Fine-Tuning PaLM 2: A Practical Guide

1. Why Fine-Tune PaLM 2 in 2025?

“You can’t squeeze water from a stone—unless you teach the stone where the river is.”

That’s how I’ve come to see base LLMs. They’re powerful, sure—but they don’t always get you the specificity you need. In my own projects, I’ve hit several hard walls where prompt engineering and retrieval weren’t enough.

One involved summarizing legal documents with strict terminology compliance. Another? A financial chatbot that needed to follow regulatory phrasing without drifting.

In both cases, even the most cleverly crafted prompts failed to deliver the consistency I needed. Retrieval-Augmented Generation (RAG) helped… until it didn’t. The context window filled up fast, and hallucinations crept in anyway. That’s where fine-tuning made a real difference.

If you’re building tools that require domain precision, adherence to strict response styles, or behavior that can’t be reliably achieved through prompts alone—fine-tuning isn’t optional. It’s the upgrade you need.

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2. Pre-requisites: What You Actually Need (Not Just Theory)

Let’s skip the obvious. If you’re reading this, I’m assuming you know your way around Python. What tripped me up early on wasn’t the code—it was the config. Here’s what actually matters if you’re serious about fine-tuning PaLM 2 using Google’s Vertex AI.

Tools I Used

• Google Cloud SDK (gcloud)
• Vertex AI Python SDK (google-cloud-aiplatform)
• Google Cloud Storage (GCS) for dataset hosting
• Optional but handy: transformers, datasets, tqdm for preprocessing

Step-by-Step Setup (Command-Line + Python)

1. Enable Required APIs

gcloud services enable aiplatform.googleapis.com
gcloud services enable storage.googleapis.com

2. Set Up a GCP Project and Authenticate

gcloud auth login
gcloud config set project your-project-id

3. Create and Assign IAM Role for Tuning

This part is often skipped in tutorials. You’ll need to give your service account the right permissions.

gcloud projects add-iam-policy-binding your-project-id \
  --member="serviceAccount:your-sa@your-project-id.iam.gserviceaccount.com" \
  --role="roles/aiplatform.admin"

Minimum required roles:

• Vertex AI Admin
• Storage Admin (for dataset uploads)
• Service Account User

4. Install the SDKs

pip install google-cloud-aiplatform

Make sure you’re using a recent Python version (>=3.8). I recommend using a clean virtual environment, especially if you’re juggling other GCP projects.

5. Upload Your Dataset to GCS

You’ll need your data hosted in a GCS bucket:

gsutil mb -l us-central1 gs://your-bucket-name/
gsutil cp data.jsonl gs://your-bucket-name/data/

Keep this path handy—you’ll pass it directly to the training job.

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3. Preparing Your Dataset for Fine-Tuning

“Garbage in, garbage out”—you’ve heard it before, but when it comes to fine-tuning PaLM 2, this isn’t just a warning—it’s a guarantee.

I’ve spent more time wrangling datasets than I’d like to admit. Here’s what I’ve learned the hard way: your fine-tuned model is only as good as the examples you give it. Half-baked formatting, inconsistent instruction phrasing, or variable output length? That’ll tank your results faster than you think.

My Preprocessing Pipeline

I usually start with raw data in CSV or JSON. Depending on the task (instruction tuning, Q&A, summarization), I normalize it into a PaLM-compatible JSONL format like this:

Instruction Tuning Format (JSONL)

{"input": "Translate the following sentence to French: 'Good morning'", "output": "Bonjour"}
{"input": "Summarize: The stock market saw...", "output": "The market declined slightly today."}

Q&A Format

{"input": "Q: What is the capital of France?\nA:", "output": "Paris"}

Summarization Format

{"input": "Text: [very long document here]", "output": "TL;DR: ..."}

You want the input-output format to stay consistent across all rows. If half your samples are phrased like “Translate X,” and the other half are “Please convert X,” you’re just introducing ambiguity.

Python Code to Generate JSONL

Here’s a quick snippet I’ve reused across several projects:

import json
import pandas as pd

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

with open("formatted_data.jsonl", "w") as f:
    for _, row in df.iterrows():
        prompt = f"{row['instruction']} {row['input']}"
        entry = {"input": prompt.strip(), "output": row["output"].strip()}
        f.write(json.dumps(entry) + "\n")

Uploading to GCS

Once your dataset’s ready, upload it to a GCS bucket:

gsutil cp formatted_data.jsonl gs://your-bucket-name/data/

You’ll need this exact GCS path when creating the tuning job in Vertex AI.

Validation Checks That Saved Me Time

Before triggering the job, here are the checks I now always run:

• Schema check – Every line should be valid JSON with only input and output keys.
• Token length analysis – I use tiktoken to ensure input+output stays within 2048 tokens for PaLM 2 tuning.
• Duplicate entries – I remove duplicates to avoid skewing training.
• Balance across task types – If I’m doing multi-task fine-tuning (say, classification + summarization), I keep the sample count reasonably balanced.

What Not to Do

Here’s the deal: mistakes during data prep are rarely obvious at first—but they always show up in your outputs later.

• ❌ Don’t mix formats. PaLM 2 learns the structure you teach it. Mixed structures confuse it.
• ❌ Don’t overstuff context. I had cases where long prompts degraded output quality drastically.
• ❌ Don’t assume your raw data is clean. Even from reliable sources, I’ve found malformed examples and typos.

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4. Choosing the Right Model Variant and Training Strategy

“Give me six hours to chop down a tree and I will spend the first four sharpening the axe.”
—Lincoln, probably talking about choosing LLM variants.

This part matters more than most people think. I’ve fine-tuned both text-bison@002 and chat-bison@001, and they behave very differently depending on your task.

When to Use Which Variant

• text-bison@002 — My go-to for any backend task where response formatting matters (e.g., structured summaries, sentence rewriting).
• chat-bison@001 — Best when you’re training for multi-turn interactions, like chatbots or tutoring systems.

Quick tip: If your fine-tuned model will only be used via single-turn API calls, stick to text-bison. It’s more controllable and less prone to drifting into conversational fluff.

Training Configs That Actually Worked for Me

Here’s the config I’ve used repeatedly with success:

training_job = aiplatform.CustomTrainingJob(
    display_name="finetune-palm2",
    model_display_name="custom-text-bison",
    dataset="gs://your-bucket/data.jsonl",
    model_type="text-bison@002",
    learning_rate=3e-5,
    batch_size=32,
    training_steps=5000,
    evaluation_strategy="steps",
    eval_steps=500
)

If you’re using gcloud, here’s the CLI equivalent:

gcloud ai models upload \
  --region=us-central1 \
  --display-name=custom-text-bison \
  --container-image-uri=us-docker.pkg.dev/vertex-ai/prediction/text-bison@002 \
  --artifact-uri=gs://your-bucket/output/

My Experience with Tuning Methods

Let me break it down:

Strategy	When I Use It	Pros	Cons
Prompt tuning	Quick iteration/testing	Fast, cheap, no infra	Doesn’t generalize well
Adapter tuning	Slight tweaks to base model	Efficient, great for niche	Can be brittle on edge cases
Full fine-tuning	Long-term production models	Strong generalization	Costly, longer training/inference

Personally, I default to adapter tuning unless I know I need fine-grained control or am building something core to the product.

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5. Fine-Tuning on Vertex AI: Step-by-Step Code

“Don’t just press the button—know what it’s doing under the hood.”
That’s something I’ve had to remind myself often when working with Vertex AI.

You might’ve seen Google’s UI for model training and thought, “Do I really need to go through the SDK or CLI?” I’ve tried both, and if you’re building repeatable workflows or anything production-adjacent, trust me—code is the only sane way forward.

Step 1: Creating the Training Pipeline

I usually rely on the google.cloud.aiplatform SDK—clean, scriptable, and easy to debug. First, you need to initialize your project and location:

from google.cloud import aiplatform

aiplatform.init(
    project="your-gcp-project-id",
    location="us-central1",  # Use the region where your model lives
    staging_bucket="gs://your-staging-bucket"
)

Step 2: Defining the Tuning Job

Here’s a full code block I’ve used to fine-tune text-bison@002. This uses the built-in tuning pipeline—not a custom training container.

from google.cloud.aiplatform.models import TextGenerationModel

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

tuned_model = model.tune_model(
    training_data="gs://your-bucket/data.jsonl",
    validation_data="gs://your-bucket/val_data.jsonl",  # Optional but highly recommended
    model_display_name="palm2-finetuned-medical-summary",
    train_steps=4000,
    learning_rate=2e-5,
    batch_size=32,
    lora_weights_mode="merged",  # Adapter tuning by default
    tuning_job_location="us-central1"
)

Note: I’ve experimented with different learning rates. 2e-5 tends to be a safe sweet spot for most instruction-tuning cases—higher values make convergence unstable.

Step 3: Monitoring the Job

You’ll get a job URL when this kicks off. I usually keep an eye on two things:

• Logs via gcloud logging read
• Vertex AI dashboard → Tuning Job → Metrics tab

This gives you real-time token accuracy and validation loss trends.

If you’re using the CLI instead, the command looks like this:

gcloud beta ai models tune-model text-bison@002 \
  --location=us-central1 \
  --training-data=gs://your-bucket/data.jsonl \
  --output-model-display-name="palm2-finetuned-summary" \
  --train-steps=4000 \
  --batch-size=32 \
  --learning-rate=2e-5

Step 4: Handling Model Checkpoints

Here’s the part that often gets missed: Vertex AI does support checkpointing, but only internally. If the job crashes, you don’t get to resume from last step manually—but the fine-tuned model does get saved if it completes 100%.

Personally, I keep an eye on quota limits and ensure the dataset is well below token limits to avoid silent failures.

Step 5: Custom Training vs Prebuilt Pipelines

This might surprise you: I’ve tried both, and for most use cases, the prebuilt tuning pipeline wins—faster to set up, cheaper, and battle-tested.

But when I needed to integrate external logic (like dynamic sampling or filtering noisy records mid-training), I had to switch to custom containers using TrainingPipeline. Here’s a basic structure if you go that route:

from google.cloud.aiplatform import CustomJob

job = CustomJob(
    display_name="custom-palm2-tuning",
    worker_pool_specs=[
        {
            "machine_spec": {
                "machine_type": "n1-standard-8",
                "accelerator_type": "NVIDIA_TESLA_V100",
                "accelerator_count": 1
            },
            "replica_count": 1,
            "container_spec": {
                "image_uri": "gcr.io/your-project/custom-training-image",
                "command": [],
                "args": []
            },
        }
    ],
)

job.run()

But again—only use this if your use case requires full control over the training loop.

Step 6: Spot Instances (If You’re Budget-Conscious)

If you’re running custom training, you can use spot instances to save cost—but keep in mind the trade-off: they can get preempted at any time.

Here’s how I set that up inside worker_pool_specs:

"scheduling": {
  "preemptible": true
}

In my experience, this setup worked fine for short training runs (< 2 hours). For anything longer or more critical, I stick with on-demand.

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6. Evaluating the Fine-Tuned PaLM 2

“Not everything that glitters is tuned gold.”
Fine-tuning doesn’t always lead to dramatic improvements — and I’ve learned that the hard way. Sometimes you get subtle, targeted gains. Other times, you just shift the problem somewhere else.

So let me walk you through how I actually evaluated my fine-tuned models.

Automatic Evaluation: The Metrics I Actually Used

I’ve used a few off-the-shelf metrics, but I’ll be honest: they only tell part of the story. Here’s what I usually include in my eval scripts:

from datasets import load_metric

bleu = load_metric("bleu")
rouge = load_metric("rouge")
bertscore = load_metric("bertscore")

predictions = ["Generated text here"]
references = [["Reference text here"]]

print("BLEU:", bleu.compute(predictions=predictions, references=references))
print("ROUGE:", rouge.compute(predictions=predictions, references=references))
print("BERTScore:", bertscore.compute(predictions=predictions, references=references, lang="en"))

Tip: BLEU is almost useless for anything not word-for-word aligned. I still include it, but I put more weight on ROUGE-L and BERTScore.

Manual Evaluation: The Part That Really Matters

Here’s the deal — manual eval has saved me from shipping underperforming models more times than I can count.

I built a quick internal Streamlit app for side-by-side comparisons:

import streamlit as st

st.title("Model Comparison")

prompt = st.text_area("Input Prompt")
col1, col2 = st.columns(2)

with col1:
    st.subheader("Base Model")
    st.write(get_base_model_output(prompt))

with col2:
    st.subheader("Fine-Tuned Model")
    st.write(get_finetuned_output(prompt))

This let my team and I rate outputs with 1–5 stars and flag hallucinations. You’d be surprised how often the fine-tuned version nails form but loses facts.

Before vs. After: Real Examples

Prompt: “Summarize this radiology report in plain English.”

Base model output:

“The examination demonstrates no acute abnormality. No pneumothorax.”

Fine-tuned output:

“The scan shows nothing serious. The lungs look normal and there’s no sign of collapse.”

That’s where fine-tuning made a difference — translating dense medical jargon into something actually readable.

But here’s a counter-example.

Prompt: “Generate a legal clause for NDA enforcement.”

Fine-tuned output hallucination:

“…this clause is enforceable in all 50 states and territories under GDPR law…”

That’s completely wrong — and it never happened before fine-tuning. So now, I always add hallucination-focused prompts in the manual eval set.

When Fine-Tuning Helped — And When It Didn’t

Fine-tuning absolutely helped when I needed consistent tone, formal language, and structured output formats. I saw clear gains in:

• Medical summarization
• Multi-turn customer service chats
• Policy explanation with strict terminology

But in open-ended creative tasks or fuzzy goals like “be more engaging,” fine-tuning often plateaued or backfired. Prompt engineering + RAG worked better there.

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7. Deploying the Fine-Tuned Model

Once I had something I was proud of, it was time to ship it. And honestly, deployment via Vertex AI is smoother than most platforms I’ve worked with — as long as you know what to expect.

Creating an Endpoint

endpoint = aiplatform.Model.upload(
    display_name="palm2-finetuned-summary",
    artifact_uri=tuned_model.uri,
    serving_container_image_uri="us-docker.pkg.dev/vertex-ai/prediction/text-bison",
).deploy(machine_type="n1-standard-4")

Yes, you can version it and manage traffic splits later too.

Versioning and Traffic Splitting

When I want to A/B test model versions, this is the flow I follow:

endpoint.update_deployed_model(
    deployed_model_id="123456",
    traffic_percentage=50
)

The cool part? You can run real-world evals against live traffic with zero downtime.

Latency Benchmarks and Inference Costs

I usually run a simple latency logger during testing:

import time

start = time.time()
response = endpoint.predict(["Your input here"])
print("Latency:", time.time() - start)

• Typical latency (n1-standard-4): ~800ms–1.2s
• Cost: Around $0.0005 per 1K tokens for fine-tuned inference vs. $0.0004 for base

You might be wondering: is that extra cost worth it?
For high-precision use cases like medical or legal summaries — absolutely. For casual chats or FAQs — probably not.

Token Usage Breakdown

Here’s a trick I use to analyze token spend:

from vertexai.preview.language_models import TextGenerationModel

model = TextGenerationModel.get_tuned_model("projects/.../locations/.../models/...")

response = model.predict("Your prompt", temperature=0.2)

print("Input Tokens:", response._prediction_metadata["tokenMetadata"]["inputTokenCount"])
print("Output Tokens:", response._prediction_metadata["tokenMetadata"]["outputTokenCount"])

These logs helped me realize that verbose prompts were burning budget fast. Trimming prompt length alone saved 15–20% in monthly inference cost.

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8. Bonus: Integrating with RAG, Agents, or Pipelines

“A model by itself is just a spark. You need the right system to turn it into fire.”

I’ve rarely deployed fine-tuned models in isolation. In most real-world use cases, they’re just one part of a larger architecture — especially when the stakes are high and you need grounding in fresh, dynamic data. Here’s how I’ve plugged PaLM 2 into bigger systems that actually do something useful.

Fine-Tuned PaLM 2 + RAG

When I needed factual grounding (legal, finance, or fast-moving domains), I wrapped the fine-tuned model with a RAG layer. It gave the model memory — and sanity.

Here’s the minimal setup I used with langchain:

from langchain.vectorstores import FAISS
from langchain.embeddings import VertexAIEmbeddings
from langchain.llms import VertexAI
from langchain.chains import RetrievalQA

vector_store = FAISS.load_local("faiss_index", VertexAIEmbeddings())
llm = VertexAI(model_name="projects/xxx/models/my-finetuned-palm")

qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=vector_store.as_retriever(),
    return_source_documents=True
)

response = qa_chain.run("What’s the new compliance update for April 2024?")
print(response)

Pro tip: Fine-tuning helped me generate more structured and domain-aware completions, but without RAG, it still hallucinated dates and numbers.

Agents (LangChain, LlamaIndex)

For dynamic workflows — think multi-step question answering or task decomposition — I wired PaLM 2 into agent chains.

I tried both LangChain agents and LlamaIndex’s query engines. Here’s a LangChain snippet that actually worked:

from langchain.agents import initialize_agent, Tool
from langchain.tools import DuckDuckGoSearchRun

search = DuckDuckGoSearchRun()
tools = [Tool(name="Search", func=search.run, description="Searches the web")]

agent = initialize_agent(
    tools,
    llm,
    agent="zero-shot-react-description",
    verbose=True
)

agent.run("Find the latest SEC filing for OpenAI and summarize it.")

That combo — fine-tuned model for tone + agent logic for reasoning — saved me from building brittle pipelines of prompts.

Batch Inference Pipelines (Airflow + Vertex AI)

In one project, I had to run fine-tuned summarization across 100K+ support tickets daily. Doing this with Airflow + Vertex AI made it painless (and cost-effective).

Here’s a simplified operator I used in a DAG:

from google.cloud.aiplatform_v1 import PredictionServiceClient

client = PredictionServiceClient()
endpoint_path = client.endpoint_path(
    project="my-project",
    location="us-central1",
    endpoint="your-endpoint-id"
)

def run_inference(input_texts):
    instances = [{"content": t} for t in input_texts]
    response = client.predict(endpoint=endpoint_path, instances=instances)
    return response.predictions

Spot instances helped me reduce cost by ~60% during nightly runs — highly recommend using accelerator_count=1 with preemptible machines for large batches.

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Final Thoughts & Real-World Use

“Tools don’t solve problems. People with the right tools do.”
I’ve used fine-tuned PaLM 2 models in production systems that handle everything from legal drafting to customer sentiment summarization. But here’s the honest truth — fine-tuning isn’t always the answer.

When Fine-Tuning Changed the Game

Here’s where it really delivered:

• Custom tone of voice. When I needed outputs that matched a client’s legal or medical brand — no prompt could get me 100% there. Fine-tuning nailed it.
• Response structure. In one pipeline, I needed JSON-style structured output with specific keys. A prompt could guide it 80% of the time. Fine-tuning brought it to 99.5%.
• Domain compression. In a use case where token budget was tight (low-latency apps), fine-tuning helped reduce prompt bloat by baking context into weights.

When It Didn’t Deliver

And here’s where I wasted compute:

• General creativity tasks. I was hoping to make a “wittier” chatbot. Fine-tuning made it verbose and inconsistent. Prompt tuning or system messages worked better.
• Factual recall. Even with domain data, fine-tuned models hallucinate. I learned the hard way: if your use case requires up-to-date or factual responses, always pair with RAG.
• Speed-sensitive workloads. Fine-tuned models sometimes had slower inference and higher cost — especially if you don’t prune input or use batching wisely.

Final Thought

So should you fine-tune? That depends.

If you need repeatable, domain-specific, structured outputs — yes, absolutely. It’s like hiring a domain expert and training them to speak like your brand.
But if you just want a few improvements around tone or creativity, save yourself the effort. Prompting + retrieval + smart orchestration gets you most of the way there.

Personally, I now treat fine-tuning as a precision tool — not a hammer. And that mindset shift has saved me thousands in training and ops cost.