“config.yalm” and “config_planning.yalm” files are missing.

[Tangyueming311]

“config.yalm” and “config_planning.yalm” files are missing.

[AM97Y]

No YAML content found.

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Model -

if "Qwen" in model_name:
    llm = LLM(model=model_name, 
            tensor_parallel_size=tp_size, 
            max_model_len=max_model_len,
            gpu_memory_utilization=0.8,
            trust_remote_code=True, enforce_eager=True, 
            rope_scaling={"factor": 4.0, "original_max_position_embeddings": 20000, "rope_type": "yarn"})
    sampling_params = SamplingParams(temperature=temperature, max_tokens=max_model_len)

yaml -

## config.yaml
### Detect symbolic events (e.g., diabetic-to-normal blood sugar conversion events)

Dataset instances: Types of diabetic-to-normal blood sugar conversion events are:

  1. `Diabetic_to_Normal`: Contains a single diabetic action episode with 10 representative frames, spans a duration of 36 frames, organized as fixed intervals at a constant frequency.
  2. `Diabetic_to_Normal_Selected`: Same as `Diabetic_to_Normal`, but contains a set of at least 3 gallstone-related periodic actions with 100 frames each, spanning 1-36 frames (with highly volatile and discontinuous implementations) while maintaining temporal context similarity across identical epochs.
  
#### Trainer class
### 外部 API: Meta Trainer interface

```python
class TrainerMeta(DFM):
    def __init__(self, model, dwkit, dataset_loader, values, private_segmentation_model='Diabetic_to_Normal_Selected'):
        super().__init__(dataset_loader, mask=True, batch_norm=True)
        self.model = model
        self.dwkit = dwkit
        self.values = values

        # Initialize and prepare private segmentation model with WACV 2024 paper values
        if private_segmentation_model == 'Diabetic_to_Normal_Selected':
            self.wizard_wacv2024paper.InputType | self centers | weights:
                self.wizard_wacv2024paper丢弃非周期性零样本实 ```