scripts/train.py#

import argparse import torch from transformers import GPT2LMHeadModel, GPT2TokenizerFast, Trainer, TrainingArguments from datasets import load_dataset

def parse_args(): parser = argparse.ArgumentParser(description=“Fine-tune GPT-2 for text generation.”) parser.add_argument(“—train_file”, type=str, required=True, help=“Path to the training CSV.”) parser.add_argument(“—val_file”, type=str, required=True, help=“Path to the validation CSV.”) parser.add_argument(“—epochs”, type=int, default=3, help=“Number of training epochs.”) parser.add_argument(“—batch_size”, type=int, default=2, help=“Batch size.”) parser.add_argument(“—lr”, type=float, default=5e-5, help=“Learning rate.”) parser.add_argument(“—output_dir”, type=str, default=“models/checkpoints”, help=“Checkpoint path.”) return parser.parse_args()

def main(): args = parse_args() dataset = load_dataset(“csv”, data_files={“train”: args.train_file, “validation”: args.val_file}) tokenizer = GPT2TokenizerFast.from_pretrained(“gpt2”) tokenizer.pad_token = tokenizer.eos_token

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def tokenize_fn(examples):
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    texts = []
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    for c, r in zip(examples["context"], examples["reply"]):
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        text = f"<|startoftext|>{c}<|sep|>{r}<|endoftext|>"
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        texts.append(text)
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    return tokenizer(texts, truncation=True, padding="max_length", max_length=128)
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ds_tokenized = dataset.map(tokenize_fn, batched=True)
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ds_tokenized.set_format("torch", columns=["input_ids", "attention_mask"])
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train_ds = ds_tokenized["train"]
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val_ds = ds_tokenized["validation"]
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model = GPT2LMHeadModel.from_pretrained("gpt2")
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train_args = TrainingArguments(
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    output_dir=args.output_dir,
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    evaluation_strategy="epoch",
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    save_strategy="epoch",
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    num_train_epochs=args.epochs,
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    per_device_train_batch_size=args.batch_size,
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    per_device_eval_batch_size=args.batch_size,
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    learning_rate=args.lr,
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    load_best_model_at_end=True
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)
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trainer = Trainer(
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    model=model,
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    args=train_args,
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    train_dataset=train_ds,
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    eval_dataset=val_ds
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)
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trainer.train()
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trainer.save_model(args.output_dir)

if name == “main”: main()#

5.3 Fine-Tuning Considerations
• Track both training loss and validation loss to prevent overfitting.
• Adjust learning rate and batch size to find the right balance.
• Use early stopping or manually check model quality to avoid training beyond the optimal point.

6. Inference Pipeline and Deployment

6.1 Generation Script and Logic
scripts/generate.py#

import torch from transformers import GPT2LMHeadModel, GPT2TokenizerFast

MODEL_PATH = “models/checkpoints” tokenizer = GPT2TokenizerFast.from_pretrained(MODEL_PATH) model = GPT2LMHeadModel.from_pretrained(MODEL_PATH)

def generate_text(context, max_length=50): prompt = f”<|startoftext|>{context}<|sep|>” input_ids = tokenizer(prompt, return_tensors=“pt”).input_ids with torch.no_grad(): output_ids = model.generate( input_ids, max_length=max_length, num_beams=5, no_repeat_ngram_size=2, early_stopping=True ) result = tokenizer.decode(output_ids[0], skip_special_tokens=True) # Split out the text after our custom separator if ”<|sep|>” in result: result = result.split(”<|sep|>”)[1] if ”<|endoftext|>” in result: result = result.split(”<|endoftext|>”)[0] return result.strip()

if name == “main”: sample_context = “Hi, I’d like to learn about what you can do.” print(“Context:”, sample_context) print(“Generated:”, generate_text(sample_context))#

6.2 Building a Simple API
Below is a conceptual approach using FastAPI to provide a REST endpoint for text generation:

app/main.py#

from fastapi import FastAPI from pydantic import BaseModel from generate import generate_text

app = FastAPI()

class GenerateRequest(BaseModel): context: str max_length: int = 50

@app.post(“/generate”) def generate_endpoint(req: GenerateRequest): response_text = generate_text(req.context, max_length=req.max_length) return {“generated_text”: response_text}#

6.3 Containerization and Reproducibility
• requirements.txt or conda environment.yml to lock dependencies
• Dockerfile that copies the project, installs dependencies, and launches your API server

Example Dockerfile snippet:

FROM python:3.9-slim

WORKDIR /app COPY . /app RUN pip install —no-cache-dir -r requirements.txt

CMD [“uvicorn”, “app.main”, “—host”, “0.0.0.0”, “—port”, “8080”]#

You can then run:
docker build -t my_text_gen .
docker run -p 8080:8080 my_text_gen

7. Conclusion and Future Directions

Building a text generation system using a “Zero to Hero” approach—from data pre-processing and model fine-tuning to inference and deployment—demonstrates just how accessible NLP innovations have become. Even with limited resources, you can rapidly prototype and iterate on text generation ideas across diverse use cases.

Potential next steps:
• Experiment with Advanced Fine-Tuning: Incorporate parameter-efficient methods like LoRA or Adapters.
• Investigate Model Compression: Use pruning, quantization, or knowledge distillation to optimize the model for production.
• Explore Larger or Specialized Models: Scale up with GPT-Neo/BLOOM or domain-specific LLMs for more challenging tasks.
• Embrace Multi-Modal Integration: Combine text with image or speech inputs to extend your applications across multiple modalities.