Use transformers.Trainer or trlx for fine‑tuning

Once your instruction dataset is ready, it’s time to train your base model to act like your ideal assistant. You can do this with two popular open‑source tools:

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✅ Option 1: transformers.Trainer

When to use: ✔️ Best for supervised fine‑tuning (SFT) — teaching the model to map instructions ➜ responses. ✔️ Simple to set up with transformers + datasets. ✔️ Works on CPUs, single GPUs, or multi‑GPU with accelerate.

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🗂️ Basic Trainer Workflow

1️⃣ Prepare the Dataset Load your .jsonl file and format it for training.

from datasets import load_dataset

dataset = load_dataset("json", data_files="my_instructions.jsonl")

print(dataset)

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2️⃣ Tokenize Use the same tokenizer as your base model.

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("gpt2")

def tokenize_function(example):
    prompt = f"{example['instruction']}\n{example['input']}"
    target = example["output"]
    full_text = prompt + "\n" + target

    return tokenizer(
        full_text,
        truncation=True,
        max_length=512
    )

tokenized_dataset = dataset.map(tokenize_function, batched=True)

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3️⃣ Set Up the Model

from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("gpt2")

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4️⃣ Configure Training

from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=2,
    warmup_steps=50,
    weight_decay=0.01,
    logging_dir="./logs",
    logging_steps=10,
    save_strategy="epoch",
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset["train"],
)

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5️⃣ Run Training

trainer.train()

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✔️ After training, save your fine‑tuned model:

trainer.save_model("./my-finetuned-assistant")

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✅ Option 2: trlx for Reinforcement Learning

When to use: ✔️ If you want to push your assistant beyond SFT. ✔️ trlx = Reinforcement Learning from Human Feedback (RLHF). ✔️ Adds a reward model + policy training to fine‑tune for preferred behaviors.

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🧩 What’s trlx?

• Developed by CarperAI / EleutherAI community.

• Combines transformers with reinforcement learning.

• Takes your SFT model and improves it with reward signals for good answers.

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⚙️ Basic trlx Steps

1️⃣ Fine‑tune with Trainer first to get a strong SFT base. 2️⃣ Switch to trlx:

• Define a reward function (e.g., prefer short, polite, helpful responses).

• Train the policy with trlx train.

Example:

pip install trlx

Minimal config (in code or YAML) → run:

import trlx

trlx.train(...)

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✅ When Should You Use trlx?

• For advanced projects: aligning your assistant with nuanced user preferences.

• If you want a human-in-the-loop feedback loop.

• If you plan to deploy your model publicly.

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✅ Key Takeaway

Start with transformers.Trainer for clear, predictable fine‑tuning. Use trlx only if you want to push your assistant’s behavior with reward learning.

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➡️ Next: After fine‑tuning, you’ll test your improved assistant, then learn how to add retrieval or RAG for better answers!