From 0e1452784bbb324984b293bd1f39cb9d5d74f3dd Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Fri, 31 Jul 2026 16:30:04 +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/v31_v29_live_specialist_r3_lr12e6_ep035 Source: Original Platform --- .gitattributes | 36 + README.md | 58 + all_results.json | 8 + chat_template.jinja | 89 + config.json | 63 + experiment_recipe.json | 68 + generation_config.json | 12 + model.safetensors | 3 + ...84520.ubuntu-System-Product-Name.1295876.0 | 3 + tokenizer.json | 3 + tokenizer_config.json | 16 + train.log | 2062 +++++++++++++++++ train_results.json | 8 + trainer_log.jsonl | 13 + trainer_state.json | 127 + training_args.bin | 3 + training_loss.png | Bin 0 -> 45651 bytes 17 files changed, 2572 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/Jul13_03-28-40_ubuntu-System-Product-Name/events.out.tfevents.1783884520.ubuntu-System-Product-Name.1295876.0 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..1d23061 --- /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: v31_v29_live_specialist_r3_lr12e6_ep035 + results: [] +--- + + + +# v31_v29_live_specialist_r3_lr12e6_ep035 + +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_v29_live_specialist_r3 dataset. + +## Model description + +More information needed + +## Intended uses & limitations + +More information needed + +## Training and evaluation data + +More information needed + +## Training procedure + +### Training hyperparameters + +The following hyperparameters were used during training: +- learning_rate: 1.2e-06 +- train_batch_size: 1 +- eval_batch_size: 8 +- seed: 202607311 +- 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: 0.02 +- num_epochs: 0.35 + +### 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..9f02a01 --- /dev/null +++ b/all_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 0.3562043795620438, + "total_flos": 2.935955062939853e+16, + "train_loss": 1.5556521063945332, + "train_runtime": 514.4769, + "train_samples_per_second": 0.466, + "train_steps_per_second": 0.119 +} \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..01be9b3 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,89 @@ +{%- if tools %} + {{- '<|im_start|>system\n' }} + {%- if messages[0].role == 'system' %} + {{- messages[0].content + '\n\n' }} + {%- endif %} + {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n" }} + {%- for tool in tools %} + {{- "\n" }} + {{- tool | tojson }} + {%- endfor %} + {{- "\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n<|im_end|>\n" }} +{%- else %} + {%- if messages[0].role == 'system' %} + {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }} + {%- endif %} +{%- endif %} +{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %} +{%- for message in messages[::-1] %} + {%- set index = (messages|length - 1) - loop.index0 %} + {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %} + {%- set ns.multi_step_tool = false %} + {%- set ns.last_query_index = index %} + {%- endif %} +{%- endfor %} +{%- for message in messages %} + {%- if message.content is string %} + {%- set content = message.content %} + {%- else %} + {%- set content = '' %} + {%- endif %} + {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} + {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }} + {%- elif message.role == "assistant" %} + {%- set reasoning_content = '' %} + {%- if message.reasoning_content is string %} + {%- set reasoning_content = message.reasoning_content %} + {%- else %} + {%- if '' in content %} + {%- set reasoning_content = content.split('')[0].rstrip('\n').split('')[-1].lstrip('\n') %} + {%- set content = content.split('')[-1].lstrip('\n') %} + {%- endif %} + {%- endif %} + {%- if loop.index0 > ns.last_query_index %} + {%- if loop.last or (not loop.last and reasoning_content) %} + {{- '<|im_start|>' + message.role + '\n\n' + reasoning_content.strip('\n') + '\n\n\n' + content.lstrip('\n') }} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + content }} + {%- endif %} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + content }} + {%- endif %} + {%- if message.tool_calls %} + {%- for tool_call in message.tool_calls %} + {%- if (loop.first and content) or (not loop.first) %} + {{- '\n' }} + {%- endif %} + {%- if tool_call.function %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n{"name": "' }} + {{- tool_call.name }} + {{- '", "arguments": ' }} + {%- if tool_call.arguments is string %} + {{- tool_call.arguments }} + {%- else %} + {{- tool_call.arguments | tojson }} + {%- endif %} + {{- '}\n' }} + {%- endfor %} + {%- endif %} + {{- '<|im_end|>\n' }} + {%- elif message.role == "tool" %} + {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|im_start|>user' }} + {%- endif %} + {{- '\n\n' }} + {{- content }} + {{- '\n' }} + {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} + {{- '<|im_end|>\n' }} + {%- endif %} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {{- '<|im_start|>assistant\n' }} + {%- if enable_thinking is defined and enable_thinking is false %} + {{- '\n\n\n\n' }} + {%- endif %} +{%- endif %} \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..c4eb20b --- /dev/null +++ b/config.json @@ -0,0 +1,63 @@ +{ + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": null, + "dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 40960, + "max_window_layers": 28, + "model_type": "qwen3", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "pad_token_id": 151643, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 1000000, + "rope_type": "default" + }, + "sliding_window": null, + "tie_word_embeddings": false, + "transformers_version": "5.6.0", + "use_cache": false, + "use_sliding_window": false, + "vocab_size": 176253 +} diff --git a/experiment_recipe.json b/experiment_recipe.json new file mode 100644 index 0000000..6a76278 --- /dev/null +++ b/experiment_recipe.json @@ -0,0 +1,68 @@ +{ + "created_at": "2026-07-13 03:28:12", + "run": { + "name": "v31_v29_live_specialist_r3_lr12e6_ep035", + "dataset": "v29_live_specialist_r3", + "model_path": "/home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018", + "lr": "1.2e-6", + "epochs": 0.35, + "warmup": 0.02, + "scheduler": "cosine", + "seed": 202607311, + "note": "v29 continuation and live-domain specialist. Use diverse evidence-grounded three-level CoT plus strict no-think direct answers for every live-target record, with item/user/general-rec guards. Goal: raise recommendation-live without losing v29's speed or other dimensions.", + "config": "/home/ll/llm4rec/experiments/configs/v31_v29_live_specialist_r3_lr12e6_ep035.yaml" + }, + "dataset_manifest": { + "name": "v29_live_specialist_r3", + "path": "/home/ll/llm4rec/experiments/data/v29_live_specialist_r3.jsonl", + "records": 13013, + "groups": { + "rec": 7413, + "user": 2400, + "item": 3200 + }, + "variants": { + "raw": 1271, + "user_strict_array": 1080, + "user_extra_no_think_logic": 424, + "rec_no_think_direct_final": 3071, + "item_no_think_direct_final": 1659, + "rec_r3_draft_evidence": 3071, + "item_short_think": 1541, + "user_strict_logic": 896 + }, + "sha256": "5710cee8621dde0b19e5418cfe81cc5aea2933d432478968c498ff0f00e24934", + "seed": 202607111 + }, + "dataset_recipe": "v29 live-domain specialist: all live-target raw CoT, individualized three-level R3 draft CoT, and no-think direct answers, plus balanced general-rec, item, and user guards.", + "cot_policy": "Preserve /think reasoning supervision and do not use v7_final_only as a CoT training base. For /no_think prompts, train pure final answers without generated tags. This is route-specific behavior, not global CoT removal.", + "raw_counts": { + "rec": 19204, + "item": 10384, + "user": 2892 + }, + "eval_observations": { + "v07": "best local score so far: total=0.8978, eval_time≈47.3min; fast final outputs likely help.", + "v15": "best CoT-preserving score so far: total=0.8778, eval_time≈70.1min; logs show repeated tokens, JSON shell errors, prompt leakage, and verbose /no_think outputs.", + "v19": "best CoT-native continuation so far: total=0.8855, eval_time≈48.1min; user1 and rec4 improved but world dropped.", + "v20": "v7 final-only continuation with CoT restore failed as a CoT route: total=0.8527, item fell to 0.1840; do not use v7 as future CoT base.", + "v22": "scratch official-base 3 epoch clean CoT underperformed: total=0.8217; item/world preserved but user and rec2 are weak.", + "v23": "scratch official-base 5 epoch low-LR guard failed badly: total=0.6990; item/user collapse suggests long scratch SFT is not viable with current data mix.", + "v24": "v15 light repair is best among v22-v24 but still only total=0.8364; user2 improves but rec/world do not recover.", + "v25": "v19 product-heavy repair did not beat v19: total=0.8793. Logs show heavy repeated product tokens and repeated short-think phrases; avoid this over-sampling pattern.", + "v26": "best latest batch and fastest eval: total=0.8804, eval_time≈45.7min, best user1/rec1. It is useful as a base, but rec2/world dropped.", + "v27": "v12 product/ad repair kept user2/world relatively better but was slow and template-heavy: total=0.8563, eval_time≈70.6min. Do not continue this exact direction.", + "v28": "v26 no-template repair regressed to total=0.8748 and slowed to 51.7min; broad continuation from v26 did not recover world/rec balance.", + "v29": "new best CoT-native model: total=0.9038, eval_time≈45.3min. Strong item/user1/ad/product/world, with live recommendation (0.1054) the clearest remaining gap. Logs still show occasional no-think tag leakage and a repeated generic live-reasoning sentence.", + "v30": "three-source soup reached total=0.8846, below v29. It retained speed/world but diluted recommendation scores; do not repeat broad checkpoint averaging.", + "v16": "CoT pattern rewrite failed: total=0.7912; logs show malformed user JSON and fragmented recommendation reasoning.", + "v18": "low-LR mixed replay from v12 failed: total=0.8340; item/world dropped and rec outputs mixed text/itemic/think tags." + }, + "script": "/home/ll/llm4rec/experiments/run_experiments.py", + "script_sha256": "a5a06949ec583ca8f6e5c6de8690d3005ec1ef801b508b7286f1162f5b0c87b4", + "deadline": "none", + "reproduce": { + "prepare_command": "EXPERIMENT_PREPARE_ONLY=1 python3 experiments/run_experiments.py", + "train_command": "CUDA_VISIBLE_DEVICES= bash -lc 'source /home/ll/llm4rec/demo/LLaMA-Factory/.venv/bin/activate && llamafactory-cli train /home/ll/llm4rec/experiments/configs/v31_v29_live_specialist_r3_lr12e6_ep035.yaml'" + } +} \ 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..4802518 --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0b772648f015a574a409246cadbaddfa1dd98de62da7b25434ea5490dae78fcc +size 1602902832 diff --git a/runs/Jul13_03-28-40_ubuntu-System-Product-Name/events.out.tfevents.1783884520.ubuntu-System-Product-Name.1295876.0 b/runs/Jul13_03-28-40_ubuntu-System-Product-Name/events.out.tfevents.1783884520.ubuntu-System-Product-Name.1295876.0 new file mode 100644 index 0000000..36dd946 --- /dev/null +++ b/runs/Jul13_03-28-40_ubuntu-System-Product-Name/events.out.tfevents.1783884520.ubuntu-System-Product-Name.1295876.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6a56bcaa1a481ae033b41f025ac2d393d0735411bb84be980208b7873380d0c0 +size 8796 diff --git a/tokenizer.json b/tokenizer.json new file mode 100644 index 0000000..8aee134 --- /dev/null +++ b/tokenizer.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cd4d15f596979aecbc11ba12668cb21c9fb8452ce1235cd9d7878cc950170421 +size 16016554 diff --git a/tokenizer_config.json b/tokenizer_config.json new file mode 100644 index 0000000..35d445f --- /dev/null +++ b/tokenizer_config.json @@ -0,0 +1,16 @@ +{ + "add_prefix_space": false, + "backend": "tokenizers", + "bos_token": null, + "clean_up_tokenization_spaces": false, + "eos_token": "<|im_end|>", + "errors": "replace", + "is_local": true, + "local_files_only": false, + "model_max_length": 131072, + "pad_token": "<|endoftext|>", + "padding_side": "right", + "split_special_tokens": false, + "tokenizer_class": "Qwen2Tokenizer", + "unk_token": null +} diff --git a/train.log b/train.log new file mode 100644 index 0000000..37c9fee --- /dev/null +++ b/train.log @@ -0,0 +1,2062 @@ +/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|2026-07-13 03:28:15] 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] 2026-07-13 03:28:15,701 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-13 03:28:15,703 >> 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] 2026-07-13 03:28:16,424 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-13 03:28:16,424 >> 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] 2026-07-13 03:28:16,424 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-13 03:28:16,425 >> 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" + }, + 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01:23 [视频-收藏/长播] <|video_begin|> + 02:36 [视频-点赞/长播] <|video_begin|> + 02:42 [广告-点击] <|ad_begin|> + 02:46 [视频-点赞/长播] <|video_begin|> + 02:48 [视频-点赞/长播] <|video_begin|> + 02:54 [视频-评论/点赞/长播] <|video_begin|> + 02:54 [视频-点赞/长播] <|video_begin|> + 03:04 [视频-收藏/长播] <|video_begin|> + 04:19 [视频-点赞/长播] <|video_begin|> + 06:52 [视频-点赞/长播] <|video_begin|> + 08:32 [视频-点赞/长播] <|video_begin|> + 16:27 [视频-评论/长播] <|video_begin|> + 16:27 [视频-评论/长播] <|video_begin|> + 16:37 [视频-点赞/长播] <|video_begin|> + 17:30 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-02-03】 + 03:47 [视频-点赞/长播] <|video_begin|> + 03:49 [广告-点击] <|ad_begin|> + 04:25 [视频-点赞/长播] <|video_begin|> + 04:25 [视频-关注/长播] <|video_begin|> + 05:02 [视频-评论/长播] <|video_begin|> + 05:05 [视频-评论/点赞/长播] <|video_begin|> + 05:43 [视频-点赞/长播] <|video_begin|> + 05:59 [视频-点赞/长播] <|video_begin|> + 06:00 [视频-收藏/长播] <|video_begin|> + 07:47 [视频-点赞/长播] <|video_begin|> + 07:47 [视频-点赞/长播] <|video_begin|> + 07:58 [广告-点击] <|ad_begin|> + 08:09 [广告-深度转化] <|ad_begin|> + 08:09 [广告-点击] <|ad_begin|> + 08:20 [视频-点赞/长播] <|video_begin|> + 09:04 [视频-评论/长播] <|video_begin|> + 09:11 [视频-点赞/长播] <|video_begin|> + 09:35 [视频-评论/长播] <|video_begin|> + 09:37 [视频-点赞/长播] <|video_begin|> + 09:50 [视频-点赞/长播] <|video_begin|> + 10:29 [视频-点赞/长播] <|video_begin|> + 10:31 [视频-点赞/长播] <|video_begin|> + 13:12 [视频-点赞/长播] <|video_begin|> + 15:03 [视频-评论/长播] <|video_begin|> + 15:07 [视频-收藏/长播] <|video_begin|> + 15:28 [视频-收藏/长播] <|video_begin|> + 15:39 [视频-点赞/长播] <|video_begin|> + 16:01 [视频-评论/长播] <|video_begin|> + 16:53 [视频-收藏/长播] <|video_begin|> + 16:55 [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|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 18:27 [视频-点赞/长播] <|video_begin|> + 18:35 [广告-点击] <|ad_begin|> + 18:37 [视频-点赞/长播] <|video_begin|> + 18:38 [视频-评论/长播] <|video_begin|> + 18:44 [视频-点赞/长播] <|video_begin|> + 18:47 [视频-评论/点赞/长播] <|video_begin|> +【2026-02-04】 + 03:22 [视频-点赞/长播] <|video_begin|> + 03:22 [视频-评论/长播] <|video_begin|> + 03:48 [视频-点赞/长播] <|video_begin|> + 04:03 [视频-点赞/长播] <|video_begin|> + 04:51 [视频-点赞/长播] <|video_begin|> + 05:20 [视频-评论/长播] <|video_begin|> + 06:00 [视频-收藏/长播] <|video_begin|> + 06:06 [视频-点赞/长播] <|video_begin|> + 07:14 [广告-点击] <|ad_begin|> + 09:17 [广告-点击] <|ad_begin|> + 09:38 [视频-点赞/长播] <|video_begin|> + 11:50 [视频-点赞/长播] <|video_begin|> + 12:27 [广告-深度转化] <|ad_begin|> + 12:27 [广告-点击] <|ad_begin|> + 17:30 [视频-点赞/长播] <|video_begin|> + 17:30 [视频-收藏/长播] <|video_begin|> + 17:52 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 18:26 [视频-点赞/长播] <|video_begin|> + 18:29 [视频-评论/长播] <|video_begin|> +【2026-02-05】 + 00:12 [视频-点赞/长播] <|video_begin|> + 00:14 [视频-点赞/长播] <|video_begin|> + 00:49 [视频-点赞/长播] <|video_begin|> + 09:16 [视频-点赞/长播] <|video_begin|> + 09:32 [视频-点赞/长播] <|video_begin|> + 09:32 [视频-评论/长播] <|video_begin|> + 09:48 [视频-评论/长播] <|video_begin|> + 09:54 [视频-点赞/长播] <|video_begin|> + 10:05 [视频-点赞/长播] <|video_begin|> + 10:10 [视频-点赞/长播] <|video_begin|> + 10:15 [视频-点赞/长播] <|video_begin|> + 10:15 [视频-点赞/长播] <|video_begin|> + 10:20 [视频-评论/长播] <|video_begin|> + 10:34 [视频-点赞/长播] <|video_begin|> + 10:36 [视频-点赞/长播] <|video_begin|> + 10:43 [视频-点赞/长播] <|video_begin|> + 10:43 [视频-评论/长播] <|video_begin|> + 10:47 [视频-点赞/长播] <|video_begin|> + 10:53 [视频-评论/长播] <|video_begin|> + 11:44 [视频-评论/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|> + 13:42 [视频-长播] <|video_begin|> + 13:42 [视频-长播] <|video_begin|> + 13:42 [视频-长播] <|video_begin|> + 13:42 [视频-长播] <|video_begin|> + 13:49 [视频-长播] <|video_begin|> + 13:49 [视频-长播] <|video_begin|> + 13:55 [视频-转发/评论/点赞/长播] <|video_begin|> + 13:55 [视频-长播] <|video_begin|> + 13:55 [视频-长播] <|video_begin|> + 13:55 [视频-长播] <|video_begin|> + 13:57 [视频-长播] <|video_begin|> + 13:57 [视频-长播] <|video_begin|> + 13:58 [视频-长播] <|video_begin|> + 13:58 [视频-长播] <|video_begin|> + 13:59 [视频-长播] <|video_begin|> + 13:59 [视频-长播] <|video_begin|> + 14:02 [视频-长播] <|video_begin|> + 14:04 [视频-长播] <|video_begin|> + 14:11 [视频-长播] <|video_begin|> + 14:11 [视频-长播] <|video_begin|> + 14:11 [视频-长播] <|video_begin|> + 14:11 [视频-长播] <|video_begin|> + 14:15 [视频-长播] <|video_begin|> + 14:15 [视频-长播] <|video_begin|> + 14:15 [视频-长播] <|video_begin|> + 14:17 [视频-长播] <|video_begin|> + 14:17 [视频-长播] <|video_begin|> + 14:28 [视频-长播] <|video_begin|> + 14:28 [视频-长播] <|video_begin|> + 14:30 [视频-长播] <|video_begin|> + 14:30 [视频-长播] <|video_begin|> + 14:31 [视频-长播] <|video_begin|> + 14:32 [视频-长播] <|video_begin|> + 14:32 [视频-长播] <|video_begin|> + 14:33 [视频-长播] <|video_begin|> + 14:39 [视频-评论/长播] <|video_begin|> + 14:50 [视频-长播] <|video_begin|> + 14:50 [视频-长播] <|video_begin|> + 14:50 [视频-长播] <|video_begin|> + 15:00 [视频-长播] <|video_begin|> + 15:02 [视频-长播] <|video_begin|> + 15:02 [视频-长播] <|video_begin|> + 15:02 [视频-长播] <|video_begin|> + 15:14 [视频-长播] <|video_begin|> + 15:14 [视频-长播] <|video_begin|> + 15:19 [视频-长播] <|video_begin|> + 15:19 [视频-长播] <|video_begin|> + 15:22 [视频-长播] <|video_begin|> + 15:26 [视频-长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +角色任务：你作为极端严苛的用户行为数据挖掘专家和数据格式化专家，需要根据以上用户交互历史，围绕给定主题抽取存在行为递进逻辑与内在关联的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 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 +核心提取逻辑（递进性准则）： +请依时间顺序提取 5 步以内的行为链，并确保后项交互B是前项交互A的场景需求协同补充、副作用诱因或兴趣细化升级，也就是 B 必须由 A 的延伸需求所驱动；严禁提取任何品类平级、内容重复或仅因天气/促销形成的并列凑单行为。 +1.场景驱动协同：交互 B 和交互 A 必须落在同一生活场景、同一主题类目中，且存在很强的需求补完关系。 +2.兴趣因果递进：交互 B 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。 +请针对下列主题提取行为逻辑链： +主题：从泛化游戏兴趣到特定游戏周边的消费收敛 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从泛化游戏兴趣到特定游戏周边的消费收敛", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +} + +强约束：最终答案必须是一个可被 json.loads 解析的 JSON，不能包含 Markdown、解释、重复键或 JSON 外额外字符。/no_think<|im_end|> +<|im_start|>assistant +{"logic_chain":{"name":"从泛化游戏兴趣到特定游戏周边的消费收敛","events":[{"date":"2025-08-25","action":"[直播-关注] <|living_begin|>","logic":"兴趣触发：首次明确关注以《无畏契约》为核心内容的游戏直播，表现出对该游戏的初步兴趣。"},{"date":"2025-10-19","action":"[商品-购买] <|prod_begin|>","logic":"兴趣物化：在持续关注游戏内容后，首次将虚拟兴趣转化为实体消费，购买了与游戏直接相关的周边商品“蝴蝶刀”，实现了从内容观赏到实物拥有的认知跃迁。"},{"date":"2026-01-25","action":"[商品-点击] <|prod_begin|>","logic":"兴趣深化与拓展：在购买了标志性武器模型后，用户的消费兴趣进一步拓展到更具日常实用性的游戏标志性周边（钥匙扣），显示出兴趣的持续和深化。"},{"date":"2026-02-03","action":"[商品-购买] <|prod_begin|>","logic":"闭环与身份认同：消费行为从实体周边回归到游戏内的虚拟服务（购买人气/点赞），表明用户不仅满足于拥有实体纪念品，还追求在游戏社区内的身份认同和虚拟价值提升，形成了“内容消费-实体消费-虚拟消费”的完整闭环。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2025-08-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-07】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-08-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-10】 + --:-- [商品-购买] <|prod_begin|> +【2025-08-12】 + --:-- [商品-购买] <|prod_begin|> +【2025-09-05】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-13】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-09】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-19】 + --:-- [商品-购买] <|prod_begin|> +【2025-10-25】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-01】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-04】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-28】 + --:-- [商品-购买] <|prod_begin|> +【2025-11-30】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-01】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-06】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-15】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-16】 + --:-- [商品-购买] <|prod_begin|> +【2025-12-20】 + 14:20 [广告-深度转化] <|ad_begin|> + 19:59 [广告-深度转化] <|ad_begin|> + 20:26 [广告-深度转化] <|ad_begin|> + 22:00 [广告-深度转化] <|ad_begin|> +【2025-12-21】 + 00:40 [广告-深度转化] <|ad_begin|> + 22:27 [广告-深度转化] <|ad_begin|> +【2025-12-22】 + 03:54 [广告-深度转化] <|ad_begin|> + 12:40 [广告-深度转化] <|ad_begin|> + 20:40 [广告-深度转化] <|ad_begin|> +【2025-12-23】 + 09:35 [广告-深度转化] <|ad_begin|> + 12:25 [广告-深度转化] <|ad_begin|> + 17:33 [广告-深度转化] <|ad_begin|> + 20:42 [广告-深度转化] <|ad_begin|> +【2025-12-26】 + --:-- [商品-购买] <|prod_begin|> + 22:34 [广告-深度转化] <|ad_begin|> +【2025-12-27】 + 22:26 [广告-深度转化] <|ad_begin|> +【2025-12-28】 + 01:48 [广告-深度转化] <|ad_begin|> + 05:36 [广告-深度转化] <|ad_begin|> + 07:43 [广告-深度转化] <|ad_begin|> + 12:23 [广告-深度转化] <|ad_begin|> + 21:59 [广告-深度转化] <|ad_begin|> +【2025-12-29】 + 03:36 [广告-深度转化] <|ad_begin|> + 03:36 [广告-点击] <|ad_begin|> + 12:11 [广告-深度转化] <|ad_begin|> + 12:11 [广告-点击] <|ad_begin|> + 14:00 [广告-点击] <|ad_begin|> + 23:00 [广告-点击] <|ad_begin|> + 23:53 [广告-深度转化] <|ad_begin|> + 23:53 [广告-点击] <|ad_begin|> +【2025-12-30】 + 01:52 [广告-深度转化] <|ad_begin|> + 01:52 [广告-点击] <|ad_begin|> + 03:56 [广告-深度转化] <|ad_begin|> + 03:56 [广告-点击] <|ad_begin|> + 08:15 [广告-深度转化] <|ad_begin|> + 08:15 [广告-点击] <|ad_begin|> + 11:43 [广告-深度转化] <|ad_begin|> + 11:43 [广告-点击] <|ad_begin|> + 13:26 [广告-深度转化] <|ad_begin|> + 13:26 [广告-点击] <|ad_begin|> +【2025-12-31】 + 01:21 [广告-深度转化] <|ad_begin|> + 01:21 [广告-点击] <|ad_begin|> + 19:55 [广告-深度转化] <|ad_begin|> + 19:55 [广告-点击] <|ad_begin|> +【2026-01-01】 + 00:15 [广告-深度转化] <|ad_begin|> + 00:15 [广告-点击] <|ad_begin|> + 22:19 [广告-深度转化] <|ad_begin|> + 22:19 [广告-点击] <|ad_begin|> + 22:59 [广告-深度转化] <|ad_begin|> + 22:59 [广告-点击] <|ad_begin|> +【2026-01-02】 + 00:43 [广告-深度转化] <|ad_begin|> + 00:43 [广告-点击] <|ad_begin|> + 03:59 [广告-深度转化] <|ad_begin|> + 03:59 [广告-点击] <|ad_begin|> + 09:08 [广告-点击] <|ad_begin|> +【2026-01-03】 + 09:00 [广告-深度转化] <|ad_begin|> + 09:00 [广告-点击] <|ad_begin|> +【2026-01-04】 + 09:23 [广告-深度转化] <|ad_begin|> + 09:23 [广告-点击] <|ad_begin|> + 12:12 [广告-点击] <|ad_begin|> +【2026-01-05】 + 11:34 [广告-深度转化] <|ad_begin|> + 11:34 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-06】 + 00:54 [广告-深度转化] <|ad_begin|> + 00:54 [广告-点击] <|ad_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-07】 + 14:01 [广告-点击] <|ad_begin|> +【2026-01-08】 + --:-- [商品-购买] <|prod_begin|> +【2026-01-09】 + 11:52 [广告-深度转化] <|ad_begin|> + 11:52 [广告-点击] <|ad_begin|> + 12:07 [广告-点击] <|ad_begin|> + 12:18 [广告-点击] <|ad_begin|> +【2026-01-10】 + 17:51 [广告-深度转化] <|ad_begin|> + 17:51 [广告-点击] <|ad_begin|> + 18:44 [广告-深度转化] <|ad_begin|> + 18:44 [广告-点击] <|ad_begin|> +【2026-01-11】 + 20:01 [广告-深度转化] <|ad_begin|> + 20:01 [广告-点击] <|ad_begin|> +【2026-01-12】 + 04:23 [广告-深度转化] <|ad_begin|> + 04:23 [广告-点击] <|ad_begin|> + 05:35 [广告-点击] <|ad_begin|> + 08:27 [广告-深度转化] <|ad_begin|> + 08:27 [广告-点击] <|ad_begin|> +【2026-01-13】 + 02:20 [广告-深度转化] <|ad_begin|> + 02:20 [广告-点击] <|ad_begin|> + 03:16 [广告-深度转化] <|ad_begin|> + 03:16 [广告-点击] <|ad_begin|> + 13:17 [广告-深度转化] <|ad_begin|> + 13:17 [广告-点击] <|ad_begin|> +【2026-01-14】 + 20:39 [广告-深度转化] <|ad_begin|> + 20:39 [广告-点击] <|ad_begin|> +【2026-01-15】 + 07:53 [广告-深度转化] <|ad_begin|> + 07:53 [广告-点击] <|ad_begin|> + 08:21 [广告-点击] <|ad_begin|> +【2026-01-16】 + 00:43 [广告-深度转化] <|ad_begin|> + 00:43 [广告-点击] <|ad_begin|> + 02:15 [广告-点击] <|ad_begin|> + 06:52 [广告-深度转化] <|ad_begin|> + 06:52 [广告-点击] <|ad_begin|> + 07:39 [广告-点击] <|ad_begin|> +【2026-01-17】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-18】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2026-01-21】 + --:-- [商品-购买] <|prod_begin|> + 20:11 [视频-收藏/长播] <|video_begin|> + 20:12 [视频-收藏/长播] <|video_begin|> + 20:24 [视频-收藏/长播] <|video_begin|> + 20:40 [视频-收藏/长播] <|video_begin|> + 20:42 [视频-收藏/长播] <|video_begin|> +【2026-01-22】 + 01:54 [视频-收藏/长播] <|video_begin|> + 04:47 [视频-收藏/长播] <|video_begin|> + 04:47 [视频-收藏/长播] <|video_begin|> + 04:51 [视频-收藏/长播] <|video_begin|> + 07:14 [视频-收藏/长播] <|video_begin|> + 07:37 [视频-收藏/长播] <|video_begin|> + 22:29 [视频-收藏/长播] <|video_begin|> +【2026-01-23】 + 05:07 [视频-收藏/长播] <|video_begin|> + 08:21 [视频-点赞/长播] <|video_begin|> + 08:40 [视频-收藏/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 20:17 [广告-点击] <|ad_begin|> + 20:57 [视频-收藏/长播] <|video_begin|> +【2026-01-24】 + 00:28 [视频-收藏/长播] <|video_begin|> + 01:36 [视频-收藏/长播] <|video_begin|> + 02:36 [视频-收藏/长播] <|video_begin|> + 02:48 [视频-收藏/长播] <|video_begin|> + 02:48 [视频-收藏/长播] <|video_begin|> + 05:06 [视频-收藏/长播] <|video_begin|> + 05:08 [视频-收藏/长播] <|video_begin|> + 05:08 [视频-点赞/长播] <|video_begin|> + 05:52 [视频-收藏/长播] <|video_begin|> + 08:03 [视频-收藏/长播] <|video_begin|> + 14:20 [视频-收藏/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 22:35 [广告-深度转化] <|ad_begin|> + 22:35 [广告-点击] <|ad_begin|> +【2026-01-25】 + 13:26 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 20:12 [广告-深度转化] <|ad_begin|> + 20:12 [广告-点击] <|ad_begin|> +【2026-01-26】 + 19:32 [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|> + 20:39 [广告-深度转化] <|ad_begin|> + 20:39 [广告-点击] <|ad_begin|> +【2026-01-27】 + 00:11 [视频-收藏/长播] <|video_begin|> + 02:59 [视频-评论/长播] <|video_begin|> + 04:44 [广告-深度转化] <|ad_begin|> + 04:44 [广告-点击] <|ad_begin|> + 06:39 [广告-深度转化] <|ad_begin|> + 06:39 [广告-点击] <|ad_begin|> + 11:41 [视频-点赞/长播] <|video_begin|> + 12:32 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 22:13 [广告-深度转化] <|ad_begin|> + 22:13 [广告-点击] <|ad_begin|> +【2026-01-28】 + 03:30 [广告-深度转化] <|ad_begin|> + 03:30 [广告-点击] <|ad_begin|> + 05:23 [广告-深度转化] <|ad_begin|> + 05:23 [广告-点击] <|ad_begin|> + 06:18 [视频-点赞/长播] <|video_begin|> + 06:23 [视频-点赞/长播] <|video_begin|> + 07:46 [视频-收藏/长播] <|video_begin|> + 07:48 [视频-收藏/长播] <|video_begin|> + 08:00 [视频-收藏/长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + 23:32 [视频-收藏/长播] <|video_begin|> +【2026-01-29】 + 04:11 [广告-深度转化] <|ad_begin|> + 04:11 [广告-点击] <|ad_begin|> + 05:10 [视频-收藏/长播] <|video_begin|> + 06:19 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 20:57 [视频-收藏/长播] <|video_begin|> +【2026-01-30】 + 01:30 [广告-深度转化] <|ad_begin|> + 01:30 [广告-点击] <|ad_begin|> + 05:02 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> + 20:38 [广告-点击] <|ad_begin|> + 20:52 [广告-深度转化] <|ad_begin|> + 20:52 [广告-点击] <|ad_begin|> +【2026-01-31】 + 01:34 [视频-收藏/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> +【2026-02-01】 + 00:02 [广告-深度转化] <|ad_begin|> + 00:02 [广告-点击] <|ad_begin|> + 06:07 [视频-浏览] <|video_begin|> + 06:29 [广告-深度转化] <|ad_begin|> + 06:29 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + 23:21 [广告-点击] <|ad_begin|> + 23:43 [视频-收藏/点赞/长播] <|video_begin|> +【2026-02-02】 + 00:47 [广告-点击] <|ad_begin|> + 10:49 [广告-深度转化] <|ad_begin|> + 10:49 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 23:25 [广告-深度转化] <|ad_begin|> + 23:25 [广告-点击] <|ad_begin|> +【2026-02-03】 + 00:36 [广告-点击] <|ad_begin|> + 01:04 [视频-收藏/长播] <|video_begin|> + 02:13 [视频-评论/长播] <|video_begin|> + 02:39 [视频-收藏/长播] <|video_begin|> + 02:48 [视频-收藏/长播] <|video_begin|> + 03:56 [视频-收藏/长播] <|video_begin|> + 04:04 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [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|> + 21:00 [广告-深度转化] <|ad_begin|> + 21:00 [广告-点击] <|ad_begin|> +【2026-02-04】 + 02:05 [广告-深度转化] <|ad_begin|> + 02:05 [广告-点击] <|ad_begin|> + 08:00 [广告-深度转化] <|ad_begin|> + 08:00 [广告-点击] <|ad_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 19:40 [视频-点赞/长播] <|video_begin|> + 20:16 [视频-收藏/长播] <|video_begin|> +【2026-02-05】 + 00:02 [视频-收藏/长播] <|video_begin|> + 00:02 [视频-收藏/长播] <|video_begin|> + 06:55 [视频-长播] <|video_begin|> + 06:58 [视频-长播] <|video_begin|> + 06:58 [视频-收藏/点赞/长播] <|video_begin|> + 06:59 [视频-长播] <|video_begin|> + 06:59 [视频-长播] <|video_begin|> + 07:02 [视频-长播] <|video_begin|> + 07:02 [视频-长播] <|video_begin|> + 07:02 [视频-关注/长播] <|video_begin|> + 07:07 [视频-长播] <|video_begin|> + 07:13 [视频-长播] <|video_begin|> + 07:18 [视频-长播] <|video_begin|> + 07:23 [视频-长播] <|video_begin|> + 07:25 [视频-长播] <|video_begin|> + 07:28 [视频-长播] <|video_begin|> + 07:31 [视频-长播] <|video_begin|> + 07:31 [视频-长播] <|video_begin|> + 07:31 [视频-长播] <|video_begin|> + 07:39 [视频-长播] <|video_begin|> + 07:39 [视频-长播] <|video_begin|> + 07:40 [视频-长播] <|video_begin|> + 07:43 [视频-长播] <|video_begin|> + 07:43 [视频-长播] <|video_begin|> + 07:43 [视频-长播] <|video_begin|> + 07:45 [视频-长播] <|video_begin|> + 07:51 [视频-长播] <|video_begin|> + 07:53 [视频-收藏/长播] <|video_begin|> + 07:55 [视频-长播] <|video_begin|> + 07:56 [视频-长播] <|video_begin|> + 08:00 [视频-长播] <|video_begin|> + 08:00 [视频-长播] <|video_begin|> + 08:02 [视频-长播] <|video_begin|> + 08:04 [视频-长播] <|video_begin|> + 08:04 [视频-长播] <|video_begin|> + 08:04 [视频-长播] <|video_begin|> + 08:06 [视频-长播] <|video_begin|> + 08:06 [视频-长播] <|video_begin|> + 08:06 [视频-长播] <|video_begin|> + 08:47 [视频-长播] <|video_begin|> + 12:01 [视频-长播] <|video_begin|> + 12:04 [视频-长播] <|video_begin|> + 12:04 [视频-长播] <|video_begin|> + 12:07 [视频-长播] <|video_begin|> + 12:07 [视频-长播] <|video_begin|> + 12:07 [视频-长播] <|video_begin|> + 12:07 [视频-长播] <|video_begin|> + 12:07 [视频-长播] <|video_begin|> + 12:22 [视频-长播] <|video_begin|> + 12:22 [视频-长播] <|video_begin|> + 12:24 [视频-长播] <|video_begin|> + 12:24 [视频-长播] <|video_begin|> + 12:27 [视频-长播] <|video_begin|> + 12:27 [视频-长播] <|video_begin|> + 12:27 [视频-长播] <|video_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）： 搜索-搜索、视频-浏览、视频-长播、直播-关注、直播-首次打赏、商品-点击、商品-购买、广告-点击、广告-深度转化 +核心逻辑要求（递进性准则）： +请依时间顺序提取 5 步以内的行为链，并确保后项交互B是前项交互A的场景需求协同补充、副作用诱因或兴趣细化升级，也就是 B 必须由 A 的延伸需求所驱动；严禁提取任何品类平级、内容重复或仅因天气/促销形成的并列凑单行为。 +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 是因为 交互 A 产生的副作用或新需求，或者 A 是 B 的诱因。 +3.需求深度细化：在同一主题类目下,交互 B 的需求比 交互 A 更具体详细, 但不包括多次重复的购买、点击、搜索等行为，避免重复节点内容。 +请根据以下主题提取行为逻辑链： +主题：从健康焦虑到精准化饮食管理的认知演化 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从健康焦虑到精准化饮食管理的认知演化", + "events": [ + { + "date": "YYYY-MM-DD", + "action": "[交互行为类型] 交互内容"（严格对应Timeline中交互条目，不允许省略）, + "logic": "逻辑说明" + } + ] + } +} + +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":"从健康焦虑到精准化饮食管理的认知演化","events":[{"date":"2026-02-01","action":"[视频-浏览] <|video_begin|>","logic":"问题触发：通过浏览甲状腺术后康复视频，表现出对该健康问题的初步关注。"},{"date":"2026-02-03","action":"[视频-评论/长播] <|video_begin|>","logic":"兴趣深化：从关注术后即时状态转向长期康复和生活方式管理，通过观看他人经验分享，寻求可行的实践路径。"},{"date":"2026-02-05","action":"[视频-长播] <|video_begin|>","logic":"认知收敛与增量：从泛化的生活方式管理，聚焦到具体的营养素（硒）和食疗方案，完成了从“问题是什么”到“具体怎么做”的认知闭环，获得了明确的行动指导。"}]}}<|im_end|> +<|im_start|>user +【用户交互历史】： +【2024-06-08】 + --:-- [商品-购买] <|prod_begin|> +【2024-06-30】 + --:-- [商品-购买] <|prod_begin|> +【2024-07-17】 + --:-- [商品-购买] <|prod_begin|> +【2024-08-20】 + --:-- [商品-购买] <|prod_begin|> +【2024-08-31】 + --:-- [商品-购买] <|prod_begin|> +【2024-09-18】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-11】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注/首次打赏] <|living_begin|> +【2025-01-16】 + --:-- [直播-关注] <|living_begin|> +【2025-01-18】 + --:-- [直播-关注] <|living_begin|> +【2025-04-26】 + --:-- [直播-关注] <|living_begin|> +【2025-05-02】 + --:-- [直播-关注] <|living_begin|> +【2025-07-20】 + --:-- [直播-关注] <|living_begin|> +【2025-07-28】 + --:-- [直播-关注] <|living_begin|> + --:-- [直播-关注] <|living_begin|> + --:-- [直播-首次打赏] <|living_begin|> +【2025-08-02】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-08-09】 + --:-- [商品-点击] <|prod_begin|> +【2025-08-17】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-09-17】 + --:-- [直播-关注] <|living_begin|> +【2025-09-25】 + --:-- [直播-关注] <|living_begin|> +【2025-09-30】 + --:-- [直播-关注] <|living_begin|> +【2025-10-30】 + --:-- [直播-关注] <|living_begin|> +【2025-11-04】 + --:-- [直播-关注] <|living_begin|> +【2025-11-05】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-21】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-11-27】 + --:-- [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|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-11-28】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-11-29】 + --:-- [直播-关注] <|living_begin|> +【2025-12-04】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-05】 + --:-- [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|> +【2025-12-07】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-09】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-10】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-13】 + 12:59 [广告-点击] <|ad_begin|> +【2025-12-15】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-12-16】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-19】 + 10:22 [广告-点击] <|ad_begin|> +【2025-12-20】 + --:-- [直播-关注] <|living_begin|> + 06:35 [广告-点击] <|ad_begin|> +【2025-12-21】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-25】 + 07:28 [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-购买] <|prod_begin|> + 18:24 [广告-点击] <|ad_begin|> +【2025-12-26】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-27】 + 05:40 [广告-点击] <|ad_begin|> + 18:09 [广告-点击] <|ad_begin|> +【2025-12-29】 + 15:45 [广告-点击] <|ad_begin|> + 16:07 [广告-点击] <|ad_begin|> +【2025-12-31】 + 19:29 [广告-点击] <|ad_begin|> + 19:31 [广告-点击] <|ad_begin|> +【2026-01-01】 + 14:10 [广告-点击] <|ad_begin|> + 19:19 [广告-点击] <|ad_begin|> +【2026-01-02】 + --:-- [直播-首次打赏] <|living_begin|> + 14:52 [广告-点击] <|ad_begin|> +【2026-01-06】 + 05:16 [广告-点击] <|ad_begin|> +【2026-01-08】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + 14:05 [广告-点击] <|ad_begin|> + 14:44 [广告-点击] <|ad_begin|> + 19:34 [广告-点击] <|ad_begin|> + 19:45 [广告-点击] <|ad_begin|> + 20:05 [广告-点击] <|ad_begin|> +【2026-01-10】 + --:-- [直播-关注] <|living_begin|> + 07:03 [广告-点击] <|ad_begin|> + 16:44 [广告-点击] <|ad_begin|> +【2026-01-11】 + 19:47 [广告-点击] <|ad_begin|> +【2026-01-12】 + 10:50 [广告-点击] <|ad_begin|> +【2026-01-13】 + 04:38 [广告-点击] <|ad_begin|> +【2026-01-14】 + 19:29 [广告-点击] <|ad_begin|> +【2026-01-16】 + 17:40 [广告-点击] <|ad_begin|> + 21:13 [广告-点击] <|ad_begin|> +【2026-01-17】 + 16:43 [广告-点击] <|ad_begin|> +【2026-01-18】 + 12:01 [广告-点击] <|ad_begin|> + 20:08 [广告-深度转化] <|ad_begin|> + 20:08 [广告-点击] <|ad_begin|> +【2026-01-19】 + --:-- [商品-点击] <|prod_begin|> + 10:08 [广告-点击] <|ad_begin|> +【2026-01-20】 + 06:05 [广告-点击] <|ad_begin|> + 10:16 [广告-点击] <|ad_begin|> +【2026-01-21】 + 07:32 [广告-点击] <|ad_begin|> + 09:46 [广告-点击] <|ad_begin|> + 16:03 [视频-评论/点赞/长播] <|video_begin|> + 19:42 [视频-评论/收藏/长播] <|video_begin|> +【2026-01-22】 + --:-- [直播-关注] <|living_begin|> + 01:06 [视频-收藏/长播] <|video_begin|> + 01:46 [视频-收藏/长播] <|video_begin|> + 03:49 [视频-评论/点赞/长播] <|video_begin|> + 03:49 [视频-关注/点赞/长播] <|video_begin|> + 17:38 [视频-收藏/长播] <|video_begin|> + 17:49 [广告-点击] <|ad_begin|> + 21:57 [视频-收藏/长播] <|video_begin|> + 23:37 [视频-评论/点赞/长播] <|video_begin|> +【2026-01-23】 + 00:29 [视频-转发/长播] <|video_begin|> + 00:36 [视频-转发/点赞/长播] <|video_begin|> + 02:50 [广告-点击] <|ad_begin|> + 04:21 [视频-收藏/长播] <|video_begin|> + 13:19 [视频-评论/点赞/长播] <|video_begin|> +【2026-01-25】 + 02:49 [视频-收藏/长播] <|video_begin|> + 04:47 [视频-评论/点赞/长播] <|video_begin|> + 05:04 [视频-收藏/长播] <|video_begin|> + 15:23 [视频-评论/点赞/长播] <|video_begin|> + 15:42 [视频-收藏/长播] <|video_begin|> +【2026-01-26】 + 03:21 [视频-评论/点赞/长播] <|video_begin|> + 05:34 [视频-收藏/长播] <|video_begin|> + 07:25 [视频-点赞/长播] <|video_begin|> + 13:55 [视频-转发/长播] <|video_begin|> + 15:02 [视频-点赞/长播] <|video_begin|> + 15:39 [视频-评论/长播] <|video_begin|> + 15:39 [视频-评论/长播] <|video_begin|> + 16:52 [视频-点赞/长播] <|video_begin|> + 17:16 [视频-转发/收藏/长播] <|video_begin|> +【2026-01-27】 + 10:43 [视频-点赞/长播] <|video_begin|> + 10:48 [视频-点赞/长播] <|video_begin|> + 13:56 [视频-评论/点赞/长播] <|video_begin|> + 16:11 [视频-点赞/长播] <|video_begin|> + 16:14 [视频-评论/长播] <|video_begin|> + 16:25 [视频-评论/点赞/长播] <|video_begin|> + 16:50 [视频-点赞/长播] <|video_begin|> + 16:50 [视频-点赞/长播] <|video_begin|> + 18:34 [广告-点击] <|ad_begin|> + 23:30 [视频-点赞/长播] <|video_begin|> + 23:30 [视频-收藏/长播] <|video_begin|> +【2026-01-28】 + 00:03 [视频-评论/长播] <|video_begin|> + 01:03 [视频-评论/长播] <|video_begin|> + 01:17 [视频-关注/点赞/长播] <|video_begin|> + 01:29 [视频-点赞/长播] <|video_begin|> + 01:36 [广告-点击] <|ad_begin|> + 06:30 [视频-评论/长播] <|video_begin|> + 06:55 [视频-评论/点赞/长播] <|video_begin|> + 06:55 [视频-点赞/长播] <|video_begin|> + 07:02 [视频-转发/长播] <|video_begin|> + 07:25 [视频-评论/长播] <|video_begin|> + 07:33 [视频-评论/长播] <|video_begin|> + 08:01 [视频-点赞/长播] <|video_begin|> + 08:04 [视频-评论/长播] <|video_begin|> + 08:15 [视频-点赞/长播] <|video_begin|> + 08:33 [视频-点赞/长播] <|video_begin|> + 08:58 [视频-评论/长播] <|video_begin|> + 08:58 [视频-点赞/长播] <|video_begin|> + 09:43 [视频-评论/长播] <|video_begin|> + 10:02 [视频-点赞/长播] <|video_begin|> + 10:20 [视频-评论/长播] <|video_begin|> + 10:22 [视频-点赞/长播] <|video_begin|> + 10:33 [视频-评论/点赞/长播] <|video_begin|> + 10:43 [视频-评论/长播] <|video_begin|> + 10:43 [视频-评论/长播] <|video_begin|> + 11:01 [视频-点赞/长播] <|video_begin|> + 11:29 [视频-评论/长播] <|video_begin|> + 15:17 [视频-评论/点赞/长播] <|video_begin|> + 15:41 [视频-点赞/长播] <|video_begin|> + 23:04 [视频-评论/长播] <|video_begin|> + 23:13 [视频-评论/长播] <|video_begin|> + 23:18 [视频-点赞/长播] <|video_begin|> + 23:18 [视频-收藏/长播] <|video_begin|> + 23:20 [视频-转发/长播] <|video_begin|> +【2026-01-29】 + 01:34 [视频-评论/长播] <|video_begin|> + 01:34 [视频-点赞/长播] <|video_begin|> + 07:00 [视频-评论/长播] <|video_begin|> + 07:00 [视频-评论/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 10:40 [视频-点赞/长播] <|video_begin|> + 10:55 [视频-评论/长播] <|video_begin|> + 11:15 [视频-评论/长播] <|video_begin|> + 11:15 [视频-评论/长播] <|video_begin|> + 13:04 [视频-点赞/长播] <|video_begin|> + 13:04 [视频-点赞/长播] <|video_begin|> + 15:36 [视频-评论/点赞/长播] <|video_begin|> + 15:42 [视频-点赞/长播] <|video_begin|> + 18:04 [视频-浏览] <|video_begin|> + 19:22 [视频-收藏/点赞/长播] <|video_begin|> + 19:31 [视频-评论/长播] <|video_begin|> + 20:22 [视频-点赞/长播] <|video_begin|> + 21:53 [视频-评论/长播] <|video_begin|> +【2026-01-30】 + --:-- [商品-点击] <|prod_begin|> + 12:29 [视频-评论/长播] <|video_begin|> + 13:08 [视频-评论/长播] <|video_begin|> + 13:27 [视频-评论/长播] <|video_begin|> + 13:27 [视频-评论/长播] <|video_begin|> + 18:58 [视频-评论/收藏/长播] <|video_begin|> + 19:49 [视频-评论/长播] <|video_begin|> + 21:35 [视频-评论/收藏/长播] <|video_begin|> + 21:46 [视频-评论/长播] <|video_begin|> + 21:50 [视频-点赞/长播] <|video_begin|> +【2026-01-31】 + 01:10 [视频-评论/点赞/长播] <|video_begin|> + 01:10 [视频-点赞/长播] <|video_begin|> + --:-- [商品-点击] <|prod_begin|> + 15:52 [视频-点赞/长播] <|video_begin|> + 15:52 [视频-点赞/长播] <|video_begin|> + 18:40 [视频-点赞/长播] <|video_begin|> + 20:57 [视频-评论/长播] <|video_begin|> +【2026-02-01】 + 14:04 [视频-评论/长播] <|video_begin|> + 15:52 [视频-点赞/长播] <|video_begin|> + 17:18 [视频-评论/长播] <|video_begin|> + 19:06 [视频-评论/长播] <|video_begin|> + 19:15 [视频-点赞/长播] <|video_begin|> +【2026-02-02】 + 03:41 [视频-点赞/长播] <|video_begin|> + 03:41 [视频-点赞/长播] <|video_begin|> + 03:52 [视频-点赞/长播] <|video_begin|> + 07:15 [视频-评论/点赞/长播] <|video_begin|> + 07:24 [视频-评论/点赞/长播] <|video_begin|> + 07:28 [视频-点赞/长播] <|video_begin|> + 07:32 [视频-点赞/长播] <|video_begin|> + 07:40 [视频-评论/长播] <|video_begin|> + 07:59 [视频-评论/长播] <|video_begin|> + 08:21 [视频-点赞/长播] <|video_begin|> + 08:47 [视频-评论/长播] <|video_begin|> + 13:23 [视频-评论/长播] <|video_begin|> + 13:28 [视频-收藏/长播] <|video_begin|> + 16:08 [视频-浏览] <|video_begin|> + 16:10 [视频-评论/长播] <|video_begin|> + 16:25 [视频-点赞/长播] <|video_begin|> + 17:05 [视频-评论/长播] <|video_begin|> + 17:09 [视频-点赞/长播] <|video_begin|> + 17:10 [视频-评论/长播] <|video_begin|> +【2026-02-03】 + 04:45 [视频-评论/长播] <|video_begin|> + 08:53 [视频-收藏/长播] <|video_begin|> + 09:07 [视频-点赞/长播] <|video_begin|> + 09:29 [视频-评论/长播] <|video_begin|> + 09:36 [视频-点赞/长播] <|video_begin|> + 11:47 [视频-长播] <|video_begin|> + 11:55 [视频-长播] <|video_begin|> + 11:55 [视频-长播] <|video_begin|> + 11:57 [视频-长播] <|video_begin|> + 13:02 [视频-长播] <|video_begin|> + 13:02 [视频-长播] <|video_begin|> + 13:02 [视频-长播] <|video_begin|> + 13:02 [视频-长播] <|video_begin|> + 13:08 [视频-长播] <|video_begin|> + 13:08 [视频-长播] <|video_begin|> + 13:08 [视频-评论/长播] <|video_begin|> + 16:07 [广告-点击] <|ad_begin|> + 19:52 [视频-长播] <|video_begin|> + 19:52 [视频-长播] <|video_begin|> + 20:13 [视频-点赞/长播] <|video_begin|> + 20:41 [视频-评论/长播] <|video_begin|> + 21:04 [视频-评论/点赞/长播] <|video_begin|> + 21:09 [视频-长播] <|video_begin|> + 21:09 [视频-评论/长播] <|video_begin|> + 22:14 [视频-评论/长播] <|video_begin|> + 22:29 [视频-评论/长播] <|video_begin|> + 22:29 [视频-收藏/长播] <|video_begin|> + 22:40 [视频-点赞/长播] <|video_begin|> + 22:40 [视频-点赞/长播] <|video_begin|> + 23:19 [视频-长播] <|video_begin|> + 23:19 [视频-长播] <|video_begin|> +【2026-02-04】 + 07:44 [视频-长播] <|video_begin|> + 09:45 [视频-长播] <|video_begin|> + 09:50 [视频-长播] <|video_begin|> + 13:30 [视频-长播] <|video_begin|> + 13:30 [视频-点赞/长播] <|video_begin|> + 13:30 [视频-点赞/长播] <|video_begin|> + 13:44 [视频-长播] <|video_begin|> + 13:44 [视频-评论/长播] <|video_begin|> + 17:58 [视频-长播] <|video_begin|> + 18:10 [视频-评论/点赞/长播] <|video_begin|> + 21:10 [视频-长播] <|video_begin|> + 21:14 [视频-长播] <|video_begin|> + 21:20 [视频-长播] <|video_begin|> + 21:20 [视频-长播] <|video_begin|> + 21:27 [视频-长播] <|video_begin|> + 21:27 [视频-长播] <|video_begin|> + 21:46 [视频-点赞/长播] <|video_begin|> + 21:54 [视频-长播] <|video_begin|> + 21:54 [视频-长播] <|video_begin|> + 21:57 [视频-长播] <|video_begin|> + 22:03 [视频-长播] <|video_begin|> + 22:12 [视频-长播] <|video_begin|> + 22:12 [视频-长播] <|video_begin|> + 22:16 [视频-长播] <|video_begin|> + 22:34 [视频-长播] <|video_begin|> + 22:34 [视频-长播] <|video_begin|> + 22:37 [视频-长播] <|video_begin|> + 22:44 [视频-长播] <|video_begin|> + 22:49 [视频-长播] <|video_begin|> +【2026-02-05】 + 07:45 [视频-长播] <|video_begin|> + 07:48 [视频-长播] <|video_begin|> + 08:55 [视频-长播] <|video_begin|> + 08:55 [视频-长播] <|video_begin|> + 08:55 [视频-长播] <|video_begin|> + 13:23 [视频-长播] <|video_begin|> + 13:23 [视频-长播] <|video_begin|> + 13:34 [视频-长播] <|video_begin|> + 13:34 [视频-点赞/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|> + +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：从有线外设到无线解决方案的体验优化 + +输出格式要求：输出必须且仅为包含 SID 的 JSON 数组，绝对不要输出任何额外的解释、提示或格式化外的文字。 + +强约束：最终答案必须是一个可被 json.loads 解析的 JSON，不能包含 Markdown、解释、重复键或 JSON 外额外字符。/no_think<|im_end|> +<|im_start|>assistant +["<|living_begin|>","<|living_begin|>","<|living_begin|>","<|living_begin|>","<|living_begin|>","<|living_begin|>","<|living_begin|>","<|living_begin|>","<|living_begin|>","<|living_begin|>","<|living_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|living_begin|>","<|prod_begin|>","<|living_begin|>","<|living_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>"]<|im_end|> +<|im_start|>user +【用户交互历史】： +【2022-09-19】 + --:-- [商品-购买] <|prod_begin|> +【2022-10-04】 + --:-- [商品-购买] <|prod_begin|> +【2022-10-16】 + --:-- [商品-购买] <|prod_begin|> +【2022-12-01】 + --:-- [商品-购买] <|prod_begin|> +【2023-03-28】 + --:-- [商品-购买] <|prod_begin|> +【2023-04-01】 + --:-- [商品-购买] <|prod_begin|> +【2023-04-09】 + --:-- [商品-购买] <|prod_begin|> +【2023-04-17】 + --:-- [商品-购买] <|prod_begin|> +【2023-06-26】 + --:-- [商品-购买] <|prod_begin|> +【2023-07-08】 + --:-- [商品-购买] <|prod_begin|> +【2023-07-28】 + --:-- [商品-购买] <|prod_begin|> +【2023-08-03】 + --:-- [商品-购买] <|prod_begin|> +【2023-08-26】 + --:-- [商品-购买] <|prod_begin|> +【2023-09-01】 + --:-- [商品-购买] <|prod_begin|> +【2023-12-18】 + --:-- [商品-购买] <|prod_begin|> +【2024-01-09】 + --:-- [商品-购买] <|prod_begin|> +【2024-02-13】 + --:-- [商品-购买] <|prod_begin|> +【2024-03-10】 + --:-- [商品-购买] <|prod_begin|> +【2024-04-01】 + --:-- [商品-购买] <|prod_begin|> +【2024-05-19】 + --:-- [商品-购买] <|prod_begin|> +【2024-05-25】 + --:-- [商品-购买] <|prod_begin|> +【2024-05-26】 + --:-- [商品-购买] <|prod_begin|> +【2024-06-24】 + --:-- [商品-购买] <|prod_begin|> +【2024-06-30】 + --:-- [商品-购买] <|prod_begin|> +【2024-07-02】 + --:-- [商品-购买] <|prod_begin|> +【2024-07-06】 + --:-- [商品-购买] <|prod_begin|> +【2024-08-17】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-08】 + --:-- [商品-购买] <|prod_begin|> +【2024-10-30】 + --:-- [商品-购买] <|prod_begin|> +【2024-11-20】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-02】 + --:-- [商品-购买] <|prod_begin|> +【2025-01-23】 + --:-- [直播-关注] <|living_begin|> +【2025-02-26】 + --:-- [直播-关注] <|living_begin|> +【2025-03-04】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-05】 + --:-- [直播-关注] <|living_begin|> +【2025-03-10】 + --:-- [商品-购买] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-03-12】 + --:-- [直播-关注] <|living_begin|> +【2025-03-19】 + --:-- [直播-关注] <|living_begin|> +【2025-03-27】 + --:-- [商品-购买] <|prod_begin|> +【2025-03-31】 + --:-- [直播-关注] <|living_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-04-16】 + --:-- [直播-首次打赏] <|living_begin|> +【2025-04-17】 + --:-- [直播-关注] <|living_begin|> +【2025-04-18】 + --:-- [直播-关注] <|living_begin|> +【2025-04-19】 + --:-- [直播-关注] <|living_begin|> +【2025-05-03】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-05-11】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-05-12】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-05-14】 + --:-- [商品-点击] <|prod_begin|> +【2025-05-24】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-05-25】 + --:-- [商品-点击] <|prod_begin|> +【2025-05-31】 + --:-- [商品-点击] <|prod_begin|> +【2025-06-03】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-06-17】 + --:-- [直播-关注] <|living_begin|> +【2025-06-21】 + --:-- [商品-点击] <|prod_begin|> +【2025-06-23】 + --:-- [商品-点击] <|prod_begin|> +【2025-06-24】 + --:-- [商品-点击] <|prod_begin|> +【2025-06-30】 + --:-- [直播-关注/首次打赏] <|living_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-07-01】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-07-12】 + --:-- [商品-点击] <|prod_begin|> +【2025-07-16】 + --:-- [商品-点击] <|prod_begin|> +【2025-07-19】 + --:-- [商品-点击] <|prod_begin|> +【2025-07-21】 + --:-- [商品-点击] <|prod_begin|> +【2025-07-25】 + --:-- [商品-点击] <|prod_begin|> +【2025-08-09】 + --:-- [商品-点击] <|prod_begin|> +【2025-08-10】 + --:-- [商品-点击] <|prod_begin|> +【2025-08-24】 + --:-- [商品-点击] <|prod_begin|> +【2025-08-30】 + --:-- [商品-点击] <|prod_begin|> +【2025-08-31】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-09-08】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> +【2025-09-10】 + --:-- [商品-点击] <|prod_begin|> +【2025-09-11】 + --:-- [商品-点击] <|prod_begin|> +【2025-09-14】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-10-01】 + --:-- [直播-关注] <|living_begin|> +【2025-10-29】 + --:-- [商品-点击] <|prod_begin|> +【2025-11-01】 + --:-- [商品-点击] <|prod_begin|> +【2025-12-11】 + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-点击] <|prod_begin|> + --:-- [商品-购买] <|prod_begin|> +【2025-12-29】 + 01:21 [广告-深度转化] <|ad_begin|> +【2025-12-31】 + 23:10 [广告-深度转化] <|ad_begin|> +【2026-01-01】 + 20:46 [广告-深度转化] <|ad_begin|> +【2026-01-02】 + 12:09 [广告-深度转化] <|ad_begin|> +【2026-01-03】 + 10:49 [广告-深度转化] <|ad_begin|> +【2026-01-05】 + 01:10 [广告-深度转化] <|ad_begin|> +【2026-01-08】 + 17:07 [广告-深度转化] <|ad_begin|> +【2026-01-09】 + 01:08 [广告-深度转化] <|ad_begin|> + 05:26 [广告-深度转化] <|ad_begin|> + 11:22 [广告-深度转化] <|ad_begin|> +【2026-01-11】 + 12:22 [广告-深度转化] <|ad_begin|> +【2026-01-12】 + 00:13 [广告-深度转化] <|ad_begin|> + 00:53 [广告-深度转化] <|ad_begin|> + 04:53 [广告-深度转化] <|ad_begin|> +【2026-01-13】 + 01:05 [广告-深度转化] <|ad_begin|> + 01:05 [广告-点击] <|ad_begin|> + 04:58 [广告-深度转化] <|ad_begin|> + 04:58 [广告-点击] <|ad_begin|> + 08:57 [广告-深度转化] <|ad_begin|> + 08:57 [广告-点击] <|ad_begin|> +【2026-01-14】 + 13:37 [广告-深度转化] <|ad_begin|> + 13:37 [广告-点击] <|ad_begin|> +【2026-01-15】 + 09:03 [广告-点击] <|ad_begin|> + 12:56 [广告-深度转化] <|ad_begin|> + 12:56 [广告-点击] <|ad_begin|> +【2026-01-16】 + 00:30 [广告-点击] <|ad_begin|> + 04:42 [广告-深度转化] <|ad_begin|> + 04:42 [广告-点击] <|ad_begin|> + 06:19 [广告-点击] <|ad_begin|> + 10:52 [广告-点击] <|ad_begin|> + 11:27 [广告-深度转化] <|ad_begin|> + 11:27 [广告-点击] <|ad_begin|> + 12:05 [广告-点击] <|ad_begin|> +【2026-01-17】 + 10:56 [广告-深度转化] <|ad_begin|> + 10:56 [广告-点击] <|ad_begin|> + 11:16 [广告-深度转化] <|ad_begin|> + 11:16 [广告-点击] <|ad_begin|> + 12:04 [广告-深度转化] <|ad_begin|> + 12:04 [广告-点击] <|ad_begin|> +【2026-01-18】 + 21:45 [广告-深度转化] <|ad_begin|> + 21:45 [广告-点击] <|ad_begin|> +【2026-01-19】 + 01:01 [广告-深度转化] <|ad_begin|> + 01:01 [广告-点击] <|ad_begin|> + 05:14 [广告-深度转化] <|ad_begin|> + 05:14 [广告-点击] <|ad_begin|> + 06:49 [广告-深度转化] <|ad_begin|> + 06:49 [广告-点击] <|ad_begin|> + 12:08 [广告-深度转化] <|ad_begin|> + 12:08 [广告-点击] <|ad_begin|> +【2026-01-20】 + 13:05 [广告-点击] <|ad_begin|> +【2026-01-21】 + 00:55 [广告-深度转化] <|ad_begin|> + 00:55 [广告-点击] <|ad_begin|> + 04:09 [广告-深度转化] <|ad_begin|> + 04:09 [广告-点击] <|ad_begin|> + 04:22 [广告-深度转化] <|ad_begin|> + 04:22 [广告-点击] <|ad_begin|> + 07:23 [广告-深度转化] <|ad_begin|> + 07:23 [广告-点击] <|ad_begin|> + 07:37 [广告-深度转化] <|ad_begin|> + 07:37 [广告-点击] <|ad_begin|> + 10:38 [广告-深度转化] <|ad_begin|> + 10:38 [广告-点击] <|ad_begin|> +【2026-01-24】 + 01:16 [广告-深度转化] <|ad_begin|> + 01:16 [广告-点击] <|ad_begin|> + 05:22 [广告-深度转化] <|ad_begin|> + 05:22 [广告-点击] <|ad_begin|> + 07:09 [广告-深度转化] <|ad_begin|> + 07:09 [广告-点击] <|ad_begin|> + 10:46 [广告-点击] <|ad_begin|> + 12:39 [广告-深度转化] <|ad_begin|> + 12:39 [广告-点击] <|ad_begin|> +【2026-01-25】 + 00:46 [视频-点赞/长播] <|video_begin|> + 02:46 [视频-点赞/长播] <|video_begin|> + 04:42 [广告-深度转化] <|ad_begin|> + 04:42 [广告-点击] <|ad_begin|> + 04:52 [视频-点赞/长播] <|video_begin|> + 05:20 [视频-点赞/长播] <|video_begin|> + 06:46 [广告-点击] <|ad_begin|> + 06:46 [视频-点赞/长播] <|video_begin|> + 21:19 [广告-深度转化] <|ad_begin|> + 21:19 [广告-点击] <|ad_begin|> +【2026-01-26】 + 05:35 [广告-深度转化] <|ad_begin|> + 05:35 [广告-点击] <|ad_begin|> + 05:39 [广告-深度转化] <|ad_begin|> + 05:39 [广告-点击] <|ad_begin|> + 06:42 [视频-长播] <|video_begin|> + 08:30 [广告-点击] <|ad_begin|> + 13:34 [广告-深度转化] <|ad_begin|> + 13:34 [广告-点击] <|ad_begin|> + 22:05 [广告-深度转化] <|ad_begin|> + 22:05 [广告-点击] <|ad_begin|> + 22:58 [广告-深度转化] <|ad_begin|> + 22:58 [广告-点击] <|ad_begin|> +【2026-01-27】 + 01:24 [广告-深度转化] <|ad_begin|> + 01:24 [广告-点击] <|ad_begin|> + 04:19 [视频-长播] <|video_begin|> + 04:19 [视频-长播] <|video_begin|> + 04:19 [视频-长播] <|video_begin|> + 04:56 [广告-深度转化] <|ad_begin|> + 04:56 [广告-点击] <|ad_begin|> + 06:15 [广告-深度转化] <|ad_begin|> + 06:15 [广告-点击] <|ad_begin|> + 06:44 [广告-点击] <|ad_begin|> + 07:41 [视频-长播] <|video_begin|> +【2026-01-28】 + 21:27 [广告-深度转化] <|ad_begin|> + 21:27 [广告-点击] <|ad_begin|> + 21:49 [广告-深度转化] <|ad_begin|> + 21:49 [广告-点击] <|ad_begin|> +【2026-01-29】 + 00:31 [视频-长播] <|video_begin|> + 00:52 [广告-深度转化] <|ad_begin|> + 00:52 [广告-点击] <|ad_begin|> + 05:55 [广告-深度转化] <|ad_begin|> + 05:55 [广告-点击] <|ad_begin|> + 09:56 [视频-长播] <|video_begin|> + 09:57 [视频-长播] <|video_begin|> + 10:55 [广告-深度转化] <|ad_begin|> + 10:55 [广告-点击] <|ad_begin|> +【2026-01-30】 + 00:41 [广告-深度转化] <|ad_begin|> + 00:41 [广告-点击] <|ad_begin|> + 04:14 [广告-深度转化] <|ad_begin|> + 04:14 [广告-点击] <|ad_begin|> + 05:10 [视频-点赞/长播] <|video_begin|> + 08:41 [视频-长播] <|video_begin|> + 10:34 [视频-长播] <|video_begin|> +【2026-01-31】 + 01:07 [广告-深度转化] <|ad_begin|> + 01:07 [广告-点击] <|ad_begin|> + 05:03 [广告-深度转化] <|ad_begin|> + 05:03 [广告-点击] <|ad_begin|> + 06:46 [广告-深度转化] <|ad_begin|> + 06:46 [广告-点击] <|ad_begin|> + 11:06 [视频-长播] <|video_begin|> + 11:49 [视频-长播] <|video_begin|> + 11:49 [视频-长播] <|video_begin|> + 11:54 [视频-长播] <|video_begin|> + 12:04 [视频-长播] <|video_begin|> +【2026-02-01】 + 05:30 [视频-长播] <|video_begin|> + 05:30 [视频-长播] <|video_begin|> + 05:48 [视频-长播] <|video_begin|> + --:-- [商品-购买] <|prod_begin|> + 06:36 [广告-深度转化] <|ad_begin|> + 06:36 [广告-点击] <|ad_begin|> + 12:36 [广告-深度转化] <|ad_begin|> + 12:36 [广告-点击] <|ad_begin|> +【2026-02-02】 + 01:45 [广告-深度转化] <|ad_begin|> + 01:45 [广告-点击] <|ad_begin|> + 04:11 [广告-深度转化] <|ad_begin|> + 04:11 [广告-点击] <|ad_begin|> + 04:14 [视频-长播] <|video_begin|> + 04:14 [视频-长播] <|video_begin|> + 04:59 [广告-深度转化] <|ad_begin|> + 04:59 [广告-点击] <|ad_begin|> + 06:46 [视频-长播] <|video_begin|> + 08:49 [视频-长播] <|video_begin|> + 10:35 [视频-长播] <|video_begin|> + 11:37 [广告-深度转化] <|ad_begin|> + 11:37 [广告-点击] <|ad_begin|> + 11:50 [广告-点击] <|ad_begin|> + 12:19 [广告-点击] <|ad_begin|> + 21:23 [视频-长播] <|video_begin|> +【2026-02-03】 + 03:07 [广告-深度转化] <|ad_begin|> + 03:07 [广告-点击] <|ad_begin|> + 03:52 [视频-长播] <|video_begin|> + 05:25 [视频-长播] <|video_begin|> + 05:25 [视频-长播] <|video_begin|> + 05:25 [视频-长播] <|video_begin|> + 05:39 [视频-长播] <|video_begin|> + 09:49 [广告-深度转化] <|ad_begin|> + 09:49 [广告-点击] <|ad_begin|> +【2026-02-04】 + 04:45 [视频-长播] <|video_begin|> + 05:41 [视频-长播] <|video_begin|> + 06:09 [视频-长播] <|video_begin|> + 11:56 [广告-点击] <|ad_begin|> + 12:17 [视频-长播] <|video_begin|> + 13:07 [视频-长播] <|video_begin|> + 13:21 [视频-长播] <|video_begin|> + 21:28 [视频-长播] <|video_begin|> +【2026-02-05】 + 00:23 [视频-长播] <|video_begin|> + 00:23 [视频-长播] <|video_begin|> + 00:55 [广告-深度转化] <|ad_begin|> + 00:55 [广告-点击] <|ad_begin|> + 01:03 [视频-长播] <|video_begin|> + 01:03 [视频-长播] <|video_begin|> + 01:03 [视频-长播] <|video_begin|> + 04:40 [视频-长播] <|video_begin|> + 05:02 [广告-深度转化] <|ad_begin|> + 05:02 [广告-点击] <|ad_begin|> + 05:27 [视频-长播] <|video_begin|> + 07:37 [视频-长播] <|video_begin|> + 08:39 [视频-长播] <|video_begin|> + 10:09 [视频-长播] <|video_begin|> + 10:09 [视频-长播] <|video_begin|> + 10:55 [视频-长播] <|video_begin|> + 11:07 [广告-深度转化] <|ad_begin|> + 11:07 [广告-点击] <|ad_begin|> + 11:15 [视频-长播] <|video_begin|> + 11:15 [视频-长播] <|video_begin|> + 11:27 [广告-点击] <|ad_begin|> +角色任务：你是一名极端严苛的用户行为数据挖掘专家和数据格式化专家，请基于以上用户交互历史，围绕指定主题提取具备行为递进关系与内在关联的交互行为逻辑链。 +交互行为类型（固定格式严禁更改）： 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 +核心提取逻辑（递进性准则）： +按照时间顺序抽取 5 步以内的行为链，要求后续交互B必须是前序交互A在场景需求协同补充、副作用诱发或兴趣细化升级上的延伸——也就是说，B 必须因 A 而产生，严禁提取任何品类平级、内容重复或仅受天气/促销影响形成的并列凑单行为。 +1.场景驱动协同：交互 B 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 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：在相同主题类目下，交互 B 的需求应较交互 A 更加具体和细致，但不包含多次重复的购买、点击、搜索等行为，以避免节点内容重复。 +请面向以下主题提取行为逻辑链： +主题：从皮肤问题到牙齿健康的健康焦点演化 + +输出格式：请以 JSON 对象形式返回，结构如下： +{ + "logic_chain": { + "name": "从皮肤问题到牙齿健康的健康焦点演化", + "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。/think<|im_end|> +<|im_start|>assistant + +按时间顺序筛选关键交互，抽取兴趣变化和行为转化链路，最终只返回合法 JSON。 + +{"logic_chain":{"name":"从皮肤问题到牙齿健康的健康焦点演化","events":[{"date":"2025-03-27","action":"[商品-购买] <|prod_begin|>","logic":"问题触发：首次针对特定皮肤问题（“百癣”）购买内服药物，表明用户正面临较为明确的皮肤困扰。"},{"date":"2025-03-31","action":"[商品-购买] <|prod_begin|>","logic":"方案修正与问题细化：在购买内服药仅4天后，用户转而购买针对“皮炎湿疹”的外用“医用液体敷料”。这表明前一个方案可能效果不佳或认知发生变化，问题从泛化的“癣”聚焦到更具体的“皮炎湿疹”，且解决方案从内服转向外用，是典型的基于负反馈的路径修正。"},{"date":"2025-07-01","action":"[商品-点击] <|prod_begin|>","logic":"认知收敛与新焦点：在前期购买了泛泛的“固齿”草本片剂后，用户开始点击并关注具有“医用”和“脱敏”特定功能的牙膏。这表明用户对牙齿问题的认知已从笼统的“固齿”保健，升级到需要解决“牙齿脱敏”这一具体痛点，体现了从发散保健到问题聚焦的认知收敛。"}]}}<|im_end|> +<|im_start|>user +你擅长理解快手用户跨场景行为和语义ID表示，请根据输入信息归纳该用户的目标内容。 +下面给出用户历史行为线索： +用户在直播域: 关注了主播 <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>，首次打赏了 <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>，给主播 <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|>, <|living_begin|> 打赏过。 +用户购物行为: 浏览了商品 <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>，购买了 <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>, <|prod_begin|>。 +用户视频行为: 对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过评论/点赞/长播行为，深度观看了 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|> 有过评论/收藏/点赞/长播行为，对视频 <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|>, <|video_begin|> 有过评论/长播行为，对视频 <|video_begin|>, <|video_begin|> 有过收藏/长播行为。 +广告行为: 对广告 <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|> 完成过深度转化，点击过广告 <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>, <|ad_begin|>。 + +请基于这些线索总结该用户在各场景中的目标内容。/no_think<|im_end|> +<|im_start|>assistant +该用户最近喜欢的视频有: <|video_begin|><|im_end|> +<|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|> +label_ids: +[-100, 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18830, 25, 220, 176245, 152201, 162598, 170732, 151645, 198, -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][INFO|configuration_utils.py:769] 2026-07-13 03:28:39,554 >> loading configuration file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/config.json +[INFO|configuration_utils.py:847] 2026-07-13 03:28:39,556 >> 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 +} + + +labels: +{"logic_chain":{"name":"从泛化游戏兴趣到特定游戏周边的消费收敛","events":[{"date":"2025-08-25","action":"[直播-关注] <|living_begin|>","logic":"兴趣触发：首次明确关注以《无畏契约》为核心内容的游戏直播，表现出对该游戏的初步兴趣。"},{"date":"2025-10-19","action":"[商品-购买] <|prod_begin|>","logic":"兴趣物化：在持续关注游戏内容后，首次将虚拟兴趣转化为实体消费，购买了与游戏直接相关的周边商品“蝴蝶刀”，实现了从内容观赏到实物拥有的认知跃迁。"},{"date":"2026-01-25","action":"[商品-点击] <|prod_begin|>","logic":"兴趣深化与拓展：在购买了标志性武器模型后，用户的消费兴趣进一步拓展到更具日常实用性的游戏标志性周边（钥匙扣），显示出兴趣的持续和深化。"},{"date":"2026-02-03","action":"[商品-购买] <|prod_begin|>","logic":"闭环与身份认同：消费行为从实体周边回归到游戏内的虚拟服务（购买人气/点赞），表明用户不仅满足于拥有实体纪念品，还追求在游戏社区内的身份认同和虚拟价值提升，形成了“内容消费-实体消费-虚拟消费”的完整闭环。"}]}}<|im_end|> +{"logic_chain":{"name":"从健康焦虑到精准化饮食管理的认知演化","events":[{"date":"2026-02-01","action":"[视频-浏览] <|video_begin|>","logic":"问题触发：通过浏览甲状腺术后康复视频，表现出对该健康问题的初步关注。"},{"date":"2026-02-03","action":"[视频-评论/长播] <|video_begin|>","logic":"兴趣深化：从关注术后即时状态转向长期康复和生活方式管理，通过观看他人经验分享，寻求可行的实践路径。"},{"date":"2026-02-05","action":"[视频-长播] <|video_begin|>","logic":"认知收敛与增量：从泛化的生活方式管理，聚焦到具体的营养素（硒）和食疗方案，完成了从“问题是什么”到“具体怎么做”的认知闭环，获得了明确的行动指导。"}]}}<|im_end|> +["<|living_begin|>","<|living_begin|>","<|living_begin|>","<|living_begin|>","<|living_begin|>","<|living_begin|>","<|living_begin|>","<|living_begin|>","<|living_begin|>","<|living_begin|>","<|living_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|prod_begin|>","<|living_begin|>","<|prod_begin|>","<|living_begin|>","<|living_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>","<|video_begin|>"]<|im_end|> + +按时间顺序筛选关键交互，抽取兴趣变化和行为转化链路，最终只返回合法 JSON。 + +{"logic_chain":{"name":"从皮肤问题到牙齿健康的健康焦点演化","events":[{"date":"2025-03-27","action":"[商品-购买] <|prod_begin|>","logic":"问题触发：首次针对特定皮肤问题（“百癣”）购买内服药物，表明用户正面临较为明确的皮肤困扰。"},{"date":"2025-03-31","action":"[商品-购买] <|prod_begin|>","logic":"方案修正与问题细化：在购买内服药仅4天后，用户转而购买针对“皮炎湿疹”的外用“医用液体敷料”。这表明前一个方案可能效果不佳或认知发生变化，问题从泛化的“癣”聚焦到更具体的“皮炎湿疹”，且解决方案从内服转向外用，是典型的基于负反馈的路径修正。"},{"date":"2025-07-01","action":"[商品-点击] <|prod_begin|>","logic":"认知收敛与新焦点：在前期购买了泛泛的“固齿”草本片剂后，用户开始点击并关注具有“医用”和“脱敏”特定功能的牙膏。这表明用户对牙齿问题的认知已从笼统的“固齿”保健，升级到需要解决“牙齿脱敏”这一具体痛点，体现了从发散保健到问题聚焦的认知收敛。"}]}}<|im_end|> +该用户最近喜欢的视频有: <|video_begin|><|im_end|> + +[INFO|2026-07-13 03:28:39] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +[INFO|2026-07-13 03:28:39] llamafactory.model.model_utils.liger_kernel:144 >> Liger kernel has been applied to the model. +[INFO|modeling_utils.py:748] 2026-07-13 03:28:39,991 >> loading weights file /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018/model.safetensors +[INFO|modeling_utils.py:821] 2026-07-13 03:28:39,991 >> Will use dtype=torch.bfloat16 as defined in model's config object +[INFO|configuration_utils.py:1027] 2026-07-13 03:28:40,042 >> 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] 2026-07-13 03:28:40,687 >> 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] 2026-07-13 03:28:40,687 >> Could not locate the custom_generate/generate.py inside /home/ll/llm4rec/experiments/outputs/v29_v19_user_world_guard_lr8e7_ep018. +[INFO|2026-07-13 03:28:40] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-07-13 03:28:40] llamafactory.model.model_utils.attention:144 >> Using FlashAttention-2 for faster training and inference. +[INFO|2026-07-13 03:28:40] llamafactory.model.adapter:144 >> Pure bf16 / BAdam detected, remaining trainable params in half precision. +[INFO|2026-07-13 03:28:40] llamafactory.model.adapter:144 >> Fine-tuning method: Full +[INFO|2026-07-13 03:28:40] llamafactory.model.loader:144 >> trainable params: 801,433,600 || all params: 801,433,600 || trainable%: 100.0000 +[INFO|trainer.py:1469] 2026-07-13 03:28:40,825 >> ***** Running training ***** +[INFO|trainer.py:1470] 2026-07-13 03:28:40,825 >> Num examples = 685 +[INFO|trainer.py:1471] 2026-07-13 03:28:40,825 >> Num Epochs = 1 +[INFO|trainer.py:1472] 2026-07-13 03:28:40,825 >> Num update steps per epoch = 172 +[INFO|trainer.py:1473] 2026-07-13 03:28:40,825 >> Instantaneous batch size per device = 1 +[INFO|trainer.py:1476] 2026-07-13 03:28:40,825 >> Total train batch size (w. parallel, distributed & accumulation) = 4 +[INFO|trainer.py:1477] 2026-07-13 03:28:40,825 >> Gradient Accumulation steps = 4 +[INFO|trainer.py:1478] 2026-07-13 03:28:40,825 >> Total optimization steps = 61 +[INFO|trainer.py:1479] 2026-07-13 03:28:40,826 >> Number of trainable parameters = 801,433,600 + 0%| | 0/61 [00:00> `use_return_dict` is deprecated! 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Do not forget to share your model on huggingface.co/models =) + + + 100%|██████████| 61/61 [08:34<00:00, 8.23s/it] 100%|██████████| 61/61 [08:34<00:00, 8.43s/it] +[INFO|trainer.py:3815] 2026-07-13 03:37:15,304 >> Saving model checkpoint to /home/ll/llm4rec/experiments/outputs/v31_v29_live_specialist_r3_lr12e6_ep035 +[INFO|configuration_utils.py:533] 2026-07-13 03:37:15,305 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v31_v29_live_specialist_r3_lr12e6_ep035/config.json +[INFO|configuration_utils.py:816] 2026-07-13 03:37:15,305 >> Configuration saved in /home/ll/llm4rec/experiments/outputs/v31_v29_live_specialist_r3_lr12e6_ep035/generation_config.json +{'loss': '1.614', 'grad_norm': '9.75', 'learning_rate': '1.197e-06', 'epoch': '0.0292'} +{'loss': '1.302', 'grad_norm': '7.688', 'learning_rate': '1.159e-06', 'epoch': '0.05839'} +{'loss': '1.634', 'grad_norm': '8.562', 'learning_rate': '1.082e-06', 'epoch': '0.08759'} +{'loss': '1.509', 'grad_norm': '7.625', 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/home/ll/llm4rec/experiments/outputs/v31_v29_live_specialist_r3_lr12e6_ep035/model.safetensors +[INFO|tokenization_utils_base.py:3278] 2026-07-13 03:37:16,249 >> chat template saved in /home/ll/llm4rec/experiments/outputs/v31_v29_live_specialist_r3_lr12e6_ep035/chat_template.jinja +[INFO|tokenization_utils_base.py:2111] 2026-07-13 03:37:16,270 >> tokenizer config file saved in /home/ll/llm4rec/experiments/outputs/v31_v29_live_specialist_r3_lr12e6_ep035/tokenizer_config.json +***** train metrics ***** + epoch = 0.3562 + total_flos = 27343212GF + train_loss = 1.5557 + train_runtime = 0:08:34.47 + train_samples_per_second = 0.466 + train_steps_per_second = 0.119 +Figure saved at: /home/ll/llm4rec/experiments/outputs/v31_v29_live_specialist_r3_lr12e6_ep035/training_loss.png +[WARNING|2026-07-13 03:37:16] llamafactory.extras.ploting:149 >> No metric eval_loss to plot. +[WARNING|2026-07-13 03:37:16] llamafactory.extras.ploting:149 >> No metric eval_accuracy to plot. +[INFO|modelcard.py:263] 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