Blog Post Template — All Features Showcase

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import torch
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def train_step(model, batch, optimizer):
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    # BAD
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    optimizer.zero_grad()
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    loss = model(batch).loss  # tính loss mỗi step nhưng không accumulate
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    # GOOD
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    loss = model(batch).loss / ACCUMULATION_STEPS
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    loss.backward()
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    if (step + 1) % ACCUMULATION_STEPS == 0:
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        optimizer.step()
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        optimizer.zero_grad()

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model = AutoModelForCausalLM.from_pretrained(
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    model_id,
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    attn_implementation='eager',
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    attn_implementation='flash_attention_2',
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    torch_dtype=torch.bfloat16,
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)

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# --- BOILERPLATE (thu gọn) ---
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import os
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import json
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import torch
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import logging
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from pathlib import Path
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from dataclasses import dataclass
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from torch.utils.data import DataLoader
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logging.basicConfig(level=logging.INFO)
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# --- PHẦN QUAN TRỌNG (hiển thị) ---
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@dataclass
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class TrainConfig:
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    model_id: str = "meta-llama/Llama-3-8B"
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    lr: float = 2e-4
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    batch_size: int = 4
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    max_steps: int = 1000
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def train(cfg: TrainConfig):
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    model = AutoModelForCausalLM.from_pretrained(cfg.model_id)
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    optimizer = torch.optim.AdamW(model.parameters(), lr=cfg.lr)
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    for step, batch in enumerate(DataLoader(..., batch_size=cfg.batch_size)):
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        loss = model(**batch).loss
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        loss.backward()
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        optimizer.step()
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        optimizer.zero_grad()
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        if step % 100 == 0:
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            print(f"Step {step}: loss={loss.item():.4f}")
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        if step >= cfg.max_steps:
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            break
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# --- BOILERPLATE (thu gọn) ---
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if __name__ == "__main__":
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    import argparse
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    parser = argparse.ArgumentParser()
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    parser.add_argument("--model_id", type=str)
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    parser.add_argument("--lr", type=float, default=2e-4)
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    parser.add_argument("--batch_size", type=int, default=4)
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    parser.add_argument("--max_steps", type=int, default=1000)
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    args = parser.parse_args()
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    train(TrainConfig(**vars(args)))

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package main
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import (
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    "net/http"
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    "github.com/gin-gonic/gin"
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)
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func main() {
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    r := gin.Default()
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    r.GET("/health", func(c *gin.Context) {
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        c.JSON(http.StatusOK, gin.H{"status": "ok"})
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    })
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    r.Run(":8080")
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}

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FROM python:3.12-slim
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WORKDIR /app
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COPY requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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COPY . .
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CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

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services:
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  api:
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    build: .
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    ports:
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      - "8000:8000"
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    environment:
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      - DATABASE_URL=postgresql://user:pass@db:5432/mydb
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  db:
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    image: postgres:16
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    volumes:
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      - pgdata:/var/lib/postgresql/data
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volumes:
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  pgdata: