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Running
Running
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```mermaid
graph LR
Entry_Point["Entry Point"]
Configuration_Management["Configuration Management"]
Data_Pipeline["Data Pipeline"]
Model_Abstraction["Model Abstraction"]
Training_Orchestrator["Training Orchestrator"]
Entry_Point -- "Initializes and Uses" --> Configuration_Management
Entry_Point -- "Initializes" --> Data_Pipeline
Entry_Point -- "Initializes" --> Model_Abstraction
Entry_Point -- "Initializes and Invokes" --> Training_Orchestrator
Configuration_Management -- "Provides Configuration To" --> Model_Abstraction
Configuration_Management -- "Provides Configuration To" --> Data_Pipeline
Configuration_Management -- "Provides Configuration To" --> Training_Orchestrator
Data_Pipeline -- "Provides Data To" --> Training_Orchestrator
Model_Abstraction -- "Provides Model To" --> Training_Orchestrator
click Entry_Point href "https://github.com/Josephrp/SmolFactory/blob/main/docs/Entry_Point.md" "Details"
click Configuration_Management href "https://github.com/Josephrp/SmolFactory/blob/main/docs/Configuration_Management.md" "Details"
click Data_Pipeline href "https://github.com/Josephrp/SmolFactory/blob/main/docs/Data_Pipeline.md" "Details"
click Model_Abstraction href "https://github.com/Josephrp/SmolFactory/blob/main/docs/Model_Abstraction.md" "Details"
click Training_Orchestrator href "https://github.com/Josephrp/SmolFactory/blob/main/docs/Training_Orchestrator.md" "Details"
```
## Details
Abstract Components Overview
### Entry Point [[Expand]](./Entry_Point.md)
The primary execution script that orchestrates the entire training process. It initializes all other major components, loads configurations, sets up the training environment, and invokes the `Training Orchestrator`.
**Related Classes/Methods**:
- `train`
### Configuration Management [[Expand]](./Configuration_Management.md)
Centralized management of all training parameters, model specifications, data paths, and hyper-parameters. It is responsible for loading, validating, and providing access to configuration settings, supporting base and custom configurations.
**Related Classes/Methods**:
- <a href="https://github.com/Josephrp/SmolFactory/docs/blob/main/src/config.py#L1-L1" target="_blank" rel="noopener noreferrer">`config` (1:1)</a>
### Data Pipeline [[Expand]](./Data_Pipeline.md)
Handles the entire data lifecycle, including dataset loading, preprocessing (e.g., tokenization, formatting), and creating efficient data loaders for both training and evaluation phases.
**Related Classes/Methods**:
- <a href="https://github.com/Josephrp/SmolFactory/docs/blob/main/src/data.py#L1-L1" target="_blank" rel="noopener noreferrer">`data` (1:1)</a>
### Model Abstraction [[Expand]](./Model_Abstraction.md)
Encapsulates the logic for loading pre-trained models, defining model architectures, and managing different model variants (e.g., quantization, LoRA adapters). It provides a consistent interface for model interaction.
**Related Classes/Methods**:
- <a href="https://github.com/Josephrp/SmolFactory/docs/blob/main/src/model.py#L1-L1" target="_blank" rel="noopener noreferrer">`model` (1:1)</a>
### Training Orchestrator [[Expand]](./Training_Orchestrator.md)
Implements the core training and fine-tuning loop. This includes managing forward and backward passes, optimization, loss calculation, and integration with acceleration libraries (e.g., `accelerate`). It also handles callbacks and evaluation logic.
**Related Classes/Methods**:
- <a href="https://github.com/Josephrp/SmolFactory/docs/blob/main/src/trainer.py#L1-L1" target="_blank" rel="noopener noreferrer">`trainer` (1:1)</a>
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