Seventh phase of Arc 5. Customizing models without breaking the bank.

Full fine-tuning of a large model requires datacenter compute. Parameter-Efficient Fine-Tuning (PEFT), LoRA and QLoRA, updates only ~1% of weights via low-rank adapters, making fine-tuning practical on consumer GPUs. By phase end you’ve fine-tuned at least one open-weights model on a small task, tracked the run in MLflow, served the fine-tuned variant alongside the base via KServe.

This phase makes you operationally fluent at fine-tuning. The patterns transfer to instruction tuning, DPO (Direct Preference Optimization), and the next-generation alignment techniques.

Prerequisites

• Phase 44 complete; inference optimization fluency
• GPU available (24GB ideal; 16GB workable for QLoRA on smaller models)

Why this phase exists

In 2026 fine-tuning is the practical adaptation mechanism for production LLM applications. Few teams pre-train; many fine-tune. The patterns (LoRA, instruction tuning, DPO) are dominant. This phase installs them at production depth.

The pattern-first frame

Same eight steps.

1. PROBLEM

You have an open-weights base model. You want it to behave differently: answer in your company’s voice, follow a specific task format, refuse certain categories, adopt domain expertise. Fine-tuning adjusts the model. Full fine-tuning costs too much; PEFT makes it tractable.

2. PRINCIPLES

2.1 LoRA: low-rank adaptation

Instead of updating all weights, train small low-rank matrices that “shift” the original weights. Typical: ~1% of total parameters. Quality usually close to full fine-tune.

→ Pattern: fine-tuning-strategies: OUTLINE this phase

Investigate:

• Walk LoRA math: W + ΔW where ΔW = A × B, A and B low-rank.
• Why does LoRA work? (Hint: most useful adaptations are low-rank.)
• What’s the cost/benefit of higher rank?

2.2 QLoRA: quantized LoRA

Combine quantization (4-bit base) with LoRA. The base model fits on a small GPU; LoRA adapter is trained. The unlock that enabled fine-tuning Llama-70B on a single 24GB GPU.

Investigate:

• Walk QLoRA: base model in 4-bit + LoRA adapters in FP16, double-quant for memory.
• Why is gradient computation through 4-bit base still feasible?
• When does QLoRA degrade quality vs LoRA on FP16 base?

2.3 Instruction tuning

Fine-tune on (instruction, response) pairs. Teaches the model to follow instructions specifically. The dataset shape (format, diversity, quality) dominates outcome.

Investigate:

• Walk instruction tuning vs raw next-token prediction.
• Why does dataset quality matter more than dataset size for instruction tuning?
• What’s the role of supervision rate (fully-supervised vs RLHF)?

2.4 DPO: Direct Preference Optimization

Train on (prompt, chosen_response, rejected_response) tuples to align toward preferred behavior. Simpler than RLHF; comparable results in many cases.

Investigate:

• Walk DPO loss: encourage chosen > rejected via likelihood ratio.
• Why is DPO simpler than RLHF? (Hint: no reward model + PPO needed.)
• When does RLHF beat DPO?

2.5 Dataset preparation as the actual work

The model isn’t the bottleneck; the data is. Data quality, format consistency, deduplication, contamination check (test set leakage), distribution shaping.

Investigate:

• What’s a contamination check, and why does it matter?
• How do you curate a diverse instruction dataset?
• What’s data deduplication’s role in dataset prep?

2.6 K8s-native fine-tune workflow

Fine-tune as a KubeRay RayJob: declare the job → operator runs it → MLflow tracks → registered model lands in registry. Composes with Phase 37’s distributed training stack.

→ Pattern: operator-pattern reinforced

Investigate:

• Walk a fine-tune RayJob CRD: container image + dataset reference + LoRA config.
• How does the fine-tuned adapter end up in MLflow?
• How does KServe load the base model + LoRA adapter for serving?

3. TRADE-OFFS

4. TOOLS (as of 2026-06)

• PEFT (HuggingFace): LoRA + QLoRA library
• TRL (HuggingFace): SFT + DPO + RLHF trainers
• bitsandbytes: 4-bit quantization
• Unsloth: speed-optimized LoRA training
• KubeRay for distribution
• MLflow for tracking

Reading

• LoRA paper (Hu et al.)
• QLoRA paper (Dettmers et al.)
• DPO paper (Rafailov et al.)
• HuggingFace PEFT + TRL docs

5. MASTERY: Fine-tune via KubeRay

[ ] Pick a base model (Llama 3.1 8B, Mistral, or similar open weights)
[ ] Pick a fine-tune task (your chronicle style mimicry, code-completion, classification)
[ ] Curate or compose dataset (~1K-10K examples)
[ ] Contamination check against any benchmark you care about
[ ] LoRA fine-tune locally (single GPU); verify reasonable loss curve
[ ] Switch to KubeRay RayJob for distributed fine-tune (multi-GPU)
[ ] Track in MLflow: hyperparameters, loss curves, adapter artifacts
[ ] Eval the fine-tuned model: held-out test set + qualitative inspection
[ ] Register fine-tuned adapter in MLflow registry
[ ] Deploy via KServe: base model + LoRA adapter via vLLM LoRA support
[ ] Optional: DPO fine-tune on a small preference dataset

6. COMPARE: Unsloth

Pick one fine-tune; run it via Unsloth’s optimized path. Compare training time + quality.

400-word reflection.

7. OPERATE

• 2-3 runbooks: fine-tune OOM, LoRA serving failure (adapter not loading), eval regression
• 1-2 ADRs (LoRA over QLoRA when fits; PEFT lib over raw PyTorch)
• Weekly log

8. CONTRIBUTE

• PEFT / TRL: docs, edge cases
• A public dataset + LoRA fine-tune on HuggingFace Hub

What ships from this phase

• At least one fine-tuned model registered + served on basecamp
• Fine-tune RayJob template in basecamp/ml-infra-helpers
• Fine-tune runbooks

Validation criteria

[ ] LoRA fine-tune working via KubeRay RayJob
[ ] Fine-tuned model registered + served via KServe
[ ] Quality eval shows targeted behavior change
[ ] All 10 operational depth checks
[ ] Compare reflection (400 words)
[ ] 2-3 runbooks
[ ] 1-2 ADRs
[ ] Pattern entries:
    - fine-tuning-strategies → OUTLINE
[ ] Exit Test passed

Exit Test

Time: 2.5 hours.

Part 1: Build (90 min)

Run a small LoRA fine-tune via KubeRay. Register adapter. Serve via KServe + vLLM LoRA support. Verify behavior change.

Part 2: Articulate (60 min)

~1000 words: “Walk a LoRA fine-tune end-to-end. Math (low-rank update), engineering (KubeRay job, MLflow tracking), deployment (LoRA adapter loading at serving). Cite patterns.”

Anti-patterns

Patterns touched this phase

• fine-tuning-strategies: OUTLINE
• operator-pattern reinforced

→ Next: Phase 46: LLM Gateway: ship prism