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Home/Services/Discipline 02
// Discipline 02

Train models on data that actually matters.

Custom training from scratch, transfer learning, LoRA / QLoRA adaptation, instruction tuning. We start by defining what 'better' looks like — then build the pipeline that gets you there.

// training · run_284 · LoRA r=16 · A100 · 100k stepsRUN1.00.750.500.2505101520epochepoch 14 · best ✓val_loss: 0.27 · checkpoint savedoverfit zoneval ↑ overfittingbaseline → fine-tuned↓ 63% loss reductiontrain_loss · 0.14val_loss · 0.27best checkpointLoRA r=16 α=32 · lr: 2e-4 · bs: 32 · warmup: 100 steps · base: Llama-3-8B
/ 2.1

Custom Architecture Training

Training from scratch or adapting published architectures when no foundation model fits the domain.

// train · custom_detector · epoch 14/30
train_loss ............. 0.142 ↓ –18% from prev
val_loss ............. 0.167 ★ best checkpoint
mAP@0.5 ............. 0.734 +6.1% vs baseline
throughput ............. 248 img/s 4× A100 · DDP
memory ............. 18.4 / 80 GB
ETA ................. 16 epochs · ~2.4h

// Details

  • PyTorch, JAX, HuggingFace Transformers
  • Classification, detection, segmentation, generative
  • Evaluation harness designed before training starts
  • Full experiment tracking (W&B / MLflow)

// Output formats

PyTorch .ptONNXTorchScript
/ 2.2

Fine-Tuning & PEFT

LoRA, QLoRA, and full fine-tuning for LLMs and vision models. Efficient adaptation without unnecessary compute.

// lora_config · mistral-7b · r=16
base_model ........ Mistral-7B-v0.1
strategy ........ QLoRA · 4-bit NF4
rank (r) ........ 16 alpha: 32
target_mods ........ q_proj · v_proj · k_proj
trainable ........ 41M / 7.24B (0.57%)
gpu_memory ........ 12.8 GB vs 42 GB full ft

// Details

  • LoRA / QLoRA instruction tuning
  • DPO / RLHF preference training
  • Vision-language model adaptation
  • 4-bit / 8-bit quantization-aware training

// Output formats

HuggingFace HubGGUFAWQ
/ 2.3

Model Evaluation

We build the benchmark before we build the model. Evaluation is a first-class deliverable, not an afterthought.

// eval_report · domain_benchmark_v2 · 2,480 items
metricoursbaselinedelta
accuracy0.9340.891+4.8%
f1_macro0.9210.874+5.4%
precision0.9380.898+4.5%
recall0.9040.852+6.1%
latency_p5048ms51ms–5.9%

// Details

  • Domain-specific benchmarks designed with stakeholders
  • Error analysis by category, slice, and edge case
  • Regression suite for ongoing model updates
  • Human-in-the-loop evaluation for generative tasks

// Output formats

Eval reportJSON resultsDashboard
/ 2.4

Training Pipeline Engineering

Distributed training, data loading, checkpoint management, and reproducibility. The infrastructure that makes experiments reliable.

// train_pipeline.yaml · v2.4 · reproducible
data_source ...... gs://datasets/v3
batch_size ...... 32 grad_accum: 4
optimizer ...... AdamW · lr: 2e-4
scheduler ...... cosine + 500 warmup
checkpoint ...... every 2 epochs → GCS
seed ...... 42 reproducible: ✓

// Details

  • Multi-GPU training (DDP / FSDP)
  • Reproducible experiment configs
  • Data pipeline optimization
  • Cost-aware compute planning

// Output formats

DockerKubernetes YAMLCI configs
// Work with us

Ready to ship? Let's scope it together.

Whether it's labeled data, a fine-tuned model, a RAG pipeline, or an agent running in production — bring us the brief. We'll scope it, price it, and tell you honestly if we're the right team. Inside 48 hours, no commitment.