Fine-Tuning Bard (PaLM) on Vertex AI

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1. Why Fine-Tune PaLM (Bard)?

“A well-crafted prompt is a patch; fine-tuning is a firmware upgrade.”

I’ve used Bard (backed by PaLM) across a few client-facing NLP use cases — summarization, domain-specific Q&A, and even multi-turn chat systems. And here’s what I’ve found: prompt engineering hits a ceiling pretty fast once your use case goes beyond generic tasks.

For example, I was working on a legal document assistant. Bard was okay with generic prompts like “Summarize this contract,” but it lacked precision when interpreting nuanced legal clauses — even when I stacked prompts with few-shot examples. That’s when I turned to fine-tuning.

Here’s the deal:

Prompt engineering is flexible and quick, sure. But it’s stateless. Every time you call the model, it has no memory of past tweaks or preferred output formats. Fine-tuning, on the other hand, bakes your domain-specific language, tone, structure, and logic into the model itself. You’re not just steering it — you’re reprogramming its behavior.

You might be wondering: “Is it worth the hassle?”

In my experience — yes, but only if:

  • You’re working with niche domains (legal, medical, code analysis, etc.)
  • You need consistent formatting across outputs
  • You want to reduce inference time (shorter prompts, fewer tokens)

The ROI becomes clear when you start pushing the model to follow very specific patterns or workflows. For me, it was the difference between “just works” and “production-ready.”

2. Prerequisites & Setup

I’ll keep this lean — because if you’re here, I know you don’t need a spoon-fed GCP walkthrough.

Environment setup (bare minimum):

Here’s exactly what I use in my fine-tuning pipeline:

pip install google-cloud-aiplatform vertexai

You’ll also need:

  • A GCP project with Vertex AI API enabled
  • A service account with the following roles:
    • Vertex AI Admin
    • Storage Admin
    • Service Account Token Creator

I usually use the script below to handle IAM & API setup. Saves time.

gcloud services enable aiplatform.googleapis.com

gcloud iam service-accounts create vertex-sa \
  --description="Vertex Fine-Tune SA" \
  --display-name="vertex-sa"

gcloud projects add-iam-policy-binding <PROJECT_ID> \
  --member="serviceAccount:vertex-sa@<PROJECT_ID>.iam.gserviceaccount.com" \
  --role="roles/aiplatform.admin"

gcloud iam service-accounts keys create key.json \
  --iam-account=vertex-sa@<PROJECT_ID>.iam.gserviceaccount.com

Pro tip: Always scope your roles tightly in production. The ones above are good for experimentation.

Accessing PaLM fine-tuning on Vertex AI

Now, this might surprise you: not all PaLM models are fine-tunable, and the access isn’t always default. Here’s what I’ve had to do:

  1. Request access via your GCP sales contact or through the PaLM API console.
  2. Use these regions: us-central1, us-west4 (as of my last use).

Also, make sure you’re targeting a fine-tunable variant:

  • text-bison@001 → inference only
  • text-bison@latest or text-bison@002 → may support tuning (depending on release cycle)

Double-check with:

gcloud ai models list --region=us-central1

I’ve personally been caught off-guard before by using the wrong model ID — costs time and quota.

3. Preparing Your Dataset for Fine-Tuning

“Give the model the wrong food, and it’ll grow up to be confused.”
That’s something I learned the hard way.

Let’s be real — the dataset makes or breaks your fine-tuning run. I’ve seen great models fail miserably just because the training samples were noisy, repetitive, or lacked structure. So let me walk you through exactly how I prepare my datasets — no vague advice, just the stuff I’ve actually done.

Supported Format (What the model expects)

The fine-tuning interface expects JSONL (JSON Lines), and every line needs to follow this structure:

{"input_text": "What is prompt engineering?", "output_text": "Prompt engineering is the practice of crafting effective prompts to guide large language models."}

This format is non-negotiable.

If your dataset is messy, like a CSV or random markdown files, I highly recommend converting it early in your pipeline. I usually use a small script like this:

import pandas as pd
import json

df = pd.read_csv("raw_data.csv")

with open("fine_tune_dataset.jsonl", "w") as out:
    for _, row in df.iterrows():
        sample = {
            "input_text": row["prompt"].strip(),
            "output_text": row["response"].strip()
        }
        out.write(json.dumps(sample) + "\n")

Make sure every input_text and output_text is clean — no HTML, broken unicode, or extra whitespace. I’ve been burned before by sneaky tokens bloating my loss.

Formatting Tips That Saved Me Hours

Here’s what I do before sending data into training:

  • Token length checks: I cap both input and output around ~1024 tokens. PaLM can handle more, but going beyond that risks truncation and inconsistent training.
  • Deduplication: Repeating examples = overfitting. I run this deduplication check:
unique_pairs = set()
cleaned = []

for line in open("fine_tune_dataset.jsonl"):
    obj = json.loads(line)
    key = (obj["input_text"], obj["output_text"])
    if key not in unique_pairs:
        unique_pairs.add(key)
        cleaned.append(obj)

with open("cleaned_dataset.jsonl", "w") as f:
    for obj in cleaned:
        f.write(json.dumps(obj) + "\n")

Tokenization sanity checks: Sometimes a sentence looks short, but explodes into tokens. I use this quick check with the tiktoken tokenizer to get a sense:

import tiktoken
enc = tiktoken.encoding_for_model("gpt-3.5-turbo")

for obj in cleaned:
    input_tokens = len(enc.encode(obj["input_text"]))
    output_tokens = len(enc.encode(obj["output_text"]))
    if input_tokens + output_tokens > 2048:
        print("⚠️ Long sample:", obj)

Yes, I still use GPT’s tokenizer sometimes to estimate sizes. Works well enough.

Data Selection: Few-shot vs Full-task

This might surprise you: more data isn’t always better.

I’ve had better results from high-quality, diverse few-shot examples than from dumping 100K mediocre samples into training. In one project — a financial chatbot — I fine-tuned Bard on just 1,000 carefully curated Q&A pairs and it outperformed the prompt-only baseline by a mile.

What worked for me:

  • ~1,000 to 10,000 samples = sweet spot for most use cases
  • Avoid repeating identical task structures over and over
  • Cover edge cases (not just happy paths)

Augmentation (Only When It Makes Sense)

There’s a temptation to pad your dataset using paraphrasing tools or templating. I’ve done it — and sometimes it helps, sometimes it ruins the balance.

When I do use augmentation, I follow this:

  • Only paraphrase input_text, never output_text
  • Cap each base example at 2-3 variants max
  • Re-check token overlap — some paraphrasers introduce junk

Quick paraphrasing sample using nlpaug:

import nlpaug.augmenter.word as naw

aug = naw.SynonymAug(aug_src='wordnet')

def augment_text(text, n=2):
    return [aug.augment(text) for _ in range(n)]

Final Words (Before We Train)

Personally, I never move forward without these 3 checks:

  • Histogram of token lengths
  • Sample test run on 5-10 examples using inference API
  • Manual spot-checking for tone and consistency

These tiny checks have saved me GPU hours and budget more times than I can count.

4. Uploading Data to Vertex AI

“You can fine-tune the world’s best model — but if your training data’s stuck on your laptop, good luck.”

Once I’ve got my dataset prepped (as covered earlier), the next step is to get it into GCP — specifically, into a Cloud Storage bucket. Vertex AI uses these buckets as the source for both training and validation data.

I’ve used both the CLI (gcloud) and the Python SDK, but when I’m automating things or integrating into pipelines, the Python route is way cleaner.

Here’s the snippet I’ve personally used in multiple projects:

from google.cloud import storage

def upload_to_gcs(bucket_name, source_file_path, destination_blob_name):
    client = storage.Client()
    bucket = client.bucket(bucket_name)
    blob = bucket.blob(destination_blob_name)

    blob.upload_from_filename(source_file_path)
    print(f"Uploaded {source_file_path} to gs://{bucket_name}/{destination_blob_name}")

Here’s how I usually run this:

upload_to_gcs(
    bucket_name="my-finetune-bucket",
    source_file_path="cleaned_dataset.jsonl",
    destination_blob_name="data/train.jsonl"
)
upload_to_gcs(
    bucket_name="my-finetune-bucket",
    source_file_path="validation_dataset.jsonl",
    destination_blob_name="data/valid.jsonl"
)

You might be wondering: what if my bucket doesn’t exist yet?
No worries — I’ve got you covered:

gsutil mb -l us-central1 -p <PROJECT_ID> gs://my-finetune-bucket/

Always choose a region close to where you’ll be training (us-central1 or us-west4 are safe bets for PaLM).

Schema Validation (Yes, it’s a thing)

I’ve had fine-tuning jobs fail after 20+ minutes of waiting — just because the dataset didn’t match expected schema. Total waste of time.

Use the Vertex SDK’s Model resource checker, or run a dry validation using this basic structure:

import json

def validate_jsonl_schema(path):
    with open(path) as f:
        for i, line in enumerate(f):
            try:
                obj = json.loads(line)
                assert "input_text" in obj and "output_text" in obj
            except Exception as e:
                print(f"Schema issue at line {i}: {e}")

5. Launching a Fine-Tuning Job

Now we’re getting to the good stuff. This is where all your careful prep actually turns into a trained model.

Here’s the deal: you can launch a fine-tuning job via the Vertex AI Python SDK or REST API, but I always go with the SDK for readability and debugging.

Sample Fine-Tuning Script (Tested & Annotated)

from vertexai.language_models import TextGenerationModel

model = TextGenerationModel.from_pretrained("text-bison@002")

finetuned_model = model.tune_model(
    training_data="gs://my-finetune-bucket/data/train.jsonl",
    validation_data="gs://my-finetune-bucket/data/valid.jsonl",
    model_display_name="text-bison-legal-finetuned-v1",
    epochs=3,
    learning_rate=0.0002,
    batch_size=8
)

That’s the core — but let me break down the key params from my own tuning runs:

Parameters that actually matter:

  • learning_rate – I usually stay conservative (0.0002 to 0.0005). PaLM is already smart — you’re nudging, not rewriting.
  • batch_size – Depends on training dataset size. I’ve used 4–16 successfully; bigger isn’t always better.
  • epochs – I cap this at 3–5 max. Beyond that, you risk overfitting unless your dataset is huge.
  • Evaluation Strategy – Currently, you provide a validation set, and the SDK handles evaluation per epoch (loss, accuracy metrics available via console).

Job Monitoring & Logging (Don’t Skip This)

One of the biggest mistakes I see: folks kick off a job and forget to monitor.

  • Use the Vertex AI Console > Training Jobs tab to track loss curves, validation metrics, and job logs in real-time.
  • Logs go to Cloud Logging. Run this to tail them live:
gcloud logging read "resource.type=vertex_ai_job" --limit 50 --format="value(textPayload)"

If anything goes sideways — schema issues, permissions, memory errors — you’ll see it here.

That wraps this section. You’re now at the point where your model is actually training — which is where the real fun begins.

6. Evaluating the Fine-Tuned Model

“If training a model is the journey, evaluation is where you find out if the destination was worth it.”

Once my job’s done, the first thing I check isn’t the accuracy — it’s whether the model actually improved at what matters. And in most real-world fine-tuning use cases, that means evaluating performance in-context: with actual downstream prompts and tasks.

Check Metrics on Vertex AI Dashboard

You’ll find training and validation metrics right in the Vertex AI Console > Training > Custom Jobs.
In my experience, this includes:

  • Training loss per epoch
  • Validation loss
  • Job status logs (super helpful for catching silent schema or quota issues)

Still, dashboard metrics only tell part of the story. You need qualitative evaluation — and that’s where I lean on scripts and examples.

Practical Evaluation Script: Zero-Shot vs Fine-Tuned

I like to test the base model and the fine-tuned model side by side, using prompts sampled from my actual use case (e.g., customer support summaries, legal clause simplification, etc.).

Here’s a simple evaluation framework I’ve reused across projects:

from vertexai.language_models import TextGenerationModel

def test_prompt(prompt, model_name):
    model = TextGenerationModel.from_pretrained(model_name)
    response = model.predict(prompt, temperature=0.3)
    return response.text

prompt = "Summarize: The claimant argues that clause 9.4 voids liability due to negligence."

base_output = test_prompt(prompt, "text-bison@002")
tuned_output = test_prompt(prompt, "projects/your-project/locations/us-central1/models/your-tuned-model-id")

print("Base Output:\n", base_output)
print("\nTuned Output:\n", tuned_output)

This might surprise you: I’ve seen tuned models strip boilerplate, reduce hallucinations, and follow stylistic patterns (like passive voice or bulleting) far more consistently than base PaLM.

Metric-Based Evaluation: BLEU / ROUGE / Human Eval

When I need quantifiable comparisons — say, for reporting to stakeholders — I use simple scoring pipelines. Here’s a quick one for ROUGE:

from rouge_score import rouge_scorer

scorer = rouge_scorer.RougeScorer(['rougeL'], use_stemmer=True)
scores = scorer.score("reference output here", "model generated output here")
print(scores)

But I’ll say this from experience: human eval still wins. For nuanced tasks (like tone, style, or reasoning quality), I ask domain experts to rate side-by-sides blindly. That feedback is gold.

7. Deploying the Fine-Tuned Model

“A model sitting in a bucket is like a chef without a kitchen — it’s not doing much until it’s serving people.”

Once my tuned model performs the way I want, I deploy it to a staging endpoint first. Always.

Python Snippet: Deploying to Vertex AI

finetuned_model.deploy(
    endpoint_name="tuned-model-staging-endpoint",
    machine_type="n1-standard-4"
)

I keep staging and prod separate for two reasons:

  1. I want to test live prompt performance on low traffic before going wide.
  2. I sometimes A/B test with the base model to validate lift in KPIs (e.g., reduced manual corrections in a summarization app).

Versioning Strategy I Use

Each model gets a version tag like text-bison-legal-v1, text-bison-legal-v2, etc.
I store metadata (prompt templates, dataset ID, hyperparams) in a small JSON config file per version so I can track reproducibility.

{
  "version": "v1",
  "dataset": "gs://my-bucket/legal_dataset_v1",
  "epochs": 3,
  "lr": 0.0002
}

This helps a lot when you revisit projects after 2–3 months and need to retrain or rollback.

Autoscaling & Latency Tips

I’ve had models deployed for live inference on user-facing apps. Here’s what I’ve learned:

  • Latency is more about prompt size than model size. Keep prompts under 1K tokens where possible.
  • Autoscaling works fine with the default settings, but I usually cap min_replica_count=1 to avoid cold-start delays.
  • Use n1-highmem machines if your prompts or outputs are large (legal docs, multi-turn summaries, etc.).

8. Serving Inference

“Serving a model is like opening a restaurant — training was the recipe testing phase, now it’s showtime.”

Once I’ve deployed a fine-tuned model, my next challenge is making sure inference is fast, cheap, and resilient. I’ve been burned by latency spikes and cost overruns in the past, so I’ve developed a few go-to patterns that work well in production.

Real-Time Inference: Python SDK + REST

Here’s a minimal SDK call I use to hit a deployed model endpoint:

from vertexai.language_models import TextGenerationModel

model = TextGenerationModel.from_pretrained("projects/your-project/locations/us-central1/models/your-model-id")

response = model.predict(
    "Rewrite this paragraph with a more formal tone...",
    temperature=0.3,
    max_output_tokens=256
)

print(response.text)

If I’m wiring this into a microservice, though, I usually prefer calling the REST API directly — lower dependency surface area.

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  https://us-central1-aiplatform.googleapis.com/v1/projects/YOUR_PROJECT/locations/us-central1/publishers/google/models/YOUR_MODEL:predict \
  -d '{
    "instances": [{"prompt": "Summarize this document..."}]
  }'

Batch Prediction Pipeline

Batch inference is where I typically lean on Vertex Pipelines or a simple Cloud Function + GCS trigger setup. This combo scales better than trying to loop over files in a notebook.

Here’s a trimmed-down version of what I use with the SDK:

from google.cloud import aiplatform

aiplatform.init(project="your-project", location="us-central1")

batch_prediction_job = aiplatform.Model("your-model-id").batch_predict(
    job_display_name="batch-predict-legal",
    gcs_source="gs://your-bucket/prompts.jsonl",
    gcs_destination_prefix="gs://your-bucket/predictions/",
    instances_format="jsonl",
    predictions_format="jsonl",
    machine_type="n1-standard-4"
)

batch_prediction_job.wait()

It’s fully managed, and honestly, I’ve found it super reliable even when predicting over gigabytes of prompts.

Caching & Rate-Limiting

This might surprise you: I cache almost every output in production, even for generative models. Here’s why:

  • Prompt templates are mostly static
  • Reuse patterns are high in user queries (especially in support/chat apps)
  • Cost savings are non-trivial

I use Cloud Memorystore (Redis) for real-time APIs and BigQuery for longer-term deduplication and analytics.

Rate-limiting? I’ve built it using API Gateway + Cloud Armor for most cases — but for ultra-low-latency environments, I’ve written simple token bucket algorithms into FastAPI middleware.

Inference Cost Breakdown & Optimization

Vertex AI charges for:

  1. Prediction compute time
  2. Tokens processed
  3. Model deployment resources

Here’s how I keep costs sane:

  • Set min_replica_count = 0 for staging endpoints
  • Use batch prediction for anything not real-time
  • Keep prompt+output sizes under 512 tokens when possible (this drastically reduces token charges)
  • If latency isn’t critical, use n1-standard-2 or even n1-highcpu machines for lightweight models

From my experience, tuning the prompt format alone can save 25–30% of token cost without impacting output quality.

9. Post-Fine-Tuning Optimization

“The end of training is just the start of adaptation.”

Once my model’s deployed, I focus on making it smarter over time — without retraining from scratch every time. This is where lighter-weight optimization techniques shine.

LoRA & PEFT (When Supported)

Right now, Vertex AI doesn’t officially support LoRA or PEFT-style adapters for PaLM, but if/when it does, I’ll be the first to jump on it. Why?

  • They cut fine-tuning costs drastically
  • You can store multiple adapter versions for different tasks
  • Training is faster — ideal for agile workflows or frequent updates

In past non-GCP projects, I’ve used PEFT with open LLMs and saved 80–90% of training time and storage. It’s a game-changer.

Prompt Adapters vs Full Fine-Tuning

Here’s the deal: I use prompt engineering or prompt adapters when:

  • Use cases are evolving fast
  • Data is sparse
  • I don’t want to commit GPU budget yet

But the moment I start seeing repeated prompt hacks like “Rewrite this in the voice of X”, I know it’s time to fine-tune. Rule of thumb: if you’re modifying prompts instead of improving data, you’re stalling.

Logging & Feedback Loop

I can’t stress this enough — if you’re not logging real usage, you’re blind.

I personally log:

  • Prompt text
  • Model version
  • Output text
  • Latency
  • User actions (clicked? rephrased? ignored?)

Then I periodically sample this data and retrain with real examples. It’s helped me catch drifts I didn’t even know were happening — like tone mismatch or jargon shifts in enterprise copy.

11. Conclusion + Resources

“Just because you can fine-tune doesn’t mean you should.”

I’ve run enough production LLMs to tell you this: fine-tuning isn’t a magic bullet. In fact, there’ve been times when I rolled back a fine-tuned model and got better performance just by improving prompt structure or adding a retrieval layer.

So let me give it to you straight — here’s what I’ve learned by actually shipping these systems:

When Fine-Tuning Pays Off

  • Your use case has consistent structure (e.g., contract generation, templated reports).
  • You’re repeating the same complex prompt patterns across users.
  • You’ve already maxed out what prompt engineering and RAG can do.
  • You want deterministic behavior — especially critical in high-stakes domains like legal or healthcare.

Personally, I’ve seen fine-tuning give ~15–25% uplift in task-specific accuracy vs base models, but only when my data was clean and my eval process was solid.

When NOT to Fine-Tune

This might surprise you: I skip fine-tuning entirely when:

  • The task benefits more from up-to-date info (like recent news, pricing, etc.) — I go with Retrieval Augmented Generation (RAG) instead.
  • Prompting alone gives >85% accuracy (especially with tools like few-shot chaining).
  • My data is messy, sparse, or too broad to model effectively.
  • I need fast iteration or multiple task variations — prompt adapters or prefix-tuning wins here.

If you’re building chatbots, support assistants, or any system where context changes fast — go RAG or prompt-chaining first. I’ve built entire MVPs that scaled to thousands of users with zero fine-tuning, just smart retrieval and structured prompt scaffolds.

Tools & Resources I Recommend

Here are the references and tools I personally use or have built around:

Final Thought

If there’s one thing I hope you take away from this — it’s that fine-tuning should be your last move, not your first. I treat it like a scalpel: precise, expensive, and only worth it when other tools fall short.

And when I do fine-tune, I log everything, I evaluate continuously, and I revisit the problem every few weeks with fresh data. That’s how you make it sustainable — and production-worthy.

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