Alpaca vs Phi-3 for Fine-Tuning

In this blog post Alpaca vs Phi-3 for Instruction Fine-Tuning in Practice we will unpack the trade-offs between these two popular paths to instruction-tuned models, show practical steps to fine-tune them, and help you choose the right option for your team.

Instruction tuning teaches a general language model to follow human-written tasks (“Write a summary”, “Generate SQL”) reliably. Alpaca popularised low-cost instruction-tuning on top of a 7B base model. Phi-3 represents a new generation of small language models (SLMs) engineered for efficient reasoning and high utility per parameter. This post keeps things practical: a high-level comparison first, then concrete steps and code.

High-level overview

Alpaca is a recipe: start with a capable base model (originally LLaMA-7B), fine-tune it on a curated set of instruction–response pairs (about 52k), and get a model that follows prompts pretty well for its size. It proved you could get strong instruction-following performance with modest compute using methods like LoRA.

Phi-3 is a family of small language models trained by Microsoft on high-quality, reasoning-focused data. Out of the box, Phi-3 models come with strong instruction-following and reasoning capabilities and can be efficiently fine-tuned for domain tasks. They aim to deliver better accuracy-per-dollar and lower latency than older 7B baselines.

The technology behind instruction tuning

Transformer decoder models: Both Alpaca-style and Phi-3 models are decoder-only transformers. They predict the next token conditioned on the prompt.
Supervised fine-tuning (SFT): We show the model many examples of (instruction, optional input) → (ideal response). This aligns behaviour to follow tasks.
Adapters with LoRA/QLoRA: Instead of updating all weights, we train small low-rank adapter matrices on quantized base weights. This slashes GPU memory while preserving quality.
Formatting and prompting: Consistent prompt templates, chat roles, and system messages are crucial. Instruction models can be brittle to format drift.
Evaluation loops: After fine-tuning, evaluate with held-out tasks, spot-check for factuality and safety, and iterate.

What is Alpaca, really?

Alpaca is a Stanford research project that fine-tuned the original LLaMA-7B on ~52k instruction–response pairs generated via a larger model. The appeal was its simplicity and cost efficiency. Key points:

Base model: Originally LLaMA-7B (older architecture and license constraints). Many modern reproductions use Llama 2/3 or open-llama variants.
Data: Short, diverse instructions. Great for general instruction-following; limited on complex reasoning.
Method: LoRA adapters on top of the base with a simple prompt template.
Pros: Extremely accessible recipe; easy to replicate; runs on commodity GPUs.
Cons: Results depend heavily on the base model; older Alpaca stacks may lag in safety, reasoning, and license suitability for commercial use. Check base-model terms.

What is Phi-3?

Phi-3 is Microsoft’s small language model family, engineered to be compact but strong at reasoning and instruction following. It’s trained on high-quality, curated and synthetic data emphasizing correctness, explanations, and alignment. Highlights:

Sizes: Multiple sizes (e.g., “mini” class around a few billion parameters). Good fit for edge and low-latency server inference.
Quality focus: Emphasis on textbook-quality and safety-aware data, yielding robust out-of-the-box behavior.
Efficiency: Strong accuracy-per-parameter and low memory footprint; ideal for QLoRA fine-tunes.
Availability: Offered through common hubs and cloud catalogs. Review model-specific licensing and usage terms for your deployment context.

Head-to-head comparison

Data quality: Alpaca’s original dataset is simple and synthetic; it may require augmentation for domain depth. Phi-3’s training corpus emphasizes reasoning and safety, often reducing the need for large fine-tune sets.
Performance per parameter: Modern Phi-3 variants typically outperform older 7B Alpaca-style models on reasoning-heavy tasks at similar or smaller sizes.
Latency and cost: Phi-3’s small sizes fine-tune and serve cheaply (especially with 4-bit quantization). An Alpaca stack on older 7B bases may need more VRAM and still underperform.
Safety and alignment: Phi-3 benefits from curated data and alignment; Alpaca-style models depend on your data sanitation and the base model’s guardrails.
Ecosystem: Alpaca is a recipe you can apply to many bases (Llama 2/3, Mistral). Phi-3 has an emerging ecosystem with good support in popular tooling.
Licensing: Alpaca itself is a method; your actual license comes from the base model and data. Phi-3 has model-specific terms; verify commercial usage rights before shipping.

When to choose one over the other

Choose Alpaca-style if: You want a reproducible, transparent SFT recipe on a base you already vetted (e.g., Llama 2/3), you need full control over data and prompting, and you accept to build your own guardrails.
Choose Phi-3 if: You want strong default reasoning and efficient inference, plan to deploy on modest GPUs or edge, and prefer starting from a modern, safety-aware SLM with smaller fine-tuning demands.

Practical fine-tuning steps (applies to both)

Define your goals: Which tasks, constraints, and success metrics (accuracy, latency, memory)?
Assemble data: Start with an instruction dataset (e.g., Alpaca format). Add domain examples and counterexamples (edge cases). Balance breadth and depth.
Choose a base: A modern, instruction-capable base saves time. If you need 7B+, consider newer architectures; otherwise Phi-3 “mini”-class can be plenty.
Pick a prompt template: Consistency matters. Use a stable chat format for both training and inference.
Train with QLoRA: 4-bit quantization + LoRA adapters keeps VRAM low with minimal quality loss.
Evaluate: Use a held-out set; measure exact matches, BLEU/ROUGE for text tasks, and human spot-checks for correctness and tone.
Iterate: Patch data holes, adjust templates, tune hyperparameters (rank, alpha, learning rate).
Harden: Add safety filters, constrain output where needed, and add monitoring.

Minimal code: Phi-3 QLoRA SFT

# pip install -U transformers datasets peft accelerate bitsandbytes trl

import torch
from datasets import load_dataset
from transformers import (AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig)
from peft import LoraConfig
from trl import SFTTrainer, SFTTrainingArguments

model_id = "microsoft/Phi-3-mini-4k-instruct"  # Check license/terms

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    quantization_config=bnb_config,
)

tokenizer = AutoTokenizer.from_pretrained(model_id)

dataset = load_dataset("tatsu-lab/alpaca", split="train")  # replace with your data

# Format: simple instruction → response. Keep template consistent.
def format_example(ex):
    instr = ex.get("instruction", "")
    input_ = ex.get("input", "").strip()
    output = ex.get("output", "")
    if input_:
        prompt = f"<|user|>\n{instr}\n\n{input_}\n<|assistant|>\n"
    else:
        prompt = f"<|user|>\n{instr}\n<|assistant|>\n"
    return {"text": prompt + output}

train_data = dataset.map(format_example, remove_columns=dataset.column_names)

peft_config = LoraConfig(
    r=16, lora_alpha=32, lora_dropout=0.05, target_modules=["q_proj","v_proj"], bias="none"
)

args = SFTTrainingArguments(
    output_dir="./phi3-instruct-lora",
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    num_train_epochs=2,
    learning_rate=2e-4,
    bf16=True,
    logging_steps=20,
    save_steps=500,
    optim="paged_adamw_8bit",
)

trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    peft_config=peft_config,
    args=args,
    train_dataset=train_data,
    dataset_text_field="text",
)

trainer.train()

# Save adapter
trainer.model.save_pretrained("./phi3-instruct-lora/adapter")

Minimal code: Alpaca-style SFT on a Llama base

Many teams use the Alpaca recipe on a modern Llama base (e.g., Llama 2/3) for better licenses and quality than the original LLaMA-7B. Replace the model ID with one you’re approved to use.

# pip install -U transformers datasets peft accelerate bitsandbytes trl

import torch
from datasets import load_dataset
from transformers import (AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig)
from peft import LoraConfig
from trl import SFTTrainer, SFTTrainingArguments

model_id = "meta-llama/Llama-2-7b-hf"  # Accept license on HF before use

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    quantization_config=bnb_config,
)

tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)

alpaca = load_dataset("tatsu-lab/alpaca", split="train")

def alpaca_format(ex):
    instr, inp, out = ex["instruction"], ex.get("input",""), ex["output"]
    prompt = (
        "### Instruction:\n" + instr + "\n\n" +
        ("### Input:\n" + inp + "\n\n" if inp else "") +
        "### Response:\n"
    )
    return {"text": prompt + out}

train_data = alpaca.map(alpaca_format, remove_columns=alpaca.column_names)

peft_config = LoraConfig(r=16, lora_alpha=32, lora_dropout=0.05, target_modules=["q_proj","v_proj"])

args = SFTTrainingArguments(
    output_dir="./llama2-alpaca-lora",
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    num_train_epochs=2,
    learning_rate=2e-4,
    bf16=True,
)

trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    peft_config=peft_config,
    args=args,
    train_dataset=train_data,
    dataset_text_field="text",
)

trainer.train()
trainer.model.save_pretrained("./llama2-alpaca-lora/adapter")

Hardware and cost notes

VRAM: Phi-3 “mini” QLoRA fine-tunes comfortably on a single 8–16 GB GPU. A 7B Llama base often prefers 16–24 GB for smoother throughput.
Throughput: 4-bit quantization and gradient accumulation keep costs low with minimal quality trade-offs.
Serving: Phi-3 “mini” can hit sub-50 ms/token on modest GPUs. Quantized 7B models can also serve quickly but may require more memory.

Evaluation, safety, and reliability

Task accuracy: Construct a held-out set aligned to your real user prompts. Track exact match, ROUGE/BLEU, and latency.
Behavioral checks: Red-team for jailbreaks, harmful content, and data leakage. Add rule-based or model-based filters if needed.
Regression tests: Save prompts that broke previous versions; run them in CI before every release.
Human-in-the-loop: For critical use-cases (e.g., healthcare, finance), require human review and detailed logging.

Deployment tips

Keep the training and inference prompt template identical.
Export adapters separately; merge only when you need a single artifact.
Use half-precision or 4-bit for serving to fit tighter memory budgets.
Add simple guardrails: max output tokens, stop sequences, and content filters.
Monitor drift: Track acceptance rates, objectionable content flags, and response length distributions over time.

Decision checklist

If you need strong reasoning at low cost and quick time-to-value: start with Phi-3 and light QLoRA.
If you have a vetted 7B+ base model license and want full control over data/prompting: Alpaca-style SFT is solid and predictable.
If latency and memory are tight (edge/CPU/GPU-lite): Phi-3 “mini” class is often the easiest path.
If you must align to a specific enterprise policy framework: pick the base with the clearest license and responsible AI posture, then fine-tune.

Conclusion

Alpaca made instruction fine-tuning accessible; Phi-3 makes high-quality, efficient instruction models practical for production. If you’re starting fresh and want the best accuracy-per-dollar, Phi-3 is a great default. If you already have a licensed Llama stack and a strong MLOps pipeline, the Alpaca recipe remains a reliable, transparent approach. In both cases, success hinges on your data quality, prompt consistency, and a tight evaluation loop.

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