RTX 5060 Ti 16GB for Iterative Fine-Tune Loop

Model quality improves faster when iteration is cheap. A dedicated RTX 5060 Ti 16GB on our dedicated GPU hosting gives you a card that is always warm, always available, and costs a flat monthly fee – which makes “try it” the default response rather than “queue it”. This guide sets out a fine-tune/eval loop optimised for iteration speed, not production serving.

Contents

• The loop
• Dataset prep scripts
• Per-experiment LoRA
• Eval harness
• Hot-swap deploy
• Cycle time budget

The Loop

1. Collect new examples from production logs or user feedback
2. Clean, dedupe, and shuffle into train/eval splits
3. Fire a LoRA or QLoRA run with a fresh experiment ID
4. Evaluate on a fixed held-out set and compare to current champion
5. If better: hot-swap the adapter into the serving vLLM without restart
6. Monitor, gather next batch of feedback, loop

Dataset Prep Scripts

Keep prep scripts in a repo so every run is reproducible. A minimal pipeline:

# dataset_prep.py
import json, hashlib, random
from pathlib import Path

def load_prod_logs(path):
    return [json.loads(l) for l in Path(path).read_text().splitlines()]

def to_chatml(row):
    return {"messages":[
        {"role":"user","content":row["prompt"]},
        {"role":"assistant","content":row["response"]},
    ]}

def dedupe(rows):
    seen = set(); out = []
    for r in rows:
        h = hashlib.sha256(json.dumps(r,sort_keys=True).encode()).hexdigest()
        if h not in seen:
            seen.add(h); out.append(r)
    return out

rows = dedupe([to_chatml(r) for r in load_prod_logs("logs.jsonl")])
random.seed(42); random.shuffle(rows)
split = int(len(rows)*0.9)
Path("train.jsonl").write_text("\n".join(json.dumps(r) for r in rows[:split]))
Path("eval.jsonl" ).write_text("\n".join(json.dumps(r) for r in rows[split:]))
print(f"train={split} eval={len(rows)-split}")

Per-Experiment LoRA

One adapter per experiment, named and logged. Pattern:

Run with Unsloth:

EXPID=$(date +%Y-%m-%d)-qlora-r16-v$(git rev-parse --short HEAD)
python train.py --output ./runs/$EXPID --data ./data \
  --base unsloth/Meta-Llama-3.1-8B-Instruct --r 16 --lr 2e-4 --epochs 3

Eval Harness

Keep the eval set fixed across experiments. Changing the eval set invalidates cross-run comparison. A simple scorer:

import json, subprocess
from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="x")

scores = []
for ex in map(json.loads, open("eval.jsonl")):
    pred = client.chat.completions.create(
        model="candidate", messages=ex["messages"][:-1], max_tokens=256
    ).choices[0].message.content
    gold = ex["messages"][-1]["content"]
    scores.append(score_fn(pred, gold))
print(f"mean={sum(scores)/len(scores):.3f}")

Run eval against the new candidate adapter and the current champion; promote only on improvement.

Hot-Swap Deploy

LoRAX allows atomic adapter swaps without stopping the server. Traffic continues on the base model while the new adapter registers:

curl -X POST http://localhost:8000/v1/lora_adapters \
  -H "Content-Type: application/json" \
  -d '{"lora_name":"2026-04-23-v7","lora_path":"/models/adapters/2026-04-23-v7"}'

Clients choose the adapter via the model field in the OpenAI request body. Canary the new one behind a feature flag before cutting over fully.

Cycle Time Budget

See also: LoRA guide, QLoRA guide, Unsloth speed, fine-tune throughput, vLLM setup.