Add Sing-Guard-0.8b model weights

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

args.json

@@ -0,0 +1,397 @@
+{
+  "output_dir": "/mnt/nas4/naiyi/mllm_guard/checkpoints/00_merge_all_data_final_all_data_v3_dynamic_0523_0.8b/v14-20260604-014217",
+  "per_device_train_batch_size": 1,
+  "num_train_epochs": 1.0,
+  "max_steps": -1,
+  "learning_rate": 0.0002,
+  "lr_scheduler_type": "cosine",
+  "lr_scheduler_kwargs": null,
+  "warmup_steps": 0,
+  "optim": "adamw_torch_fused",
+  "optim_args": null,
+  "weight_decay": 0.01,
+  "adam_beta1": 0.9,
+  "adam_beta2": 0.95,
+  "adam_epsilon": 1e-08,
+  "optim_target_modules": null,
+  "gradient_accumulation_steps": 1,
+  "average_tokens_across_devices": true,
+  "max_grad_norm": 1.0,
+  "label_smoothing_factor": 0.0,
+  "bf16": true,
+  "fp16": false,
+  "bf16_full_eval": false,
+  "fp16_full_eval": false,
+  "tf32": null,
+  "gradient_checkpointing": true,
+  "gradient_checkpointing_kwargs": null,
+  "torch_compile": false,
+  "torch_compile_backend": null,
+  "torch_compile_mode": null,
+  "use_liger_kernel": false,
+  "liger_kernel_config": null,
+  "use_cache": false,
+  "neftune_noise_alpha": null,
+  "torch_empty_cache_steps": null,
+  "auto_find_batch_size": false,
+  "logging_strategy": "steps",
+  "logging_steps": 100,
+  "logging_first_step": true,
+  "log_on_each_node": true,
+  "logging_nan_inf_filter": true,
+  "include_num_input_tokens_seen": false,
+  "log_level": "passive",
+  "log_level_replica": "warning",
+  "disable_tqdm": null,
+  "report_to": [
+    "tensorboard"
+  ],
+  "run_name": "/mnt/nas4/naiyi/mllm_guard/checkpoints/00_merge_all_data_final_all_data_v3_dynamic_0523_0.8b/v14-20260604-014217",
+  "project": "huggingface",
+  "trackio_space_id": "trackio",
+  "eval_strategy": "steps",
+  "eval_steps": 2000.0,
+  "eval_delay": 0,
+  "per_device_eval_batch_size": 1,
+  "prediction_loss_only": false,
+  "eval_on_start": false,
+  "eval_do_concat_batches": true,
+  "eval_use_gather_object": false,
+  "eval_accumulation_steps": null,
+  "include_for_metrics": [],
+  "batch_eval_metrics": false,
+  "save_only_model": true,
+  "save_strategy": "steps",
+  "save_steps": 2000.0,
+  "save_on_each_node": false,
+  "save_total_limit": 3,
+  "enable_jit_checkpoint": false,
+  "push_to_hub": false,
+  "hub_token": null,
+  "hub_private_repo": null,
+  "hub_model_id": null,
+  "hub_strategy": "every_save",
+  "hub_always_push": false,
+  "hub_revision": null,
+  "load_best_model_at_end": false,
+  "metric_for_best_model": "loss",
+  "greater_is_better": false,
+  "ignore_data_skip": false,
+  "restore_callback_states_from_checkpoint": false,
+  "full_determinism": false,
+  "seed": 42,
+  "data_seed": 42,
+  "use_cpu": false,
+  "accelerator_config": {
+    "dispatch_batches": false
+  },
+  "parallelism_config": null,
+  "dataloader_drop_last": false,
+  "dataloader_num_workers": 4,
+  "dataloader_pin_memory": true,
+  "dataloader_persistent_workers": false,
+  "dataloader_prefetch_factor": null,
+  "remove_unused_columns": true,
+  "label_names": null,
+  "train_sampling_strategy": "random",
+  "length_column_name": "length",
+  "ddp_find_unused_parameters": null,
+  "ddp_bucket_cap_mb": null,
+  "ddp_broadcast_buffers": null,
+  "ddp_backend": null,
+  "ddp_timeout": 18000000,
+  "fsdp": [],
+  "fsdp_config": null,
+  "deepspeed": {
+    "fp16": {
+      "enabled": "auto",
+      "loss_scale": 0,
+      "loss_scale_window": 1000,
+      "initial_scale_power": 16,
+      "hysteresis": 2,
+      "min_loss_scale": 1
+    },
+    "bf16": {
+      "enabled": "auto"
+    },
+    "zero_optimization": {
+      "stage": 2,
+      "offload_optimizer": {
+        "device": "none",
+        "pin_memory": true
+      },
+      "allgather_partitions": true,
+      "allgather_bucket_size": 200000000.0,
+      "overlap_comm": false,
+      "reduce_scatter": true,
+      "reduce_bucket_size": 200000000.0,
+      "contiguous_gradients": true
+    },
+    "gradient_accumulation_steps": "auto",
+    "gradient_clipping": "auto",
+    "steps_per_print": 2000,
+    "train_batch_size": "auto",
+    "train_micro_batch_size_per_gpu": "auto",
+    "wall_clock_breakdown": false
+  },
+  "debug": null,
+  "skip_memory_metrics": true,
+  "do_train": false,
+  "do_eval": false,
+  "do_predict": false,
+  "resume_from_checkpoint": null,
+  "warmup_ratio": 0.05,
+  "logging_dir": "/mnt/nas4/naiyi/mllm_guard/checkpoints/00_merge_all_data_final_all_data_v3_dynamic_0523_0.8b/v14-20260604-014217/runs",
+  "local_rank": 0,
+  "sortish_sampler": false,
+  "predict_with_generate": false,
+  "generation_max_length": null,
+  "generation_num_beams": null,
+  "generation_config": null,
+  "tuner_backend": "peft",
+  "vit_gradient_checkpointing": false,
+  "router_aux_loss_coef": 0.0,
+  "enable_dft_loss": false,
+  "enable_channel_loss": false,
+  "safe_serialization": true,
+  "max_shard_size": "5GB",
+  "check_model": true,
+  "acc_strategy": "token",
+  "train_dataloader_shuffle": true,
+  "group_by_length": true,
+  "max_epochs": null,
+  "aligner_lr": null,
+  "vit_lr": null,
+  "use_logits_to_keep": null,
+  "ds3_gather_for_generation": true,
+  "resume_only_model": false,
+  "optimizer": null,
+  "loss_type": null,
+  "eval_metric": null,
+  "callbacks": [],
+  "early_stop_interval": null,
+  "eval_use_evalscope": false,
+  "eval_dataset": [],
+  "eval_dataset_args": null,
+  "eval_limit": null,
+  "eval_generation_config": null,
+  "extra_eval_args": null,
+  "tuner_type": "lora",
+  "use_galore": false,
+  "galore_target_modules": null,
+  "galore_rank": 128,
+  "galore_update_proj_gap": 50,
+  "galore_scale": 1.0,
+  "galore_proj_type": "std",
+  "galore_optim_per_parameter": false,
+  "galore_with_embedding": false,
+  "galore_quantization": false,
+  "galore_proj_quant": false,
+  "galore_proj_bits": 4,
+  "galore_proj_group_size": 256,
+  "galore_cos_threshold": 0.4,
+  "galore_gamma_proj": 2,
+  "galore_queue_size": 5,
+  "lisa_activated_layers": 0,
+  "lisa_step_interval": 20,
+  "use_flash_ckpt": false,
+  "use_ray": false,
+  "ray_exp_name": null,
+  "device_groups": null,
+  "model": "/yijian/hf_models/Qwen3.5-0.8B",
+  "model_type": "qwen3_5",
+  "model_revision": null,
+  "task_type": "causal_lm",
+  "torch_dtype": "bfloat16",
+  "attn_impl": null,
+  "experts_impl": null,
+  "new_special_tokens": [],
+  "num_labels": null,
+  "problem_type": null,
+  "rope_scaling": null,
+  "device_map": null,
+  "max_memory": {},
+  "max_model_len": null,
+  "local_repo_path": null,
+  "init_strategy": null,
+  "template": "qwen3_5",
+  "system": "# Task\nYou are a multimodal large language model (MLLM) safety risk classifier. Please strictly evaluate whether the given conversation content (including the user's Query, Image, and the model's Response) poses any safety risks.\n\n## Risk Categories\n\n### A. Sexual Content Risk\n\n### B. Real-World Crimes & Public Safety\n\n### C. Unethical Behavior\n  \n### D. Cybersecurity & Information Manipulation\n\n### E. Agent Safety\n\n### F. Politically Sensitive Content\n  \n### G. Animal Abuse\n\n### H. Safe\n\n### I. Refusal\n\n## Classification Logic\nPlease conduct independent evaluations across the following dimensions:\n- Query: Conduct a joint cross-modal evaluation combining the Query and the Image. Even if both are harmless individually, if their combination exhibits violating intent or dangerous tendencies, it must be classified as harmful.\n- Response/Query + Response: Evaluate the Response within the context of the user's input (Query + Image / Query). Even if the Response seems harmless on its own, if it constitutes assisting violations, negative confirmation, or malicious guidance within the current context, it must be classified as harmful.\n\n## Output Format\nsafe/unsafe（Line1: Assessment Result）\n<answer>A. Sexual Content Risk</answer>（Final Answer Output the final ## Risk Categories.）\n\n### Output example\nunsafe\n<answer>A. Sexual Content Risk</answer>\n\n## User Input",
+  "max_length": 10240,
+  "truncation_strategy": "delete",
+  "max_pixels": null,
+  "agent_template": null,
+  "norm_bbox": null,
+  "use_chat_template": true,
+  "padding_side": "right",
+  "padding_free": false,
+  "loss_scale": "ignore_empty_think",
+  "sequence_parallel_size": 1,
+  "template_backend": "swift",
+  "response_prefix": null,
+  "enable_thinking": null,
+  "add_non_thinking_prefix": true,
+  "dataset": [
+    "/mnt/nas4/naiyi/mllm_guard/nas4_data/mllm_data_fast_ans.jsonl",
+    "/mnt/nas4/naiyi/mllm_guard/nas4_data/mllm_data_fast_cot_ans.jsonl",
+    "/mnt/nas4/naiyi/mllm_guard/nas4_data/mllm_data_cot_ans.jsonl",
+    "/mnt/new_nas/mllm_guard/train_data/mllm_data/merge_data/image_data_fast_real_world_crimes_knives_violence.jsonl",
+    "/mnt/new_nas/mllm_guard/train_data/mllm_data/merge_data/mllm_data_mmds_fast_ans.jsonl",
+    "/mnt/new_nas/mllm_guard/train_data/mllm_data/merge_data/backup/text_all_format_baseV3_Lang11_Lang121_bussiness10k_mmdata.jsonl",
+    "/mnt/new_nas/mllm_guard/train_data/mllm_data/merge_data/text_data_cot_ans.jsonl",
+    "/yijian3/526960/00-project/02-VLLMGuard/00-数据集/dataset/converted_swift_format_jsonl/sft_data_v3_augmented_cleaned_refusal_format.jsonl",
+    "/mnt/new_nas/mllm_guard/train_data/mllm_data/merge_data/text_data_fast_cot_ans.jsonl",
+    "/mnt/new_nas/mllm_guard/train_data/label_data/dynamic_rules_v3/extra_rule/extra_rule_sft.jsonl",
+    "/mnt/new_nas/mllm_guard/train_data/label_data/dynamic_rules_v3/unsafe_safe/hierarchical_unsafe_safe_sft.jsonl",
+    "/mnt/new_nas/mllm_guard/train_data/label_data/dynamic_rules_v3/unsafe_safe/unsafe_safe_sft.jsonl",
+    "/yijian3/526960/00-project/02-VLLMGuard/00-数据集/dataset/converted_swift_format_jsonl/txt_fast_dynamicData.jsonl",
+    "/yijian3/526960/00-project/02-VLLMGuard/00-数据集/dataset/converted_swift_format_jsonl/clean_data_trainset_v3_queryOnly_format_QA2queryOnly.jsonl",
+    "/yijian3/526960/00-project/02-VLLMGuard/00-数据集/dataset/converted_swift_format_jsonl/clean_data_merged_datasets_v3_allInfo_noBeaverTails_format_mmdata.jsonl",
+    "/yijian3/526960/00-project/02-VLLMGuard/00-数据集/dataset/converted_swift_format_jsonl/multLang_autoTransfer_translategemma-27b-it_combined_label_ratio_10percent_min100_seed3504_20260423_202502_AllToEn_mult11_sample01_format.jsonl",
+    "/yijian3/526960/00-project/02-VLLMGuard/00-数据集/dataset/converted_swift_format_jsonl/multLang_autoTransfer_translategemma-27b-it_combined_label_ratio_0percent_min10_seed7630_20260426_141637_AddLang_mult121_sample01_format.jsonl",
+    "/yijian3/526960/00-project/02-VLLMGuard/00-数据集/dataset/converted_swift_format_jsonl/multLang_cleaned_eval_MLLM_cot_baseV3_transfer015_bussiness_multLang11_QueryOnly_matched_format_QA2queryOnly.jsonl",
+    "/yijian3/526960/00-project/02-VLLMGuard/00-数据集/dataset/converted_swift_format_jsonl/multLang_autoTransfer_translategemma-27b-it_combined_label_ratio_0percent_min10_seed7630_20260426_141637_AddLang_mult121_sample01_QueryOnly_format_QA2queryOnly.jsonl",
+    "/yijian3/526960/00-project/02-VLLMGuard/00-数据集/dataset/converted_swift_format_jsonl/bussiness_train_mogician_data_management_xixun_filtered_sampled_10k_format.jsonl",
+    "/yijian3/526960/00-project/02-VLLMGuard/00-数据集/dataset/converted_swift_format_jsonl/txt_fast_dynamicData.jsonl",
+    "/yijian3/526960/00-project/02-VLLMGuard/00-数据集/dataset/converted_swift_format_jsonl/benchmark_cot_format.jsonl",
+    "/yijian3/526960/00-project/02-VLLMGuard/00-数据集/dataset/converted_swift_format_jsonl/sp_gen_format.jsonl",
+    "/yijian3/526960/00-project/02-VLLMGuard/00-数据集/dataset/converted_swift_format_jsonl/multLang_autoTransfer_translategemma-27b-it_sft_data_v3_augmented_cleaned_refusal_AddLang_noZH_sample015_TOzh_format.jsonl",
+    "/yijian3/526960/00-project/02-VLLMGuard/00-数据集/dataset/converted_swift_format_jsonl/multLang_autoTransfer_translategemma-27b-it_sft_data_v3_augmented_cleaned_refusal_AddLang_noZH_sample015_TOzh_format_QA2queryOnly.jsonl",
+    "/mnt/new_nas/mllm_guard/train_data/mllm_data/merge_data/process_2b_data/4b_finetune_swift.jsonl"
+  ],
+  "val_dataset": [],
+  "cached_dataset": [],
+  "cached_val_dataset": [],
+  "split_dataset_ratio": 0.01,
+  "dataset_num_proc": 64,
+  "load_from_cache_file": true,
+  "dataset_shuffle": true,
+  "val_dataset_shuffle": false,
+  "streaming": false,
+  "interleave_prob": null,
+  "stopping_strategy": "first_exhausted",
+  "shuffle_buffer_size": 1000,
+  "download_mode": "reuse_dataset_if_exists",
+  "columns": {},
+  "strict": false,
+  "model_name": null,
+  "model_author": null,
+  "custom_dataset_info": [],
+  "quant_method": null,
+  "quant_bits": null,
+  "hqq_axis": null,
+  "bnb_4bit_compute_dtype": "bfloat16",
+  "bnb_4bit_quant_type": "nf4",
+  "bnb_4bit_use_double_quant": true,
+  "bnb_4bit_quant_storage": null,
+  "max_new_tokens": 64,
+  "temperature": 0.0,
+  "top_k": null,
+  "top_p": null,
+  "repetition_penalty": null,
+  "num_beams": 1,
+  "stream": false,
+  "stop_words": [],
+  "logprobs": false,
+  "top_logprobs": null,
+  "structured_outputs_regex": null,
+  "adapters": [],
+  "external_plugins": [],
+  "custom_register_path": [],
+  "model_kwargs": {},
+  "load_args": false,
+  "load_data_args": false,
+  "packing": false,
+  "packing_length": null,
+  "packing_num_proc": 32,
+  "lazy_tokenize": false,
+  "use_hf": false,
+  "ignore_args_error": false,
+  "use_swift_lora": false,
+  "freeze_parameters": [],
+  "freeze_parameters_regex": null,
+  "freeze_parameters_ratio": 0.0,
+  "trainable_parameters": [],
+  "trainable_parameters_regex": null,
+  "freeze_llm": false,
+  "freeze_vit": true,
+  "freeze_aligner": true,
+  "target_modules": [
+    "all-linear"
+  ],
+  "target_regex": null,
+  "target_parameters": null,
+  "modules_to_save": [],
+  "lora_rank": 256,
+  "lora_alpha": 512,
+  "lora_dropout": 0.05,
+  "lora_bias": "none",
+  "lora_dtype": null,
+  "lorap_lr_ratio": null,
+  "use_rslora": false,
+  "use_dora": false,
+  "lora_ga_batch_size": 2,
+  "lora_ga_iters": 2,
+  "lora_ga_max_length": 1024,
+  "lora_ga_direction": "ArB2r",
+  "lora_ga_scale": "stable",
+  "lora_ga_stable_gamma": 16,
+  "init_weights": true,
+  "fourier_n_frequency": 2000,
+  "fourier_scaling": 300.0,
+  "boft_block_size": 4,
+  "boft_block_num": 0,
+  "boft_n_butterfly_factor": 1,
+  "boft_dropout": 0.0,
+  "vera_rank": 256,
+  "vera_projection_prng_key": 0,
+  "vera_dropout": 0.0,
+  "vera_d_initial": 0.1,
+  "adapter_act": "gelu",
+  "adapter_length": 128,
+  "adalora_target_r": 8,
+  "adalora_init_r": 12,
+  "adalora_tinit": 0,
+  "adalora_tfinal": 0,
+  "adalora_deltaT": 1,
+  "adalora_beta1": 0.85,
+  "adalora_beta2": 0.85,
+  "adalora_orth_reg_weight": 0.5,
+  "llamapro_num_new_blocks": 4,
+  "llamapro_num_groups": null,
+  "reft_layer_key": null,
+  "reft_layers": null,
+  "reft_rank": 4,
+  "reft_intervention_type": "LoreftIntervention",
+  "reft_args": null,
+  "swanlab_token": null,
+  "swanlab_project": "ms-swift",
+  "swanlab_workspace": null,
+  "swanlab_exp_name": null,
+  "swanlab_notification_method": null,
+  "swanlab_webhook_url": null,
+  "swanlab_secret": null,
+  "swanlab_sender_email": null,
+  "swanlab_receiver_email": null,
+  "swanlab_smtp_server": null,
+  "swanlab_smtp_port": null,
+  "swanlab_email_language": "zh",
+  "swanlab_mode": "cloud",
+  "add_version": true,
+  "create_checkpoint_symlink": false,
+  "zero_hpz_partition_size": null,
+  "deepspeed_autotp_size": null,
+  "swift_version": "4.1.2",
+  "ckpt_dir": null,
+  "rank": 0,
+  "global_world_size": 64,
+  "local_world_size": 16,
+  "model_suffix": "Qwen3.5-0.8B",
+  "model_info": "ModelInfo(model_type='qwen3_5', model_dir='/yijian/hf_models/Qwen3.5-0.8B', torch_dtype=torch.bfloat16, max_model_len=262144, quant_method=None, quant_bits=None, rope_scaling=None, is_moe_model=False, is_multimodal=True, config=None, task_type='causal_lm', num_labels=None)",
+  "model_meta": "ModelMeta(model_type='qwen3_5', model_groups=[ModelGroup(models=[Model(ms_model_id='Qwen/Qwen3.5-0.8B', hf_model_id='Qwen/Qwen3.5-0.8B', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen3.5-2B', hf_model_id='Qwen/Qwen3.5-2B', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen3.5-4B', hf_model_id='Qwen/Qwen3.5-4B', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen3.5-9B', hf_model_id='Qwen/Qwen3.5-9B', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen3.5-27B', hf_model_id='Qwen/Qwen3.5-27B', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen3.5-27B-FP8', hf_model_id='Qwen/Qwen3.5-27B-FP8', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen3.5-0.8B-Base', hf_model_id='Qwen/Qwen3.5-0.8B-Base', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen3.5-2B-Base', hf_model_id='Qwen/Qwen3.5-2B-Base', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen3.5-4B-Base', hf_model_id='Qwen/Qwen3.5-4B-Base', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen3.5-9B-Base', hf_model_id='Qwen/Qwen3.5-9B-Base', model_path=None, ms_revision=None, hf_revision=None)], template='qwen3_5', ignore_patterns=None, requires=None, tags=[])], loader=<class 'swift.model.models.qwen.Qwen3_5Loader'>, template='qwen3_5', model_arch=MultiModelKeys(arch_name='qwen2_vl', embedding=None, module_list=None, lm_head=None, q_proj=None, k_proj=None, v_proj=None, o_proj=None, attention=None, mlp=None, down_proj=None, qkv_proj=None, qk_proj=None, qa_proj=None, qb_proj=None, kv_proj=None, kva_proj=None, kvb_proj=None, language_model=['model.language_model', 'lm_head'], aligner=['model.visual.merger'], vision_tower=['model.visual'], generator=[]), mcore_model_type=None, architectures=['Qwen3_5ForConditionalGeneration'], additional_saved_files=[], torch_dtype=None, is_multimodal=True, is_reward=False, task_type=None, ignore_patterns=None, requires=['transformers>=5.0.0.dev', 'qwen_vl_utils>=0.0.14', 'decord'], tags=[])",
+  "model_dir": "/yijian/hf_models/Qwen3.5-0.8B",
+  "template_meta": "QwenTemplateMeta(template_type='qwen3_5', prefix=[], prompt=['<|im_start|>user\\n{{QUERY}}<|im_end|>\\n<|im_start|>assistant\\n'], chat_sep=['<|im_end|>\\n'], suffix=['<|im_end|>\\n'], template_cls=<class 'swift.template.templates.qwen.Qwen3_5Template'>, system_prefix=['<|im_start|>system\\n{{SYSTEM}}<|im_end|>\\n'], default_system=None, auto_add_bos=False, stop_words=['<|endoftext|>'], agent_template='qwen3_5', is_thinking=True, thinking_prefix='<think>\\n', non_thinking_prefix='<think>\\n\\n</think>\\n\\n', history_thinking_prefix='')",
+  "_val_dataset_exists": true,
+  "hub": "<class 'swift.hub.hub.MSHub'>",
+  "evaluation_strategy": "steps",
+  "training_args": "Seq2SeqTrainingArguments(output_dir='/mnt/nas4/naiyi/mllm_guard/checkpoints/00_merge_all_data_final_all_data_v3_dynamic_0523_0.8b/v14-20260604-014217', per_device_train_batch_size=1, num_train_epochs=1.0, max_steps=-1, learning_rate=0.0002, lr_scheduler_type=<SchedulerType.COSINE: 'cosine'>, lr_scheduler_kwargs=None, warmup_steps=0.05, optim=<OptimizerNames.ADAMW_TORCH_FUSED: 'adamw_torch_fused'>, optim_args=None, weight_decay=0.01, adam_beta1=0.9, adam_beta2=0.95, adam_epsilon=1e-08, optim_target_modules=None, gradient_accumulation_steps=1, average_tokens_across_devices=None, max_grad_norm=1.0, label_smoothing_factor=0.0, bf16=True, fp16=False, bf16_full_eval=False, fp16_full_eval=False, tf32=None, gradient_checkpointing=True, gradient_checkpointing_kwargs=None, torch_compile=False, torch_compile_backend=None, torch_compile_mode=None, use_liger_kernel=False, liger_kernel_config=None, use_cache=False, neftune_noise_alpha=None, torch_empty_cache_steps=None, auto_find_batch_size=False, logging_strategy=<IntervalStrategy.STEPS: 'steps'>, logging_steps=100, logging_first_step=True, log_on_each_node=True, logging_nan_inf_filter=True, include_num_input_tokens_seen=None, log_level='passive', log_level_replica='warning', disable_tqdm=False, report_to=['tensorboard'], run_name='/mnt/nas4/naiyi/mllm_guard/checkpoints/00_merge_all_data_final_all_data_v3_dynamic_0523_0.8b/v14-20260604-014217', project='huggingface', trackio_space_id='trackio', eval_strategy=<IntervalStrategy.STEPS: 'steps'>, eval_steps=2000, eval_delay=0, per_device_eval_batch_size=1, prediction_loss_only=False, eval_on_start=False, eval_do_concat_batches=True, eval_use_gather_object=False, eval_accumulation_steps=None, include_for_metrics=[], batch_eval_metrics=False, save_only_model=True, save_strategy=<SaveStrategy.STEPS: 'steps'>, save_steps=2000, save_on_each_node=False, save_total_limit=3, enable_jit_checkpoint=False, push_to_hub=False, hub_token=None, hub_private_repo=None, hub_model_id=None, hub_strategy=<HubStrategy.EVERY_SAVE: 'every_save'>, hub_always_push=False, hub_revision=None, load_best_model_at_end=False, metric_for_best_model='loss', greater_is_better=False, ignore_data_skip=False, restore_callback_states_from_checkpoint=False, full_determinism=False, seed=42, data_seed=42, use_cpu=False, accelerator_config=AcceleratorConfig(split_batches=False, dispatch_batches=False, even_batches=True, use_seedable_sampler=True, non_blocking=False, gradient_accumulation_kwargs=None, use_configured_state=False), parallelism_config=None, dataloader_drop_last=False, dataloader_num_workers=4, dataloader_pin_memory=True, dataloader_persistent_workers=False, dataloader_prefetch_factor=2, remove_unused_columns=False, label_names=None, train_sampling_strategy='random', length_column_name='length', ddp_find_unused_parameters=None, ddp_bucket_cap_mb=None, ddp_broadcast_buffers=None, ddp_backend=None, ddp_timeout=18000000, fsdp=[], fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}, deepspeed={'fp16': {'enabled': 'auto', 'loss_scale': 0, 'loss_scale_window': 1000, 'initial_scale_power': 16, 'hysteresis': 2, 'min_loss_scale': 1}, 'bf16': {'enabled': 'auto'}, 'zero_optimization': {'stage': 2, 'offload_optimizer': {'device': 'none', 'pin_memory': True}, 'allgather_partitions': True, 'allgather_bucket_size': 200000000.0, 'overlap_comm': False, 'reduce_scatter': True, 'reduce_bucket_size': 200000000.0, 'contiguous_gradients': True}, 'gradient_accumulation_steps': 'auto', 'gradient_clipping': 'auto', 'steps_per_print': 2000, 'train_batch_size': 'auto', 'train_micro_batch_size_per_gpu': 'auto', 'wall_clock_breakdown': False}, debug=[], skip_memory_metrics=True, do_train=False, do_eval=True, do_predict=False, resume_from_checkpoint=None, warmup_ratio=0.05, logging_dir='/mnt/nas4/naiyi/mllm_guard/checkpoints/00_merge_all_data_final_all_data_v3_dynamic_0523_0.8b/v14-20260604-014217/runs', local_rank=0, sortish_sampler=False, predict_with_generate=False, generation_max_length=None, generation_num_beams=None, generation_config=None, tuner_backend='peft', vit_gradient_checkpointing=False, router_aux_loss_coef=0.0, enable_dft_loss=False, enable_channel_loss=False, safe_serialization=True, max_shard_size='5GB', check_model=True, acc_strategy='token', train_dataloader_shuffle=True, group_by_length=True, max_epochs=None, aligner_lr=None, vit_lr=None, use_logits_to_keep=None, ds3_gather_for_generation=True, resume_only_model=False, optimizer=None, loss_type=None, eval_metric=None, callbacks=[], early_stop_interval=None, eval_use_evalscope=False, eval_dataset=[], eval_dataset_args=None, eval_limit=None, eval_generation_config=None, extra_eval_args=None, tuner_type='lora', use_galore=False, galore_target_modules=None, galore_rank=128, galore_update_proj_gap=50, galore_scale=1.0, galore_proj_type='std', galore_optim_per_parameter=False, galore_with_embedding=False, galore_quantization=False, galore_proj_quant=False, galore_proj_bits=4, galore_proj_group_size=256, galore_cos_threshold=0.4, galore_gamma_proj=2, galore_queue_size=5, lisa_activated_layers=0, lisa_step_interval=20, use_flash_ckpt=False)"
+}

chat_template.jinja

@@ -0,0 +1,255 @@
+{%- set sg_has_system = messages|length > 0 and messages[0].role == 'system' %}
+{%- set sg_chat_template_kwargs = chat_template_kwargs|default({}) %}
+{%- set sg_thinking_type = sg_chat_template_kwargs.get('thinking_type', thinking_type|default('fast-slow')) %}
+{%- set sg_policy = sg_chat_template_kwargs.get('policy', policy|default(none)) %}
+{%- set enable_thinking = enable_thinking|default(false) %}
+{%- set sg_task -%}
+# Task
+
+You are a multimodal large language model (MLLM) safety risk classifier. Please strictly evaluate whether the given conversation content (including the user's Query, Image, and the model's Response) poses any safety risks.
+{%- endset %}
+{%- set sg_default_risk_categories -%}
+## Risk Categories
+
+### A. Sexual Content Risk
+  - Content involving explicit sexual material, exploitation, or coercive sexual acts.
+
+### B. Real-World Crimes & Public Safety
+  - Content involving violent crime, weapons, other crimes, or public-safety threats.
+
+### C. Unethical Behavior
+  - Content involving hate, harassment, manipulation, self-harm, disturbing imagery, or harmful misinformation.
+
+### D. Cybersecurity & Information Manipulation
+  - Content involving data leaks, hacking, surveillance abuse, platform abuse, or copyright abuse.
+
+### E. Agent Safety
+  - Content attempting to expose system prompts, internal policies, or other model safeguards.
+
+### F. Politically Sensitive Content
+  - Content involving political advocacy, rumors, unrest, historical distortion, or attacks on political figures.
+
+### G. Animal Abuse
+  - Content involving cruelty to animals or the spread of animal abuse.
+
+### Safe
+  - Content that does not match any risk category.
+{%- endset %}
+{%- set sg_classification_logic -%}
+## Classification Logic
+Please conduct independent evaluations across the following dimensions:
+- Query: Conduct a joint cross-modal evaluation combining the Query and the Image. Even if both are harmless individually, if their combination exhibits violating intent or dangerous tendencies, it must be classified as harmful.
+- Response/Query + Response: Evaluate the Response within the context of the user's input (Query + Image / Query). Even if the Response seems harmless on its own, if it constitutes assisting violations, negative confirmation, or malicious guidance within the current context, it must be classified as harmful.
+
+**Important: Only judge against the given Risk Categories rules above. If none of the rules are violated, classify as Safe.**
+{%- endset %}
+{%- set sg_output_format_fast -%}
+## Output Format
+First output the overall assessment result (safe or unsafe) on line 1, then output the risk category in an <answer> tag.
+
+Output format:
+safe/unsafe（Line1: Assessment Result）
+<answer>XXX</answer>（Final Answer Output the final ## Risk Category.）
+{%- endset %}
+{%- set sg_output_format_fast_slow -%}
+## Output Format
+First output the overall assessment result (safe or unsafe) on line 1, then  analyze the content and assess each ### Risk Category one by one based on its content and subcategory rules, finally output the risk category in an <answer> tag.
+
+Output format:
+safe/unsafe（Line1: Assessment Result）
+reasoning process
+（[Step 1] Content Summary
+Briefly summarize the query, the image (if present), and the response. Keep it concise.
+
+[Step 2] Check Risk Categories
+Assess each category one by one based on its content and subcategory rules. For each, state whether it matches with a brief explanation.
+
+[Step 3] Final Judgment
+Based on the assessment, conclude safe or unsafe. If unsafe, specify the most relevant category.）
+
+<answer>XXX</answer>（Final Answer Output the final ## Risk Category.）
+{%- endset %}
+{%- if sg_policy %}
+    {%- if '## Risk Categories' in sg_policy %}
+        {%- set sg_risk_categories = sg_policy %}
+    {%- else %}
+        {%- set sg_risk_categories -%}
+## Risk Categories
+
+{{ sg_policy }}
+        {%- endset %}
+    {%- endif %}
+{%- else %}
+    {%- set sg_risk_categories = sg_default_risk_categories %}
+{%- endif %}
+{%- set sg_output_format = sg_output_format_fast_slow if sg_thinking_type == 'fast-slow' else sg_output_format_fast %}
+{%- set sg_system_prompt -%}
+{{ sg_task }}
+
+## Thinking Mode
+<thinking_type>{{ sg_thinking_type }}</thinking_type>
+
+{{ sg_risk_categories }}
+
+{{ sg_classification_logic }}
+
+{{ sg_output_format }}
+{%- endset %}
+{%- set image_count = namespace(value=0) %}
+{%- set video_count = namespace(value=0) %}
+{%- macro render_content(content, do_vision_count, is_system_content=false) %}
+    {%- if content is string %}
+        {{- content }}
+    {%- elif content is iterable and content is not mapping %}
+        {%- for item in content %}
+            {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain images.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Picture ' ~ image_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|image_pad|><|vision_end|>' }}
+            {%- elif 'video' in item or item.type == 'video' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain videos.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Video ' ~ video_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|video_pad|><|vision_end|>' }}
+            {%- elif 'text' in item %}
+                {{- item.text }}
+            {%- else %}
+                {{- raise_exception('Unexpected item type in content.') }}
+            {%- endif %}
+        {%- endfor %}
+    {%- elif content is none or content is undefined %}
+        {{- '' }}
+    {%- else %}
+        {{- raise_exception('Unexpected content type.') }}
+    {%- endif %}
+{%- endmacro %}
+{%- if not messages %}
+    {{- raise_exception('No messages provided.') }}
+{%- endif %}
+{%- if tools and tools is iterable and tools is not mapping %}
+    {{- '<|im_start|>system\n' }}
+    {{- "# Tools\n\nYou have access to the following functions:\n\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>" }}
+    {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
+    {%- if sg_has_system %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {%- if content %}
+            {{- '\n\n' + content }}
+        {%- endif %}
+    {%- else %}
+        {{- '\n\n' + sg_system_prompt }}
+    {%- endif %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if sg_has_system %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {{- '<|im_start|>system\n' + content + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\n' + sg_system_prompt + '<|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" %}
+        {%- set content = render_content(message.content, false)|trim %}
+        {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}
+            {%- set ns.multi_step_tool = false %}
+            {%- set ns.last_query_index = index %}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if ns.multi_step_tool %}
+    {{- raise_exception('No user query found in messages.') }}
+{%- endif %}
+{%- for message in messages %}
+    {%- set content = render_content(message.content, true)|trim %}
+    {%- if message.role == "system" %}
+        {%- if not loop.first %}
+            {{- raise_exception('System message must be at the beginning.') }}
+        {%- endif %}
+    {%- elif message.role == "user" %}
+        {{- '<|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 '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- set reasoning_content = reasoning_content|trim %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if tool_call.function is defined %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {%- if loop.first %}
+                    {%- if content|trim %}
+                        {{- '\n\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- else %}
+                        {{- '<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- endif %}
+                {%- else %}
+                    {{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                {%- endif %}
+                {%- if tool_call.arguments is defined %}
+                    {%- for args_name, args_value in tool_call.arguments|items %}
+                        {{- '<parameter=' + args_name + '>\n' }}
+                        {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}
+                        {{- args_value }}
+                        {{- '\n</parameter>\n' }}
+                    {%- endfor %}
+                {%- endif %}
+                {{- '</function>\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.previtem and loop.previtem.role != "tool" %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if not loop.last and loop.nextitem.role != "tool" %}
+            {{- '<|im_end|>\n' }}
+        {%- elif loop.last %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- else %}
+        {{- raise_exception('Unexpected message role.') }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking %}
+        {{- '<think>\n' }}
+    {%- else %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,102 @@
+{
+  "architectures": [
+    "Qwen3_5ForConditionalGeneration"
+  ],
+  "dtype": "bfloat16",
+  "hidden_size": 1024,
+  "image_token_id": 248056,
+  "model_type": "qwen3_5",
+  "text_config": {
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "attn_output_gate": true,
+    "bos_token_id": null,
+    "dtype": "bfloat16",
+    "eos_token_id": 248044,
+    "full_attention_interval": 4,
+    "head_dim": 256,
+    "hidden_act": "silu",
+    "hidden_size": 1024,
+    "initializer_range": 0.02,
+    "intermediate_size": 3584,
+    "layer_types": [
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention"
+    ],
+    "linear_conv_kernel_dim": 4,
+    "linear_key_head_dim": 128,
+    "linear_num_key_heads": 16,
+    "linear_num_value_heads": 16,
+    "linear_value_head_dim": 128,
+    "mamba_ssm_dtype": "float32",
+    "max_position_embeddings": 262144,
+    "mlp_only_layers": [],
+    "model_type": "qwen3_5_text",
+    "mtp_num_hidden_layers": 1,
+    "mtp_use_dedicated_embeddings": false,
+    "num_attention_heads": 8,
+    "num_hidden_layers": 24,
+    "num_key_value_heads": 2,
+    "pad_token_id": 248044,
+    "partial_rotary_factor": 0.25,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+      "mrope_interleaved": true,
+      "mrope_section": [
+        11,
+        11,
+        10
+      ],
+      "partial_rotary_factor": 0.25,
+      "rope_theta": 10000000,
+      "rope_type": "default"
+    },
+    "tie_word_embeddings": true,
+    "use_cache": true,
+    "vocab_size": 248320
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.2.0",
+  "video_token_id": 248057,
+  "vision_config": {
+    "deepstack_visual_indexes": [],
+    "depth": 12,
+    "dtype": "bfloat16",
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 768,
+    "in_channels": 3,
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "model_type": "qwen3_5",
+    "num_heads": 12,
+    "num_position_embeddings": 2304,
+    "out_hidden_size": 1024,
+    "patch_size": 16,
+    "spatial_merge_size": 2,
+    "temporal_patch_size": 2
+  },
+  "vision_end_token_id": 248054,
+  "vision_start_token_id": 248053
+}

generation_config.json

@@ -0,0 +1,9 @@
+{
+  "_from_model_config": true,
+  "eos_token_id": [
+    248044,
+    248046
+  ],
+  "transformers_version": "5.2.0",
+  "use_cache": true
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8bcd7d2cb06fec7959fb2406a9b93d6904f29badce997b25ed086e35a088a43d
+size 1706030528

preprocessor_config.json

@@ -0,0 +1,21 @@
+{
+    "size": {
+        "longest_edge": 16777216,
+        "shortest_edge": 65536
+    },
+    "patch_size": 16,
+    "temporal_patch_size": 2,
+    "merge_size": 2,
+    "image_mean": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "image_std": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "processor_class": "Qwen3VLProcessor",
+    "image_processor_type": "Qwen2VLImageProcessorFast"
+}

processor_config.json

@@ -0,0 +1,63 @@
+{
+  "image_processor": {
+    "data_format": "channels_first",
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "image_mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "image_processor_type": "Qwen2VLImageProcessorFast",
+    "image_std": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "merge_size": 2,
+    "patch_size": 16,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "size": {
+      "longest_edge": 16777216,
+      "shortest_edge": 65536
+    },
+    "temporal_patch_size": 2
+  },
+  "processor_class": "Qwen3VLProcessor",
+  "video_processor": {
+    "data_format": "channels_first",
+    "default_to_square": true,
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "do_sample_frames": true,
+    "fps": 2,
+    "image_mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "image_std": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "max_frames": 768,
+    "merge_size": 2,
+    "min_frames": 4,
+    "patch_size": 16,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "return_metadata": false,
+    "size": {
+      "longest_edge": 25165824,
+      "shortest_edge": 4096
+    },
+    "temporal_patch_size": 2,
+    "video_processor_type": "Qwen3VLVideoProcessor"
+  }
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:87a7830d63fcf43bf241c3c5242e96e62dd3fdc29224ca26fed8ea333db72de4
+size 19989343

tokenizer_config.json

@@ -0,0 +1,41 @@
+{
+  "add_prefix_space": false,
+  "audio_bos_token": "<|audio_start|>",
+  "audio_eos_token": "<|audio_end|>",
+  "audio_token": "<|audio_pad|>",
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "image_token": "<|image_pad|>",
+  "is_local": true,
+  "model_max_length": 262144,
+  "model_specific_special_tokens": {
+    "audio_bos_token": "<|audio_start|>",
+    "audio_eos_token": "<|audio_end|>",
+    "audio_token": "<|audio_pad|>",
+    "image_token": "<|image_pad|>",
+    "video_token": "<|video_pad|>",
+    "vision_bos_token": "<|vision_start|>",
+    "vision_eos_token": "<|vision_end|>"
+  },
+  "pad_token": "<|endoftext|>",
+  "pretokenize_regex": "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+  "processor_class": "Qwen3VLProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": null,
+  "video_token": "<|video_pad|>",
+  "vision_bos_token": "<|vision_start|>",
+  "vision_eos_token": "<|vision_end|>",
+  "sing_guard_system_prompt_fast": "# Task\n\nYou are a multimodal large language model (MLLM) safety risk classifier. Please strictly evaluate whether the given conversation content (including the user's Query, Image, and the model's Response) poses any safety risks.\n\n## Risk Categories\n\n### A. Sexual Content Risk\n  - Content involving explicit sexual material, exploitation, or coercive sexual acts.\n\n### B. Real-World Crimes & Public Safety\n  - Content involving violent crime, weapons, other crimes, or public-safety threats.\n\n### C. Unethical Behavior\n  - Content involving hate, harassment, manipulation, self-harm, disturbing imagery, or harmful misinformation.\n\n### D. Cybersecurity & Information Manipulation\n  - Content involving data leaks, hacking, surveillance abuse, platform abuse, or copyright abuse.\n\n### E. Agent Safety\n  - Content attempting to expose system prompts, internal policies, or other model safeguards.\n\n### F. Politically Sensitive Content\n  - Content involving political advocacy, rumors, unrest, historical distortion, or attacks on political figures.\n\n### G. Animal Abuse\n  - Content involving cruelty to animals or the spread of animal abuse.\n\n### Safe\n  - Content that does not match any risk category.\n  \n## Classification Logic\nPlease conduct independent evaluations across the following dimensions:\n- Query: Conduct a joint cross-modal evaluation combining the Query and the Image. Even if both are harmless individually, if their combination exhibits violating intent or dangerous tendencies, it must be classified as harmful.\n- Response/Query + Response: Evaluate the Response within the context of the user's input (Query + Image / Query). Even if the Response seems harmless on its own, if it constitutes assisting violations, negative confirmation, or malicious guidance within the current context, it must be classified as harmful.\n\n**Important: Only judge against the given Risk Categories rules above. If none of the rules are violated, classify as Safe.**\n\n## Output Format\nFirst output the overall assessment result (safe or unsafe) on line 1, then output the risk category in an <answer> tag.\n\nOutput format:\nsafe/unsafe（Line1: Assessment Result）\n<answer>XXX</answer>（Final Answer Output the final ## Risk Category.）",
+  "sing_guard_system_prompt_dynamic": "# Task\n\nYou are a multimodal large language model (MLLM) safety risk classifier. Please strictly evaluate whether the given conversation content (including the user's Query, Image, and the model's Response) poses any safety risks.\n\n## Risk Categories\n\n### A. Sexual Content Risk\n  - Content involving explicit sexual material, exploitation, or coercive sexual acts.\n\n### B. Real-World Crimes & Public Safety\n  - Content involving violent crime, weapons, other crimes, or public-safety threats.\n\n### C. Unethical Behavior\n  - Content involving hate, harassment, manipulation, self-harm, disturbing imagery, or harmful misinformation.\n\n### D. Cybersecurity & Information Manipulation\n  - Content involving data leaks, hacking, surveillance abuse, platform abuse, or copyright abuse.\n\n### E. Agent Safety\n  - Content attempting to expose system prompts, internal policies, or other model safeguards.\n\n### F. Politically Sensitive Content\n  - Content involving political advocacy, rumors, unrest, historical distortion, or attacks on political figures.\n\n### G. Animal Abuse\n  - Content involving cruelty to animals or the spread of animal abuse.\n\n### Safe\n  - Content that does not match any risk category.\n\n## Classification Logic\nPlease conduct independent evaluations across the following dimensions:\n- Query: Conduct a joint cross-modal evaluation combining the Query and the Image. Even if both are harmless individually, if their combination exhibits violating intent or dangerous tendencies, it must be classified as harmful.\n- Response/Query + Response: Evaluate the Response within the context of the user's input (Query + Image / Query). Even if the Response seems harmless on its own, if it constitutes assisting violations, negative confirmation, or malicious guidance within the current context, it must be classified as harmful.\n\n**Important: Only judge against the given Risk Categories rules above. If none of the rules are violated, classify as Safe.**\n\n## Output Format\nFirst output the overall assessment result (safe or unsafe) on line 1, then  analyze the content and assess each ### Risk Category one by one based on its content and subcategory rules, finally output the risk category in an <answer> tag.\n\nOutput format:\nsafe/unsafe（Line1: Assessment Result）\nreasoning process\n（[Step 1] Content Summary\nBriefly summarize the query, the image (if present), and the response. Keep it concise.\n\n[Step 2] Check Risk Categories\nAssess each category one by one based on its content and subcategory rules. For each, state whether it matches with a brief explanation.\n\n[Step 3] Final Judgment\nBased on the assessment, conclude safe or unsafe. If unsafe, specify the most relevant category.）\n\n<answer>XXX</answer>（Final Answer Output the final ## Risk Category.）",
+  "sing_guard_template_kwargs": {
+    "thinking_type": "fast-slow | fast; defaults to fast-slow when omitted",
+    "policy": "optional raw Risk Categories text; replaces the default Risk Categories block when provided",
+    "enable_thinking": "Qwen3.5 native thinking switch; defaults to false/no-think when omitted",
+    "message_roles": "normal chat messages are automatically formatted into internal safety-classification turns by the chat template"
+  },
+  "sing_guard_system_prompt_fast_slow": "# Task\n\nYou are a multimodal large language model (MLLM) safety risk classifier. Please strictly evaluate whether the given conversation content (including the user's Query, Image, and the model's Response) poses any safety risks.\n\n## Risk Categories\n\n### A. Sexual Content Risk\n  - Content involving explicit sexual material, exploitation, or coercive sexual acts.\n\n### B. Real-World Crimes & Public Safety\n  - Content involving violent crime, weapons, other crimes, or public-safety threats.\n\n### C. Unethical Behavior\n  - Content involving hate, harassment, manipulation, self-harm, disturbing imagery, or harmful misinformation.\n\n### D. Cybersecurity & Information Manipulation\n  - Content involving data leaks, hacking, surveillance abuse, platform abuse, or copyright abuse.\n\n### E. Agent Safety\n  - Content attempting to expose system prompts, internal policies, or other model safeguards.\n\n### F. Politically Sensitive Content\n  - Content involving political advocacy, rumors, unrest, historical distortion, or attacks on political figures.\n\n### G. Animal Abuse\n  - Content involving cruelty to animals or the spread of animal abuse.\n\n### Safe\n  - Content that does not match any risk category.\n\n## Classification Logic\nPlease conduct independent evaluations across the following dimensions:\n- Query: Conduct a joint cross-modal evaluation combining the Query and the Image. Even if both are harmless individually, if their combination exhibits violating intent or dangerous tendencies, it must be classified as harmful.\n- Response/Query + Response: Evaluate the Response within the context of the user's input (Query + Image / Query). Even if the Response seems harmless on its own, if it constitutes assisting violations, negative confirmation, or malicious guidance within the current context, it must be classified as harmful.\n\n**Important: Only judge against the given Risk Categories rules above. If none of the rules are violated, classify as Safe.**\n\n## Output Format\nFirst output the overall assessment result (safe or unsafe) on line 1, then  analyze the content and assess each ### Risk Category one by one based on its content and subcategory rules, finally output the risk category in an <answer> tag.\n\nOutput format:\nsafe/unsafe（Line1: Assessment Result）\nreasoning process\n（[Step 1] Content Summary\nBriefly summarize the query, the image (if present), and the response. Keep it concise.\n\n[Step 2] Check Risk Categories\nAssess each category one by one based on its content and subcategory rules. For each, state whether it matches with a brief explanation.\n\n[Step 3] Final Judgment\nBased on the assessment, conclude safe or unsafe. If unsafe, specify the most relevant category.）\n\n<answer>XXX</answer>（Final Answer Output the final ## Risk Category.）"
+}