Pipeline PelatihanTraining Pipeline

MonitoringMonitoring

Fase PelatihanTraining Phase

SELESAICOMPLETE

Multi-Phase Training — AktifMulti-Phase Training — Active

Pre-training: 34,027 steps. SFT v1: 4,844 steps. DPO v1: 1,677 steps. SFT MAX: 4,000 steps (loss 22.86). DPO MAX: 1,300 steps (loss 5.32→0.083). Final checkpoint: best_3b.pt. Phase B Extended PT: berjalan di cloud, target 24h A10G.Pre-training: 34,027 steps. SFT v1: 4,844 steps. DPO v1: 1,677 steps. SFT MAX: 4,000 steps (loss 22.86). DPO MAX: 1,300 steps (loss 5.32→0.083). Final checkpoint: best_3b.pt. Phase B Extended PT: running on cloud, target 24h A10G.

GPU: A10G
Script: train_grpo_modal.py
Runtime: Modal (cloud)
  • aqi_content_qualityLULUSPASS
  • smar_identity_qualityLULUSPASS
  • validate_no_hallucinationLULUSPASS
  • validate_dpo_maxLULUSPASS
  • pytestLULUSPASS
  • py_compileLULUSPASS
GPU TargetA10G
RuntimeModal
Scriptphase_b.py
Model3B

Tier PelatihanTraining Tiers

1

Base Model

AktifActive

2

RAG Growth

Dalam PengembanganIn Development

3

Online DPO

Dalam PengembanganIn Development

4

Self-Play

Dalam PengembanganIn Development

DatasetDatasets

0

Training Rows

370K curriculum bank

0

Synthetic Rows

81 categories, OGW/MiMo

0

Synthetic Target

Weighted by priority tier

0

Eval Set

ID=1183, EN=317, AR=66

0

DPO Pairs

Teacher critiques + upgrade

0

Reasoning Traces

Target: 500+ RL Distilled

KategoriCategories

Total KategoriTotal Categories11
Data Pool370K
BahasaLanguages3
KurikulumCurriculum1,033

Distribusi EvalEval Distribution

Indonesian1,183
English317
Arabic66
Total1,555

Alur PelatihanTraining Flow

graph LR A["Data Collection"] --> B["Quality Filter"] B --> C["SFT 15K"] B --> D["Eval 1.5K"] C --> E["Pre-training"] D --> F["Evaluation"] E --> G["Checkpoint"] F --> H{"Pass"} H -->|"Yes"| I["Deploy"] H -->|"No"| E G --> E

Self-Learning Loop

graph TD A["Base Model"] -->|"Tier 1"| B["Pre-training"] B --> C["Conversations"] C -->|"Tier 2"| D["RAG Growth"] D --> E["Knowledge Base"] E --> F["User Feedback"] F -->|"Tier 3"| G["Online DPO"] G --> H["Aligned Model"] H --> I["Self-Generated Data"] I -->|"Tier 4"| J["Self-Play"] J --> A

Distribusi DataData Distribution

pie showData title "Dataset Categories (370K Total)" "General" : 107441 "Coding" : 56608 "Knowledge" : 35106 "Advanced" : 29147 "Islamic" : 25408 "Medical" : 22712 "Conversation" : 22142 "Math" : 21646 "Agentic" : 20508 "Indonesian" : 19436 "Psychology" : 9846