4-Week AI Sprint Plan: From Machine Learning to LLM Fine-tuning

Overview

I have designed a comprehensive 4-week AI learning sprint plan focused on AI technology stack, independent of blockchain. Each week has clear learning objectives and output requirements, reinforcing theoretical knowledge through practical projects.

Week 1 — Machine Learning Fundamentals & Data Processing

Deliverables

1 notebook: Linear/Logistic regression from scratch (NumPy) + scikit-learn implementation on a public dataset
Evaluation criteria: Regression RMSE ≤ baseline*0.9; Classification F1 ≥ 0.85 (80/20 split)

Daily Schedule

Day 1: Environment setup + Python/NumPy review
Day 2: pandas data cleaning & visualization
Day 3: Implement Logistic Regression from scratch (GD/regularization)
Day 4: sklearn Pipeline/cross-validation/feature scaling/grid search
Day 5: End-to-end project on a small dataset (UCI/Wine/Titanic)

Environment & Framework

# Create new environment
python -m venv .venv && source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install -U numpy pandas scikit-learn matplotlib seaborn jupyter

# sklearn quick binary classification template
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report

X, y = ...  # Load your features and labels
X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)

clf = make_pipeline(StandardScaler(), LogisticRegression(max_iter=200))
clf.fit(X_tr, y_tr)
print(classification_report(y_te, clf.predict(X_te)))

Week 2 — PyTorch Introduction (CV/NLP)

Deliverables

1 notebook: Train a small model with PyTorch (MNIST/CIFAR10)
Evaluation criteria: MNIST Acc ≥ 98% or CIFAR10 Acc ≥ 80%

Daily Schedule

Day 1: Tensor/Autograd/optimizers
Day 2: DataLoader/Transforms
Day 3: MLP/CNN training loop
Day 4: Regularization (Dropout, Augmentation)
Day 5: Reproduction & hyperparameter tuning

Training Loop Framework

# PyTorch minimal training loop
import torch, torch.nn as nn, torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms

device = 'cuda' if torch.cuda.is_available() else 'cpu'

train_ds = datasets.MNIST(root='data', train=True, download=True,
    transform=transforms.ToTensor())
test_ds  = datasets.MNIST(root='data', train=False, download=True,
    transform=transforms.ToTensor())
train_dl = DataLoader(train_ds, batch_size=128, shuffle=True)
test_dl  = DataLoader(test_ds, batch_size=256)

class MLP(nn.Module):
    def __init__(self):
        super().__init__()
        self.net = nn.Sequential(
            nn.Flatten(),
            nn.Linear(28*28, 256), nn.ReLU(),
            nn.Linear(256, 128), nn.ReLU(),
            nn.Linear(128, 10)
        )
    def forward(self, x): return self.net(x)

model = MLP().to(device)
opt = optim.Adam(model.parameters(), lr=1e-3)
loss_fn = nn.CrossEntropyLoss()

for epoch in range(5):
    model.train()
    for xb, yb in train_dl:
        xb, yb = xb.to(device), yb.to(device)
        opt.zero_grad(); loss = loss_fn(model(xb), yb)
        loss.backward(); opt.step()
    # evaluation
    model.eval(); correct = total = 0
    with torch.no_grad():
        for xb, yb in test_dl:
            xb, yb = xb.to(device), yb.to(device)
            pred = model(xb).argmax(1)
            correct += (pred == yb).sum().item(); total += yb.size(0)
    print(f"epoch {epoch+1} acc={correct/total:.4f}")

Week 3 — NLP Fine-tuning (Small Models)

Deliverables

1 notebook: Fine-tune a small Transformer (e.g., DistilBERT) for sentiment classification/intent recognition
Evaluation criteria: F1 ≥ 0.90; Export inference.py (command-line prediction)

Daily Schedule

Day 1: Text cleaning/tokenization
Day 2: datasets & transformers training
Day 3: Early stopping/learning rate/weight decay
Day 4: Error analysis
Day 5: Package inference script

LoRA Fine-tuning Example (Memory-friendly)

pip install -U torch datasets transformers peft accelerate evaluate

from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
from peft import LoraConfig, get_peft_model
import evaluate

ds = load_dataset("sst2")  # or your own dataset
tok = AutoTokenizer.from_pretrained("distilbert-base-uncased")

def preprocess(ex):
    out = tok(ex["sentence"], truncation=True, padding="max_length", max_length=128)
    out["labels"] = ex["label"]; return out

ds = ds.map(preprocess, batched=True)
ds = ds.remove_columns(["sentence"])
metric = evaluate.load("glue", "sst2")

model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2)
lora_cfg = LoraConfig(r=8, lora_alpha=16, target_modules=["q_lin","v_lin","k_lin"], lora_dropout=0.1, bias="none")
model = get_peft_model(model, lora_cfg)

args = TrainingArguments(
    output_dir="out", per_device_train_batch_size=16, per_device_eval_batch_size=32,
    num_train_epochs=3, learning_rate=3e-4, evaluation_strategy="epoch",
    logging_steps=50, save_strategy="epoch", fp16=True
)

def compute_metrics(eval_pred):
    import numpy as np
    logits, labels = eval_pred
    preds = logits.argmax(-1)
    return metric.compute(predictions=preds, references=labels)

trainer = Trainer(model=model, args=args, train_dataset=ds["train"], eval_dataset=ds["validation"],
                  tokenizer=tok, compute_metrics=compute_metrics)
trainer.train()
trainer.save_model("ckpt")

Week 4 — Mini LLM Practice (Lightweight LoRA + Simple Demo)

Deliverables

Fine-tune a small model (e.g., TinyLlama-1.1B or Qwen2.5-0.5B) with LoRA for instruction tuning (small sample, demonstration focused).

Build a Gradio web interface: input instruction → output response
Write 1 English blog post (documenting data construction, training details, performance comparison)

Daily Schedule

Day 1: Model selection/prepare instruction data (1-5k toy data)
Day 2: LoRA training (PEFT), quantized loading (4bit/8bit)
Day 3: Gradio Demo
Day 4: Comparison & error analysis
Day 5: Blog + README

Gradio Demo Example

pip install -U gradio

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"  # or your LoRA merged weights
tok = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")

def chat(prompt):
    inputs = tok(prompt, return_tensors="pt").to(model.device)
    out = model.generate(**inputs, max_new_tokens=256, do_sample=True, temperature=0.7, top_p=0.9)
    return tok.decode(out[0], skip_special_tokens=True)

gr.Interface(fn=chat, inputs="text", outputs="text", title="Mini LLM Demo").launch()

Repository Structure (Unified Management)

ai-sprint/
├── week1_ml/
│   ├── 01_numpy_regression.ipynb
│   ├── 02_sklearn_pipeline.ipynb
├── week2_pytorch/
│   ├── mnist_train.ipynb
│   └── mnist_train.py
├── week3_nlp/
│   ├── finetune_distilbert_lora.ipynb
│   └── inference.py
├── week4_llm/
│   ├── finetune_lora.md
│   └── app_gradio.py
├── blog/
│   └── 2025-08-LLM-mini-finetune.md
└── README.md

Evaluation & Outputs (Weekly Portfolio Items)

Week 1: Tabular classification/regression baseline + model selection report
Week 2: CV small model reproduction + hyperparameter tuning records
Week 3: NLP fine-tuning + inference.py (command-line demo)
Week 4: LoRA instruction fine-tuning + Gradio Demo + English blog

Execution Strategy

Daily Check-in: Complete daily tasks and record learning notes
Weekly Review: Check weekly goal completion and adjust next week's plan
Project Accumulation: Organize each project into showcaseable works
Technical Blogging: Document learning process and problem solutions

Expected Outcomes

Master complete AI tech stack: from traditional ML to modern deep learning
Gain practical project experience: showcaseable results at each stage
Build learning system: foundation for future AI development
Improve engineering capabilities: complete loop from theory to practice

This plan is both practical and challenging. I believe there will be significant progress after 4 weeks!