Typically we have μ_s = -g_s μ_B S/ħ? But in many contexts we write μ_s = -g_s μ_B (S/ħ). However the energy is E = -μ·B. So for spin: E = - μ_s · B = -(-g_s μ_B (S/ħ))·B = g_s μ_B (S·B)/ħ. Since B is along z, S_z = m_s ħ, with m_s = ±1/2. So E = g_s μ_B m_s (since S·B = S_z B, and dividing by ħ gives m_s B). Actually let's derive: + +μ_s = -g_s μ_B (S/ħ). So -μ_s·B = -(-g_s μ_B/ħ) S·B = (g_s μ_B/ħ) S·B. For B = B z-hat, S·B = S_z B. So E = (g_s μ_B/ħ) S_z B = g_s μ_B (S_z/ħ) B = g_s μ_B m_s B. + +Since S_z eigenvalues: m_s ħ, so m_s = ±1/2. So energies: E_up (m_s=+1/2) = (1/2) g_s μ_B B; E_down (m_s=-1/2) = -(1/2) g_s μ_B B. Then ΔE = E_up - E_down = g_s μ_B B. + +Alternatively sometimes they take g_s=2, so ΔE = 2 μ_B B. With B=2 T, μ_B=9.27e-24 J/T, so ΔE = 2 * 9.27e-24 * 2 = 4 * 9.27e-24? Wait careful: ΔE = g_s μ_B B = 2 * 9.27e-24 J/T * 2 T = 4 * 9.27e-24 J = 3.708e-23 J. But check: 2*2 =4, so 4*9.27e-24 = 37.08e-24? Actually 9.27e-24 * 4 = 3.708e-23. Yes. + +But we could also consider splitting between m_s=±1/2 is g_s μ_B B, which is correct. + +The problem statement says "Zeeman energy splitting ΔE when the magnetic moment couples to spin angular momentum." So they want the difference between the two energy levels. + +Thus answer: ΔE = g_s μ_B B = 2 * 9.27×10^{-24} J/T * 2 T = 3.708×10^{-23} J. + +We could also express in eV if needed? But the question uses J/T, so likely answer in J. They didn't ask for eV. + +Thus final answer: ΔE = 3.71×10^{-23} J (approx). But we can keep full: 3.708×10^{-23} J. + +But maybe they want in eV: 1 eV = 1.602×10^{-19} J, so ΔE ≈ 3.708e-23 / 1.602e-19 = 2.31e-4 eV. But since they gave μ_B in J/T, they expect J. + +We'll provide: ΔE = 3.71×10^{-23} J (maybe rounding). Actually 9.27×10^{-24} * 4 = 3.708×10^{-23}. Keep three significant figures? μ_B given as 9.27×10^{-24} has three significant figures, B=2 T is exact? Possibly 2.0? Might be considered exact as 2. So we can give ΔE = 3.71×10^{-23} J. + +But they might want exact expression: ΔE = g_s μ_B B = 2 * 9.27e-24 * 2 = 3.708e-23 J. + +Thus final answer. + +The Zeeman energy splitting for an electron due to its spin is given by: + +\[ +\Delta E = g_s \mu_B B +\] + +Given \(g_s = 2\), \(\mu_B = 9.27 \times 10^{-24} \, \text{J/T}\), and \(B = 2 \, \text{T}\), + +\[ +\Delta E = 2 \times (9.27 \times 10^{-24} \, \text{J/T}) \times 2 \, \text{T} = 4 \times 9.27 \times 10^{-24} \, \text{J} = 3.708 \times 10^{-23} \, \text{J}. +\] + +Thus, the energy splitting is \(\boxed{3.71 \times 10^{-23} \, \text{J}}\) (rounded to three significant figures).<|im_end|> + +[INFO|configuration_utils.py:769] 2026-07-15 06:33:42,512 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-15 06:33:42,513 >> 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|2026-07-15 06:33:42] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +[INFO|2026-07-15 06:33:42] llamafactory.model.model_utils.liger_kernel:144 >> Liger kernel has been applied to the model. +[INFO|modeling_utils.py:748] 2026-07-15 06:33:42,826 >> loading weights file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/model.safetensors +[INFO|modeling_utils.py:821] 2026-07-15 06:33:42,826 >> Will use dtype=torch.bfloat16 as defined in model's config object +[INFO|configuration_utils.py:1027] 2026-07-15 06:33:42,875 >> 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] 2026-07-15 06:33:43,176 >> 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] 2026-07-15 06:33:43,176 >> Could not locate the custom_generate/generate.py inside /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018. +[INFO|2026-07-15 06:33:43] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-07-15 06:33:43] llamafactory.model.model_utils.attention:144 >> Using FlashAttention-2 for faster training and inference. +[INFO|2026-07-15 06:33:43] llamafactory.model.adapter:144 >> Pure bf16 / BAdam detected, remaining trainable params in half precision. +[INFO|2026-07-15 06:33:43] llamafactory.model.adapter:144 >> Fine-tuning method: Full +[INFO|2026-07-15 06:33:43] llamafactory.model.loader:144 >> trainable params: 801,433,600 || all params: 801,433,600 || trainable%: 100.0000 +[INFO|trainer.py:1469] 2026-07-15 06:33:43,304 >> ***** Running training ***** +[INFO|trainer.py:1470] 2026-07-15 06:33:43,304 >> Num examples = 1,903 +[INFO|trainer.py:1471] 2026-07-15 06:33:43,304 >> Num Epochs = 2 +[INFO|trainer.py:1472] 2026-07-15 06:33:43,304 >> Num update steps per epoch = 476 +[INFO|trainer.py:1473] 2026-07-15 06:33:43,304 >> Instantaneous batch size per device = 1 +[INFO|trainer.py:1476] 2026-07-15 06:33:43,304 >> Total train batch size (w. parallel, distributed & accumulation) = 4 +[INFO|trainer.py:1477] 2026-07-15 06:33:43,304 >> Gradient Accumulation steps = 4 +[INFO|trainer.py:1478] 2026-07-15 06:33:43,304 >> Total optimization steps = 714 +[INFO|trainer.py:1479] 2026-07-15 06:33:43,304 >> Number of trainable parameters = 801,433,600 + 0%| | 0/714 [00:00> `use_return_dict` is deprecated! Use `return_dict` instead! + 0%| | 1/714 [00:07<1:34:51, 7.98s/it] 0%| | 2/714 [00:15<1:32:44, 7.82s/it] 0%| | 3/714 [00:23<1:31:27, 7.72s/it] 1%| | 4/714 [00:30<1:31:14, 7.71s/it] 1%| | 5/714 [00:38<1:29:10, 7.55s/it] 1%| | 6/714 [00:46<1:30:39, 7.68s/it] 1%| | 7/714 [00:54<1:32:52, 7.88s/it] 1%| | 8/714 [01:03<1:35:59, 8.16s/it] 1%|▏ | 9/714 [01:11<1:35:26, 8.12s/it] 1%|▏ | 10/714 [01:18<1:32:12, 7.86s/it] {'loss': '1.809', 'grad_norm': '9.625', 'learning_rate': '1.023e-07', 'epoch': '0.02102'} + 1%|▏ | 10/714 [01:18<1:32:12, 7.86s/it] 2%|▏ | 11/714 [01:26<1:31:52, 7.84s/it] 2%|▏ | 12/714 [01:34<1:32:10, 7.88s/it] 2%|▏ | 13/714 [01:42<1:32:56, 7.96s/it] 2%|▏ | 14/714 [01:51<1:35:34, 8.19s/it] 2%|▏ | 15/714 [01:58<1:33:43, 8.04s/it] 2%|▏ | 16/714 [02:07<1:35:32, 8.21s/it] 2%|▏ | 17/714 [02:15<1:33:45, 8.07s/it] 3%|▎ | 18/714 [02:22<1:31:36, 7.90s/it] 3%|▎ | 19/714 [02:31<1:33:00, 8.03s/it] 3%|▎ | 20/714 [02:38<1:29:59, 7.78s/it] {'loss': '1.956', 'grad_norm': '10.56', 'learning_rate': '2.159e-07', 'epoch': '0.04204'} + 3%|▎ | 20/714 [02:38<1:29:59, 7.78s/it] 3%|▎ | 21/714 [02:45<1:26:33, 7.49s/it] 3%|▎ | 22/714 [02:52<1:25:22, 7.40s/it] 3%|▎ | 23/714 [03:01<1:30:14, 7.84s/it] 3%|▎ | 24/714 [03:08<1:28:42, 7.71s/it] 4%|▎ | 25/714 [03:16<1:30:36, 7.89s/it] 4%|▎ | 26/714 [03:24<1:30:21, 7.88s/it] 4%|▍ | 27/714 [03:32<1:29:36, 7.83s/it] 4%|▍ | 28/714 [03:39<1:27:46, 7.68s/it] 4%|▍ | 29/714 [03:47<1:27:11, 7.64s/it] 4%|▍ | 30/714 [03:54<1:26:28, 7.58s/it] {'loss': '1.919', 'grad_norm': '8.375', 'learning_rate': '2.499e-07', 'epoch': '0.06306'} + 4%|▍ | 30/714 [03:54<1:26:28, 7.58s/it] 4%|▍ | 31/714 [04:02<1:26:54, 7.63s/it] 4%|▍ | 32/714 [04:10<1:28:27, 7.78s/it] 5%|▍ | 33/714 [04:18<1:30:18, 7.96s/it] 5%|▍ | 34/714 [04:26<1:27:09, 7.69s/it] 5%|▍ | 35/714 [04:33<1:27:49, 7.76s/it] 5%|▌ | 36/714 [04:41<1:26:00, 7.61s/it] 5%|▌ | 37/714 [04:48<1:24:54, 7.52s/it] 5%|▌ | 38/714 [04:57<1:28:39, 7.87s/it] 5%|▌ | 39/714 [05:03<1:23:52, 7.46s/it] 6%|▌ | 40/714 [05:12<1:27:58, 7.83s/it] {'loss': '1.811', 'grad_norm': '5.219', 'learning_rate': '2.496e-07', 'epoch': '0.08408'} + 6%|▌ | 40/714 [05:12<1:27:58, 7.83s/it] 6%|▌ | 41/714 [05:20<1:27:04, 7.76s/it] 6%|▌ | 42/714 [05:27<1:26:35, 7.73s/it] 6%|▌ | 43/714 [05:35<1:25:29, 7.64s/it] 6%|▌ | 44/714 [05:41<1:22:07, 7.35s/it] 6%|▋ | 45/714 [05:49<1:23:25, 7.48s/it] 6%|▋ | 46/714 [05:57<1:24:50, 7.62s/it] 7%|▋ | 47/714 [06:05<1:25:14, 7.67s/it] 7%|▋ | 48/714 [06:14<1:29:04, 8.02s/it] 7%|▋ | 49/714 [06:21<1:27:27, 7.89s/it] 7%|▋ | 50/714 [06:30<1:29:14, 8.06s/it] {'loss': '1.931', 'grad_norm': '7.156', 'learning_rate': '2.491e-07', 'epoch': '0.1051'} + 7%|▋ | 50/714 [06:30<1:29:14, 8.06s/it] 7%|▋ | 51/714 [06:37<1:26:52, 7.86s/it] 7%|▋ | 52/714 [06:43<1:21:46, 7.41s/it] 7%|▋ | 53/714 [06:52<1:23:54, 7.62s/it] 8%|▊ | 54/714 [06:59<1:23:18, 7.57s/it] 8%|▊ | 55/714 [07:07<1:24:01, 7.65s/it] 8%|▊ | 56/714 [07:15<1:23:53, 7.65s/it] 8%|▊ | 57/714 [07:22<1:22:11, 7.51s/it] 8%|▊ | 58/714 [07:29<1:22:15, 7.52s/it] 8%|▊ | 59/714 [07:36<1:18:20, 7.18s/it] 8%|▊ | 60/714 [07:45<1:24:15, 7.73s/it] {'loss': '1.96', 'grad_norm': '5.219', 'learning_rate': '2.482e-07', 'epoch': '0.1261'} + 8%|▊ | 60/714 [07:45<1:24:15, 7.73s/it] 9%|▊ | 61/714 [07:52<1:24:05, 7.73s/it] 9%|▊ | 62/714 [08:01<1:26:02, 7.92s/it] 9%|▉ | 63/714 [08:09<1:25:36, 7.89s/it] 9%|▉ | 64/714 [08:17<1:27:45, 8.10s/it] 9%|▉ | 65/714 [08:25<1:26:57, 8.04s/it] 9%|▉ | 66/714 [08:33<1:25:00, 7.87s/it] 9%|▉ | 67/714 [08:41<1:25:45, 7.95s/it] 10%|▉ | 68/714 [08:49<1:26:26, 8.03s/it] 10%|▉ | 69/714 [08:58<1:29:23, 8.32s/it] 10%|▉ | 70/714 [09:05<1:26:44, 8.08s/it] {'loss': '1.989', 'grad_norm': '15.88', 'learning_rate': '2.472e-07', 'epoch': '0.1471'} + 10%|▉ | 70/714 [09:05<1:26:44, 8.08s/it] 10%|▉ | 71/714 [09:13<1:24:58, 7.93s/it] 10%|█ | 72/714 [09:22<1:27:29, 8.18s/it] 10%|█ | 73/714 [09:30<1:28:54, 8.32s/it] 10%|█ | 74/714 [09:38<1:25:42, 8.04s/it] 11%|█ | 75/714 [09:45<1:23:04, 7.80s/it] 11%|█ | 76/714 [09:52<1:21:21, 7.65s/it] 11%|█ | 77/714 [10:00<1:22:16, 7.75s/it] 11%|█ | 78/714 [10:08<1:22:07, 7.75s/it] 11%|█ | 79/714 [10:14<1:16:19, 7.21s/it] 11%|█ | 80/714 [10:22<1:17:49, 7.37s/it] {'loss': '1.913', 'grad_norm': '11.69', 'learning_rate': '2.458e-07', 'epoch': '0.1682'} + 11%|█ | 80/714 [10:22<1:17:49, 7.37s/it] 11%|█▏ | 81/714 [10:29<1:17:38, 7.36s/it] 11%|█▏ | 82/714 [10:37<1:19:11, 7.52s/it] 12%|█▏ | 83/714 [10:45<1:21:25, 7.74s/it] 12%|█▏ | 84/714 [10:53<1:20:38, 7.68s/it] 12%|█▏ | 85/714 [11:01<1:20:47, 7.71s/it] 12%|█▏ | 86/714 [11:09<1:22:27, 7.88s/it] 12%|█▏ | 87/714 [11:15<1:18:23, 7.50s/it] 12%|█▏ | 88/714 [11:23<1:17:54, 7.47s/it] 12%|█▏ | 89/714 [11:31<1:19:10, 7.60s/it] 13%|█▎ | 90/714 [11:38<1:17:09, 7.42s/it] {'loss': '1.989', 'grad_norm': '7.375', 'learning_rate': '2.443e-07', 'epoch': '0.1892'} + 13%|█▎ | 90/714 [11:38<1:17:09, 7.42s/it] 13%|█▎ | 91/714 [11:45<1:17:57, 7.51s/it] 13%|█▎ | 92/714 [11:54<1:20:57, 7.81s/it] 13%|█▎ | 93/714 [12:03<1:23:43, 8.09s/it] 13%|█▎ | 94/714 [12:11<1:23:16, 8.06s/it] 13%|█▎ | 95/714 [12:19<1:23:20, 8.08s/it] 13%|█▎ | 96/714 [12:26<1:20:38, 7.83s/it] 14%|█▎ | 97/714 [12:34<1:20:50, 7.86s/it] 14%|█▎ | 98/714 [12:43<1:23:54, 8.17s/it] 14%|█▍ | 99/714 [12:50<1:21:41, 7.97s/it] 14%|█▍ | 100/714 [12:58<1:22:00, 8.01s/it] {'loss': '1.816', 'grad_norm': '9.25', 'learning_rate': '2.424e-07', 'epoch': '0.2102'} + 14%|█▍ | 100/714 [12:59<1:22:00, 8.01s/it] 14%|█▍ | 101/714 [13:06<1:20:20, 7.86s/it] 14%|█▍ | 102/714 [13:14<1:19:21, 7.78s/it] 14%|█▍ | 103/714 [13:21<1:19:15, 7.78s/it] 15%|█▍ | 104/714 [13:29<1:17:57, 7.67s/it] 15%|█▍ | 105/714 [13:36<1:17:37, 7.65s/it] 15%|█▍ | 106/714 [13:44<1:16:54, 7.59s/it] 15%|█▍ | 107/714 [13:52<1:18:53, 7.80s/it] 15%|█▌ | 108/714 [14:00<1:17:43, 7.70s/it] 15%|█▌ | 109/714 [14:06<1:14:35, 7.40s/it] 15%|█▌ | 110/714 [14:14<1:16:41, 7.62s/it] {'loss': '1.974', 'grad_norm': '5.438', 'learning_rate': '2.404e-07', 'epoch': '0.2312'} + 15%|█▌ | 110/714 [14:14<1:16:41, 7.62s/it] 16%|█▌ | 111/714 [14:21<1:13:00, 7.27s/it] 16%|█▌ | 112/714 [14:28<1:14:01, 7.38s/it] 16%|█▌ | 113/714 [14:37<1:16:42, 7.66s/it] 16%|█▌ | 114/714 [14:44<1:16:35, 7.66s/it] 16%|█▌ | 115/714 [14:53<1:18:27, 7.86s/it] 16%|█▌ | 116/714 [15:01<1:19:14, 7.95s/it] 16%|█▋ | 117/714 [15:09<1:18:44, 7.91s/it] 17%|█▋ | 118/714 [15:17<1:18:36, 7.91s/it] 17%|█▋ | 119/714 [15:24<1:16:44, 7.74s/it] 17%|█▋ | 120/714 [15:31<1:13:25, 7.42s/it] {'loss': '1.861', 'grad_norm': '8.375', 'learning_rate': '2.381e-07', 'epoch': '0.2522'} + 17%|█▋ | 120/714 [15:31<1:13:25, 7.42s/it] 17%|█▋ | 121/714 [15:38<1:14:17, 7.52s/it] 17%|█▋ | 122/714 [15:46<1:13:37, 7.46s/it] 17%|█▋ | 123/714 [15:54<1:14:48, 7.60s/it] 17%|█▋ | 124/714 [16:01<1:15:10, 7.64s/it] 18%|█▊ | 125/714 [16:09<1:14:02, 7.54s/it] 18%|█▊ | 126/714 [16:17<1:16:09, 7.77s/it] 18%|█▊ | 127/714 [16:25<1:15:25, 7.71s/it] 18%|█▊ | 128/714 [16:32<1:14:11, 7.60s/it] 18%|█▊ | 129/714 [16:40<1:14:42, 7.66s/it] 18%|█▊ | 130/714 [16:48<1:15:51, 7.79s/it] {'loss': '1.968', 'grad_norm': '6.406', 'learning_rate': '2.355e-07', 'epoch': '0.2733'} + 18%|█▊ | 130/714 [16:48<1:15:51, 7.79s/it] 18%|█▊ | 131/714 [16:56<1:17:37, 7.99s/it] 18%|█▊ | 132/714 [17:04<1:17:57, 8.04s/it] 19%|█▊ | 133/714 [17:12<1:17:08, 7.97s/it] 19%|█▉ | 134/714 [17:20<1:17:36, 8.03s/it] 19%|█▉ | 135/714 [17:29<1:17:47, 8.06s/it] 19%|█▉ | 136/714 [17:37<1:17:44, 8.07s/it] 19%|█▉ | 137/714 [17:43<1:12:53, 7.58s/it] 19%|█▉ | 138/714 [17:51<1:14:10, 7.73s/it] 19%|█▉ | 139/714 [17:58<1:12:44, 7.59s/it] 20%|█▉ | 140/714 [18:06<1:12:12, 7.55s/it] {'loss': '1.905', 'grad_norm': '7.844', 'learning_rate': '2.328e-07', 'epoch': '0.2943'} + 20%|█▉ | 140/714 [18:06<1:12:12, 7.55s/it] 20%|█▉ | 141/714 [18:15<1:15:09, 7.87s/it] 20%|█▉ | 142/714 [18:21<1:10:15, 7.37s/it] 20%|██ | 143/714 [18:28<1:10:58, 7.46s/it] 20%|██ | 144/714 [18:37<1:13:44, 7.76s/it] 20%|██ | 145/714 [18:44<1:11:51, 7.58s/it] 20%|██ | 146/714 [18:53<1:14:30, 7.87s/it] 21%|██ | 147/714 [19:01<1:14:55, 7.93s/it] 21%|██ | 148/714 [19:09<1:15:54, 8.05s/it] 21%|██ | 149/714 [19:18<1:17:46, 8.26s/it] 21%|██ | 150/714 [19:25<1:14:49, 7.96s/it] {'loss': '1.801', 'grad_norm': '8.188', 'learning_rate': '2.298e-07', 'epoch': '0.3153'} + 21%|██ | 150/714 [19:25<1:14:49, 7.96s/it] 21%|██ | 151/714 [19:33<1:16:07, 8.11s/it] 21%|██▏ | 152/714 [19:42<1:16:18, 8.15s/it] 21%|██▏ | 153/714 [19:50<1:16:36, 8.19s/it] 22%|██▏ | 154/714 [19:59<1:18:31, 8.41s/it] 22%|██▏ | 155/714 [20:07<1:18:20, 8.41s/it] 22%|██▏ | 156/714 [20:16<1:18:35, 8.45s/it] 22%|██▏ | 157/714 [20:25<1:19:53, 8.61s/it] 22%|██▏ | 158/714 [20:33<1:18:51, 8.51s/it] 22%|██▏ | 159/714 [20:40<1:15:05, 8.12s/it] 22%|██▏ | 160/714 [20:48<1:13:03, 7.91s/it] {'loss': '1.848', 'grad_norm': '5.688', 'learning_rate': '2.266e-07', 'epoch': '0.3363'} + 22%|██▏ | 160/714 [20:48<1:13:03, 7.91s/it] 23%|██▎ | 161/714 [20:56<1:15:09, 8.16s/it] 23%|██▎ | 162/714 [21:04<1:14:42, 8.12s/it] 23%|██▎ | 163/714 [21:11<1:10:42, 7.70s/it] 23%|██▎ | 164/714 [21:19<1:10:08, 7.65s/it] 23%|██▎ | 165/714 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25%|██▌ | 182/714 [23:37<1:07:25, 7.60s/it] 26%|██▌ | 183/714 [23:45<1:07:33, 7.63s/it] 26%|██▌ | 184/714 [23:53<1:08:01, 7.70s/it] 26%|██▌ | 185/714 [24:00<1:06:55, 7.59s/it] 26%|██▌ | 186/714 [24:09<1:10:22, 8.00s/it] 26%|██▌ | 187/714 [24:17<1:08:53, 7.84s/it] 26%|██▋ | 188/714 [24:24<1:07:56, 7.75s/it] 26%|██▋ | 189/714 [24:31<1:06:35, 7.61s/it] 27%|██▋ | 190/714 [24:40<1:08:11, 7.81s/it] {'loss': '1.997', 'grad_norm': '5.188', 'learning_rate': '2.158e-07', 'epoch': '0.3994'} + 27%|██▋ | 190/714 [24:40<1:08:11, 7.81s/it] 27%|██▋ | 191/714 [24:48<1:09:38, 7.99s/it] 27%|██▋ | 192/714 [24:56<1:08:22, 7.86s/it] 27%|██▋ | 193/714 [25:04<1:08:51, 7.93s/it] 27%|██▋ | 194/714 [25:12<1:10:10, 8.10s/it] 27%|██▋ | 195/714 [25:19<1:07:32, 7.81s/it] 27%|██▋ | 196/714 [25:27<1:07:00, 7.76s/it] 28%|██▊ | 197/714 [25:34<1:04:05, 7.44s/it] 28%|██▊ | 198/714 [25:41<1:04:35, 7.51s/it] 28%|██▊ | 199/714 [25:49<1:03:55, 7.45s/it] 28%|██▊ | 200/714 [25:56<1:04:18, 7.51s/it] {'loss': '1.9', 'grad_norm': 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[44:07<47:23, 7.60s/it] {'loss': '1.878', 'grad_norm': '9.625', 'learning_rate': '1.414e-07', 'epoch': '0.7147'} + 48%|████▊ | 340/714 [44:07<47:23, 7.60s/it] 48%|████▊ | 341/714 [44:14<46:34, 7.49s/it] 48%|████▊ | 342/714 [44:23<48:06, 7.76s/it] 48%|████▊ | 343/714 [44:30<46:15, 7.48s/it] 48%|████▊ | 344/714 [44:39<48:50, 7.92s/it] 48%|████▊ | 345/714 [44:47<48:47, 7.93s/it] 48%|████▊ | 346/714 [44:55<48:39, 7.93s/it] 49%|████▊ | 347/714 [45:02<48:30, 7.93s/it] 49%|████▊ | 348/714 [45:10<47:52, 7.85s/it] 49%|████▉ | 349/714 [45:18<47:10, 7.76s/it] 49%|████▉ | 350/714 [45:25<46:54, 7.73s/it] {'loss': '1.899', 'grad_norm': '9.875', 'learning_rate': '1.358e-07', 'epoch': '0.7357'} + 49%|████▉ | 350/714 [45:25<46:54, 7.73s/it] 49%|████▉ | 351/714 [45:33<46:06, 7.62s/it] 49%|████▉ | 352/714 [45:40<45:18, 7.51s/it] 49%|████▉ | 353/714 [45:48<46:30, 7.73s/it] 50%|████▉ | 354/714 [45:56<46:52, 7.81s/it] 50%|████▉ | 355/714 [46:04<46:31, 7.78s/it] 50%|████▉ | 356/714 [46:12<46:21, 7.77s/it] 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'1.131e-07', 'epoch': '0.8198'} + 55%|█████▍ | 390/714 [50:39<40:59, 7.59s/it] 55%|█████▍ | 391/714 [50:47<40:46, 7.57s/it] 55%|█████▍ | 392/714 [50:55<41:42, 7.77s/it] 55%|█████▌ | 393/714 [51:02<40:34, 7.58s/it] 55%|█████▌ | 394/714 [51:10<41:23, 7.76s/it] 55%|█████▌ | 395/714 [51:19<43:11, 8.13s/it] 55%|█████▌ | 396/714 [51:27<42:50, 8.08s/it] 56%|█████▌ | 397/714 [51:36<43:07, 8.16s/it] 56%|█████▌ | 398/714 [51:44<43:01, 8.17s/it] 56%|█████▌ | 399/714 [51:51<41:32, 7.91s/it] 56%|█████▌ | 400/714 [51:59<41:16, 7.89s/it] {'loss': '1.948', 'grad_norm': '9.188', 'learning_rate': '1.075e-07', 'epoch': '0.8408'} + 56%|█████▌ | 400/714 [51:59<41:16, 7.89s/it] 56%|█████▌ | 401/714 [52:07<40:44, 7.81s/it] 56%|█████▋ | 402/714 [52:15<41:02, 7.89s/it] 56%|█████▋ | 403/714 [52:23<40:54, 7.89s/it] 57%|█████▋ | 404/714 [52:30<40:35, 7.85s/it] 57%|█████▋ | 405/714 [52:38<40:24, 7.85s/it] 57%|█████▋ | 406/714 [52:46<40:39, 7.92s/it] 57%|█████▋ | 407/714 [52:54<40:02, 7.82s/it] 57%|█████▋ | 408/714 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[55:06<37:01, 7.66s/it] 60%|█████▉ | 425/714 [55:13<36:25, 7.56s/it] 60%|█████▉ | 426/714 [55:21<37:12, 7.75s/it] 60%|█████▉ | 427/714 [55:29<36:23, 7.61s/it] 60%|█████▉ | 428/714 [55:37<37:15, 7.82s/it] 60%|██████ | 429/714 [55:44<36:26, 7.67s/it] 60%|██████ | 430/714 [55:52<35:50, 7.57s/it] {'loss': '2.003', 'grad_norm': '6.188', 'learning_rate': '9.082e-08', 'epoch': '0.9038'} + 60%|██████ | 430/714 [55:52<35:50, 7.57s/it] 60%|██████ | 431/714 [56:00<36:30, 7.74s/it] 61%|██████ | 432/714 [56:08<36:43, 7.81s/it] 61%|██████ | 433/714 [56:15<36:27, 7.79s/it] 61%|██████ | 434/714 [56:23<36:38, 7.85s/it] 61%|██████ | 435/714 [56:32<37:36, 8.09s/it] 61%|██████ | 436/714 [56:40<37:51, 8.17s/it] 61%|██████ | 437/714 [56:49<37:40, 8.16s/it] 61%|██████▏ | 438/714 [56:56<36:23, 7.91s/it] 61%|██████▏ | 439/714 [57:04<36:19, 7.93s/it] 62%|██████▏ | 440/714 [57:11<35:36, 7.80s/it] {'loss': '1.943', 'grad_norm': '6.281', 'learning_rate': '8.54e-08', 'epoch': '0.9249'} + 62%|██████▏ | 440/714 [57:11<35:36, 7.80s/it] 62%|██████▏ | 441/714 [57:19<35:31, 7.81s/it] 62%|██████▏ | 442/714 [57:27<35:14, 7.77s/it] 62%|██████▏ | 443/714 [57:34<34:27, 7.63s/it] 62%|██████▏ | 444/714 [57:42<34:05, 7.58s/it] 62%|██████▏ | 445/714 [57:50<34:22, 7.67s/it] 62%|██████▏ | 446/714 [57:57<33:31, 7.51s/it] 63%|██████▎ | 447/714 [58:04<33:19, 7.49s/it] 63%|██████▎ | 448/714 [58:11<33:03, 7.46s/it] 63%|██████▎ | 449/714 [58:20<33:44, 7.64s/it] 63%|██████▎ | 450/714 [58:28<34:04, 7.74s/it] {'loss': '1.88', 'grad_norm': '5.031', 'learning_rate': '8.006e-08', 'epoch': '0.9459'} + 63%|██████▎ | 450/714 [58:28<34:04, 7.74s/it] 63%|██████▎ | 451/714 [58:35<33:43, 7.69s/it] 63%|██████▎ | 452/714 [58:42<32:52, 7.53s/it] 63%|██████▎ | 453/714 [58:52<35:03, 8.06s/it] 64%|██████▎ | 454/714 [59:00<35:00, 8.08s/it] 64%|██████▎ | 455/714 [59:08<34:58, 8.10s/it] 64%|██████▍ | 456/714 [59:16<34:20, 7.99s/it] 64%|██████▍ | 457/714 [59:23<33:52, 7.91s/it] 64%|██████▍ | 458/714 [59:31<33:44, 7.91s/it] 64%|██████▍ | 459/714 [59:40<34:30, 8.12s/it] 64%|██████▍ | 460/714 [59:48<34:01, 8.04s/it] {'loss': '1.773', 'grad_norm': '12.5', 'learning_rate': '7.482e-08', 'epoch': '0.9669'} + 64%|██████▍ | 460/714 [59:48<34:01, 8.04s/it] 65%|██████▍ | 461/714 [59:56<33:51, 8.03s/it] 65%|██████▍ | 462/714 [1:00:04<33:58, 8.09s/it] 65%|██████▍ | 463/714 [1:00:11<32:57, 7.88s/it] 65%|██████▍ | 464/714 [1:00:19<32:45, 7.86s/it] 65%|██████▌ | 465/714 [1:00:25<30:45, 7.41s/it] 65%|██████▌ | 466/714 [1:00:33<31:09, 7.54s/it] 65%|██████▌ | 467/714 [1:00:40<30:36, 7.44s/it] 66%|██████▌ | 468/714 [1:00:48<30:17, 7.39s/it] 66%|██████▌ | 469/714 [1:00:56<31:07, 7.62s/it] 66%|██████▌ | 470/714 [1:01:03<30:36, 7.53s/it] {'loss': '2.027', 'grad_norm': '6.312', 'learning_rate': '6.967e-08', 'epoch': '0.9879'} + 66%|██████▌ | 470/714 [1:01:03<30:36, 7.53s/it] 66%|██████▌ | 471/714 [1:01:12<32:00, 7.90s/it] 66%|██████▌ | 472/714 [1:01:19<30:45, 7.63s/it] 66%|██████▌ | 473/714 [1:01:27<31:38, 7.88s/it] 66%|██████▋ | 474/714 [1:01:34<29:58, 7.49s/it] 67%|██████▋ | 475/714 [1:01:42<30:33, 7.67s/it] 67%|██████▋ | 476/714 [1:01:48<28:25, 7.17s/it] 67%|██████▋ | 477/714 [1:01:56<29:02, 7.35s/it] 67%|██████▋ | 478/714 [1:02:03<28:41, 7.29s/it] 67%|██████▋ | 479/714 [1:02:11<29:46, 7.60s/it] 67%|██████▋ | 480/714 [1:02:19<29:52, 7.66s/it] {'loss': '1.848', 'grad_norm': '7.25', 'learning_rate': '6.464e-08', 'epoch': '1.008'} + 67%|██████▋ | 480/714 [1:02:19<29:52, 7.66s/it] 67%|██████▋ | 481/714 [1:02:27<29:27, 7.59s/it] 68%|██████▊ | 482/714 [1:02:34<29:19, 7.58s/it] 68%|██████▊ | 483/714 [1:02:42<29:42, 7.72s/it] 68%|██████▊ | 484/714 [1:02:50<30:04, 7.85s/it] 68%|██████▊ | 485/714 [1:02:58<29:28, 7.72s/it] 68%|██████▊ | 486/714 [1:03:06<29:51, 7.86s/it] 68%|██████▊ | 487/714 [1:03:13<29:05, 7.69s/it] 68%|██████▊ | 488/714 [1:03:21<28:35, 7.59s/it] 68%|██████▊ | 489/714 [1:03:28<27:52, 7.43s/it] 69%|██████▊ | 490/714 [1:03:36<28:28, 7.63s/it] {'loss': '1.788', 'grad_norm': '6.469', 'learning_rate': '5.974e-08', 'epoch': '1.029'} + 69%|██████▊ | 490/714 [1:03:36<28:28, 7.63s/it] 69%|██████▉ | 491/714 [1:03:43<28:01, 7.54s/it] 69%|██████▉ | 492/714 [1:03:51<28:19, 7.66s/it] 69%|██████▉ | 493/714 [1:03:59<28:04, 7.62s/it] 69%|██████▉ | 494/714 [1:04:06<27:29, 7.50s/it] 69%|██████▉ | 495/714 [1:04:14<28:01, 7.68s/it] 69%|██████▉ | 496/714 [1:04:20<26:43, 7.36s/it] 70%|██████▉ | 497/714 [1:04:29<27:50, 7.70s/it] 70%|██████▉ | 498/714 [1:04:36<26:58, 7.49s/it] 70%|██████▉ | 499/714 [1:04:43<26:45, 7.47s/it] 70%|███████ | 500/714 [1:04:51<26:25, 7.41s/it] {'loss': '2.006', 'grad_norm': '10.06', 'learning_rate': '5.497e-08', 'epoch': '1.05'} + 70%|███████ | 500/714 [1:04:51<26:25, 7.41s/it] 70%|███████ | 501/714 [1:04:58<26:10, 7.37s/it] 70%|███████ | 502/714 [1:05:06<26:56, 7.62s/it] 70%|███████ | 503/714 [1:05:14<26:47, 7.62s/it] 71%|███████ | 504/714 [1:05:20<25:42, 7.35s/it] 71%|███████ | 505/714 [1:05:29<26:34, 7.63s/it] 71%|███████ | 506/714 [1:05:37<26:48, 7.73s/it] 71%|███████ | 507/714 [1:05:45<26:44, 7.75s/it] 71%|███████ | 508/714 [1:05:53<26:54, 7.84s/it] 71%|███████▏ | 509/714 [1:06:00<26:20, 7.71s/it] 71%|███████▏ | 510/714 [1:06:07<25:49, 7.59s/it] {'loss': '1.916', 'grad_norm': '19.12', 'learning_rate': '5.034e-08', 'epoch': '1.071'} + 71%|███████▏ | 510/714 [1:06:07<25:49, 7.59s/it] 72%|███████▏ | 511/714 [1:06:14<25:03, 7.41s/it] 72%|███████▏ | 512/714 [1:06:22<24:57, 7.42s/it] 72%|███████▏ | 513/714 [1:06:30<25:23, 7.58s/it] 72%|███████▏ | 514/714 [1:06:38<25:36, 7.68s/it] 72%|███████▏ | 515/714 [1:06:45<25:05, 7.57s/it] 72%|███████▏ | 516/714 [1:06:53<25:36, 7.76s/it] 72%|███████▏ | 517/714 [1:07:02<26:06, 7.95s/it] 73%|███████▎ | 518/714 [1:07:09<25:04, 7.67s/it] 73%|███████▎ | 519/714 [1:07:16<24:39, 7.59s/it] 73%|███████▎ | 520/714 [1:07:23<24:28, 7.57s/it] {'loss': '1.783', 'grad_norm': '5.344', 'learning_rate': '4.587e-08', 'epoch': '1.092'} + 73%|███████▎ | 520/714 [1:07:23<24:28, 7.57s/it] 73%|███████▎ | 521/714 [1:07:31<24:25, 7.59s/it] 73%|███████▎ | 522/714 [1:07:38<24:05, 7.53s/it] 73%|███████▎ | 523/714 [1:07:46<24:10, 7.59s/it] 73%|███████▎ | 524/714 [1:07:55<24:46, 7.83s/it] 74%|███████▎ | 525/714 [1:08:02<24:37, 7.82s/it] 74%|███████▎ | 526/714 [1:08:11<25:22, 8.10s/it] 74%|███████▍ | 527/714 [1:08:19<25:03, 8.04s/it] 74%|███████▍ | 528/714 [1:08:26<23:42, 7.65s/it] 74%|███████▍ | 529/714 [1:08:34<23:56, 7.76s/it] 74%|███████▍ | 530/714 [1:08:41<23:28, 7.66s/it] {'loss': '1.814', 'grad_norm': '5.75', 'learning_rate': '4.156e-08', 'epoch': '1.114'} + 74%|███████▍ | 530/714 [1:08:41<23:28, 7.66s/it] 74%|███████▍ | 531/714 [1:08:49<23:37, 7.74s/it] 75%|███████▍ | 532/714 [1:08:57<23:45, 7.83s/it] 75%|███████▍ | 533/714 [1:09:05<23:59, 7.95s/it] 75%|███████▍ | 534/714 [1:09:12<22:21, 7.45s/it] 75%|███████▍ | 535/714 [1:09:20<22:45, 7.63s/it] 75%|███████▌ | 536/714 [1:09:28<22:43, 7.66s/it] 75%|███████▌ | 537/714 [1:09:36<23:10, 7.86s/it] 75%|███████▌ | 538/714 [1:09:44<23:12, 7.91s/it] 75%|███████▌ | 539/714 [1:09:51<22:12, 7.62s/it] 76%|███████▌ | 540/714 [1:09:59<22:18, 7.70s/it] {'loss': '1.869', 'grad_norm': '5.844', 'learning_rate': '3.742e-08', 'epoch': '1.135'} + 76%|███████▌ | 540/714 [1:09:59<22:18, 7.70s/it] 76%|███████▌ | 541/714 [1:10:06<22:11, 7.70s/it] 76%|███████▌ | 542/714 [1:10:14<22:25, 7.82s/it] 76%|███████▌ | 543/714 [1:10:21<20:58, 7.36s/it] 76%|███████▌ | 544/714 [1:10:29<21:12, 7.48s/it] 76%|███████▋ | 545/714 [1:10:36<20:56, 7.43s/it] 76%|███████▋ | 546/714 [1:10:43<20:47, 7.43s/it] 77%|███████▋ | 547/714 [1:10:51<21:04, 7.57s/it] 77%|███████▋ | 548/714 [1:10:58<20:37, 7.45s/it] 77%|███████▋ | 549/714 [1:11:06<20:56, 7.62s/it] 77%|███████▋ | 550/714 [1:11:14<21:03, 7.71s/it] {'loss': '1.879', 'grad_norm': '6.469', 'learning_rate': '3.346e-08', 'epoch': '1.156'} + 77%|███████▋ | 550/714 [1:11:14<21:03, 7.71s/it] 77%|███████▋ | 551/714 [1:11:22<21:03, 7.75s/it] 77%|███████▋ | 552/714 [1:11:30<21:01, 7.78s/it] 77%|███████▋ | 553/714 [1:11:37<20:05, 7.49s/it] 78%|███████▊ | 554/714 [1:11:44<20:09, 7.56s/it] 78%|███████▊ | 555/714 [1:11:53<20:25, 7.71s/it] 78%|███████▊ | 556/714 [1:12:00<20:13, 7.68s/it] 78%|███████▊ | 557/714 [1:12:08<20:00, 7.65s/it] 78%|███████▊ | 558/714 [1:12:15<19:21, 7.45s/it] 78%|███████▊ | 559/714 [1:12:23<20:04, 7.77s/it] 78%|███████▊ | 560/714 [1:12:31<20:00, 7.79s/it] {'loss': '1.855', 'grad_norm': '10.06', 'learning_rate': '2.969e-08', 'epoch': '1.177'} + 78%|███████▊ | 560/714 [1:12:31<20:00, 7.79s/it] 79%|███████▊ | 561/714 [1:12:39<20:15, 7.95s/it] 79%|███████▊ | 562/714 [1:12:47<19:46, 7.81s/it] 79%|███████▉ | 563/714 [1:12:55<20:00, 7.95s/it] 79%|███████▉ | 564/714 [1:13:03<19:47, 7.91s/it] 79%|███████▉ | 565/714 [1:13:10<19:14, 7.75s/it] 79%|███████▉ | 566/714 [1:13:17<18:09, 7.36s/it] 79%|███████▉ | 567/714 [1:13:24<18:15, 7.45s/it] 80%|███████▉ | 568/714 [1:13:33<18:36, 7.65s/it] 80%|███████▉ | 569/714 [1:13:42<19:42, 8.15s/it] 80%|███████▉ | 570/714 [1:13:50<19:39, 8.19s/it] {'loss': '1.842', 'grad_norm': '13.31', 'learning_rate': '2.612e-08', 'epoch': '1.198'} + 80%|███████▉ | 570/714 [1:13:50<19:39, 8.19s/it] 80%|███████▉ | 571/714 [1:13:58<19:05, 8.01s/it] 80%|████████ | 572/714 [1:14:06<19:08, 8.09s/it] 80%|████████ | 573/714 [1:14:14<19:07, 8.14s/it] 80%|████████ | 574/714 [1:14:22<18:42, 8.01s/it] 81%|████████ | 575/714 [1:14:30<18:16, 7.89s/it] 81%|████████ | 576/714 [1:14:37<17:41, 7.69s/it] 81%|████████ | 577/714 [1:14:44<17:09, 7.52s/it] 81%|████████ | 578/714 [1:14:52<17:18, 7.64s/it] 81%|████████ | 579/714 [1:15:01<17:52, 7.95s/it] 81%|████████ | 580/714 [1:15:09<17:54, 8.02s/it] {'loss': '1.932', 'grad_norm': '7.031', 'learning_rate': '2.275e-08', 'epoch': '1.219'} + 81%|████████ | 580/714 [1:15:09<17:54, 8.02s/it] 81%|████████▏ | 581/714 [1:15:17<17:47, 8.03s/it] 82%|████████▏ | 582/714 [1:15:26<18:17, 8.31s/it] 82%|████████▏ | 583/714 [1:15:34<18:00, 8.25s/it] 82%|████████▏ | 584/714 [1:15:43<18:14, 8.42s/it] 82%|████████▏ | 585/714 [1:15:50<17:29, 8.13s/it] 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[1:28:12<04:24, 7.78s/it] {'loss': '1.943', 'grad_norm': '6.938', 'learning_rate': '1.575e-09', 'epoch': '1.429'} + 95%|█████████▌| 680/714 [1:28:12<04:24, 7.78s/it] 95%|█████████▌| 681/714 [1:28:19<04:12, 7.66s/it] 96%|█████████▌| 682/714 [1:28:27<04:11, 7.86s/it] 96%|█████████▌| 683/714 [1:28:34<03:54, 7.56s/it] 96%|█████████▌| 684/714 [1:28:42<03:51, 7.71s/it] 96%|█████████▌| 685/714 [1:28:51<03:50, 7.94s/it] 96%|█████████▌| 686/714 [1:28:58<03:38, 7.82s/it] 96%|█████████▌| 687/714 [1:29:07<03:35, 7.98s/it] 96%|█████████▋| 688/714 [1:29:14<03:25, 7.92s/it] 96%|█████████▋| 689/714 [1:29:24<03:28, 8.33s/it] 97%|█████████▋| 690/714 [1:29:31<03:13, 8.07s/it] {'loss': '1.823', 'grad_norm': '7.094', 'learning_rate': '8.042e-10', 'epoch': '1.45'} + 97%|█████████▋| 690/714 [1:29:31<03:13, 8.07s/it] 97%|█████████▋| 691/714 [1:29:39<03:06, 8.12s/it] 97%|█████████▋| 692/714 [1:29:47<02:54, 7.91s/it] 97%|█████████▋| 693/714 [1:29:54<02:44, 7.83s/it] 97%|█████████▋| 694/714 [1:30:00<02:24, 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'learning_rate': '3.22e-11', 'epoch': '1.492'} + 99%|█████████▉| 710/714 [1:32:02<00:31, 7.77s/it] 100%|█████████▉| 711/714 [1:32:10<00:23, 7.87s/it] 100%|█████████▉| 712/714 [1:32:18<00:15, 7.86s/it] 100%|█████████▉| 713/714 [1:32:26<00:07, 7.75s/it] 100%|██████████| 714/714 [1:32:33<00:00, 7.57s/it][INFO|trainer.py:1823] 2026-07-15 08:06:16,751 >> + +Training completed. Do not forget to share your model on huggingface.co/models =) + + + {'train_runtime': '5553', 'train_samples_per_second': '0.514', 'train_steps_per_second': '0.129', 'train_loss': '1.907', 'epoch': '1.5'} + 100%|██████████| 714/714 [1:32:33<00:00, 7.57s/it] 100%|██████████| 714/714 [1:32:33<00:00, 7.78s/it] +[INFO|trainer.py:3815] 2026-07-15 08:06:16,752 >> Saving model checkpoint to /home/ll/llm4rec/experiments/outputs/v43_v29_paper_mix_low_lr25e7_ep15 +[INFO|configuration_utils.py:533] 2026-07-15 08:06:16,754 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v43_v29_paper_mix_low_lr25e7_ep15/config.json +[INFO|configuration_utils.py:816] 2026-07-15 08:06:16,755 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v43_v29_paper_mix_low_lr25e7_ep15/generation_config.json + Writing model shards: 0%| | 0/1 [00:00> Model weights saved in /home/ll/llm4rec/experiments/outputs/v43_v29_paper_mix_low_lr25e7_ep15/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-15 08:06:17,748 >> chat template saved in /home/ll/llm4rec/experiments/outputs/v43_v29_paper_mix_low_lr25e7_ep15/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-15 08:06:17,770 >> tokenizer config file saved in /home/ll/llm4rec/experiments/outputs/v43_v29_paper_mix_low_lr25e7_ep15/tokenizer_config.json +***** train metrics ***** + epoch = 1.5003 + total_flos = 318009432GF + train_loss = 1.9072 + train_runtime = 1:32:33.44 + train_samples_per_second = 0.514 + train_steps_per_second = 0.129 +Figure saved at: /home/ll/llm4rec/experiments/outputs/v43_v29_paper_mix_low_lr25e7_ep15/training_loss.png +[WARNING|2026-07-15 08:06:17] llamafactory.extras.ploting:149 >> No metric eval_loss to plot. +[WARNING|2026-07-15 08:06:17] llamafactory.extras.ploting:149 >> No metric eval_accuracy to plot. +[INFO|modelcard.py:263] 2026-07-15 08:06:17,902 >> 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..aa6dc3e --- /dev/null +++ b/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 1.5002627430373097, + "total_flos": 3.414600284463329e+17, + "train_loss": 1.907181319736299, + "train_runtime": 5553.447, + "train_samples_per_second": 0.514, + "train_steps_per_second": 0.129 +} \ No newline at end of file diff --git a/trainer_log.jsonl b/trainer_log.jsonl new file mode 100644 index 0000000..2f49b31 --- /dev/null +++ b/trainer_log.jsonl @@ -0,0 +1,72 @@ +{"current_steps": 10, "total_steps": 714, "loss": 1.8086944580078126, "lr": 1.0227272727272728e-07, "epoch": 0.021019442984760904, "percentage": 1.4, "elapsed_time": "0:01:18", "remaining_time": "1:32:11"} +{"current_steps": 20, "total_steps": 714, "loss": 1.9563072204589844, "lr": 2.159090909090909e-07, "epoch": 0.04203888596952181, "percentage": 2.8, "elapsed_time": "0:02:38", "remaining_time": "1:31:32"} +{"current_steps": 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