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map-verl-config

Map VERL training configuration to AgentJet configuration. Find VERL config in verl_default.yaml, check for existing mappings in config_auto_convertion_verl.jsonc, add new mappings to ajet_default.yaml and the conversion schema, and optionally add parameters to AgentJetJob.

Datos de origen

Repositorio
modelscope/AgentJet
Última actividad en el origen
13 de mayo de 2026 a las 05:03
Idioma detectado de SKILL.md
inglés
Estrellas
240
Forks
28

Opciones de instalación

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Lee SKILL.md y los archivos complementarios que muestra SkillsMP antes de decidir si quieres instalarlo.

Mostrando SKILL.md

SKILL.md
Instrucciones de origen · Vista previa de solo lectura
name
map-verl-config
description
Map VERL training configuration to AgentJet configuration. Find VERL config in verl_default.yaml, check for existing mappings in config_auto_convertion_verl.jsonc, add new mappings to ajet_default.yaml and the conversion schema, and optionally add parameters to AgentJetJob.
license
Complete terms in LICENSE.txt
1. find user requested verl config in codebase/agentjet/ajet/default_config/verl/verl_default.yaml 2. check `codebase/agentjet/ajet/default_config/verl/config_auto_convertion_verl.jsonc`, whether a mapping to this config already exists. 3. if not, add a config under `ajet` field in `codebase/agentjet/ajet/default_config/ajet_default.yaml`, and add a mapping in `codebase/agentjet/ajet/default_config/verl/config_auto_convertion_verl.jsonc` 4. double check, confirm that default value in `ajet_default.yaml` is the same as verl config in `verl_default.yaml`, and the mapping is correct in `config_auto_convertion_verl.jsonc` 5. ask user whether to add to AgentJetJob (ajet/copilot/job.py), if the user confirms: - learn how other config is added in ajet/copilot/job.py - add to __init__ signature (with type hint and default None) - update docstring with parameter description - add instance attribute assignment with cast() - add mapping to `overrides` dict 6. **CRITICAL**: update `ajet/default_config/ajet_config_schema.py` - the schema must have a dataclass for EVERY nested level in the config path - e.g., for `ajet.trainer_common.optim.lr`, need: - `AjetOptim` dataclass with `lr: float = 1e-6` - `AjetTrainerCommon` must have `optim: AjetOptim = field(default_factory=AjetOptim)` - if parent dataclass is missing the nested field, config loading will store it as a raw dict instead of a typed dataclass, causing `getattr()` to fail at runtime
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