Fine-Tune Llama 3 on Medical Data — GDPR-Compliant, EU Cloud GPU

[Your dataset (EU origin)]
        │
        │  HTTPS — encrypted in transit
        ▼
[GhostNexus API — Frankfurt]
        │
        │  Job dispatched to nearest EU node
        ▼
[GPU Node — Hetzner Frankfurt / Helsinki]
        │
        │  Docker --network=none  ← no outbound connections possible
        ▼
  ┌─────────────────────────────┐
  │   Training container        │
  │   Llama 3 + QLoRA adapters  │
  │   your dataset (ephemeral)  │
  └─────────────────────────────┘
        │
        │  Training complete
        ▼
[LoRA adapter weights returned to you]
[Training data deleted — zero retention]

  Data never touches non-EU infrastructure.

from datasets import Dataset
import json, re

# ─── Fictional clinical NLP examples ────────────────────────────────────────
# Format: free-text note → ICD-10 code (structured label)
# In production: apply a NER-based de-identification step before this point.

records = [
    {"note": "Patient presents with fever 39.1°C, dry cough for 3 days, no dyspnea.", "icd10": "J06.9"},
    {"note": "Acute chest pain radiating to left arm. ECG: ST elevation leads II, III, aVF.", "icd10": "I21.1"},
    {"note": "Type 2 diabetes, poorly controlled. HbA1c 9.2%. Initiating insulin therapy.", "icd10": "E11.65"},
    {"note": "Recurring migraine with aura, 2–3 episodes per month. Photophobia, nausea.", "icd10": "G43.109"},
    {"note": "Post-operative wound infection, right knee arthroplasty, purulent discharge.", "icd10": "T84.50XA"},
    {"note": "Anxiety disorder with panic attacks, sleep onset insomnia. PHQ-9 score: 14.", "icd10": "F41.0"},
]

# ─── Basic pseudonymisation check ───────────────────────────────────────────
# Strip any residual patterns that look like names or dates.
PATTERNS = [
    r"\b(?:Mr|Mrs|Dr|Prof)\.?\s+[A-Z][a-z]+",  # titles + names
    r"\b\d{2}/\d{2}/\d{4}\b",                  # DD/MM/YYYY dates
    r"\b(?:born|DOB)\s+\d{4}\b",               # birth year references
]

def pseudonymise(text: str) -> str:
    for pat in PATTERNS:
        text = re.sub(pat, "[REDACTED]", text)
    return text

records = [{"note": pseudonymise(r["note"]), "icd10": r["icd10"]} for r in records]

# ─── Build HuggingFace Dataset ───────────────────────────────────────────────
dataset = Dataset.from_list(records)
print(dataset)

# Option A: push to private Hub repo (stays in your org's infra)
# dataset.push_to_hub("your-org/medical-icd10-private", private=True)

# Option B: save as JSONL for direct use in training script
with open("medical_train.jsonl", "w") as f:
    for row in records:
        f.write(json.dumps(row, ensure_ascii=False) + "\n")

print(f"Saved {len(records)} examples to medical_train.jsonl")

# submit_training_job.py
# Runs locally. Submits the training script + dataset to a GhostNexus EU node.
# pip install ghostnexus

import ghostnexus
import time

client = ghostnexus.GhostNexus(api_key="YOUR_API_KEY")

# Read the training script
with open("fine_tune_llama3_medical.py", "r") as f:
    script = f.read()

# Submit — GhostNexus automatically selects the nearest EU node
result = client.run(
    script=script,
    task_name="llama3-medical-qlora",
    files=["medical_train.jsonl"],          # uploaded with the job, deleted after
    gpu_type="RTX_4090",                    # or RTX_3090, A100_40G
    region="eu",                            # never routes outside EEA
    webhook_url="https://your-server.com/training-complete",  # optional
)

job_id = result["job_id"]
print(f"Job {job_id} queued | Status: {result['status']}")
print(f"Estimated cost: ${result['cost_estimate_usd']:.2f}")

# ─── Poll for completion (or use webhook above) ──────────────────────────────
while True:
    status = client.get_job(job_id)
    print(f"[{status['elapsed_s']}s] {status['status']} — {status['log_tail']}")
    if status["status"] in ("completed", "failed"):
        break
    time.sleep(15)

if status["status"] == "completed":
    # Retrieve LoRA adapter weights to local directory
    client.download_artifacts(job_id, dest="./llama3-medical-adapter/")
    print(f"Adapter weights downloaded.")
    print(f"Final cost: ${status['cost_usd']:.3f}")
    print("\nLast training logs:")
    print(status["output"][-2000:])