#
Practical Exercise: GRPO with Unsloth

In this exercise, you’ll fine-tune a model with GRPO (Group Relative Policy Optimization) using Unsloth, to improve a model’s reasoning capabilities. We covered GRPO in Chapter 3.

Unsloth is a library that accelerates LLM fine-tuning, making it possible to train models faster and with less computational resources. Unsloth is plugs into TRL, so we’ll build on what we learned in the previous sections, and adapt it for Unsloth specifics.

This exercise can be run on a free Google Colab T4 GPU. For the best experience, follow along with the notebook linked above and try it out yourself.

##
Install dependencies

First, let’s install the necessary libraries. We’ll need Unsloth for the accelerated fine-tuning and vLLM for fast inference.

    pip install unsloth vllm
    pip install --upgrade pillow

##
Setting up Unsloth

Unsloth provides a class (`FastLanguageModel`) that integrates transformers with Unsloth optimizations. Let’s import it:

    from unsloth import FastLanguageModel

Now, let’s load Google’s Gemma 3 1B Instruct model and configure it for fine-tuning:

    from unsloth import FastLanguageModel
    import torch

    max_seq_length = 1024  # Can increase for longer reasoning traces
    lora_rank = 32  # Larger rank = smarter, but slower

    model, tokenizer = FastLanguageModel.from_pretrained(
        model_name="google/gemma-3-1b-it",
        max_seq_length=max_seq_length,
        load_in_4bit=True,  # False for LoRA 16bit
        fast_inference=True,  # Enable vLLM fast inference
        max_lora_rank=lora_rank,
        gpu_memory_utilization=0.6,  # Reduce if out of memory
    )

    model = FastLanguageModel.get_peft_model(
        model,
        r=lora_rank,  # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
        target_modules=[
            "q_proj",
            "k_proj",
            "v_proj",
            "o_proj",
            "gate_proj",
            "up_proj",
            "down_proj",
        ],  # Remove QKVO if out of memory
        lora_alpha=lora_rank,
        use_gradient_checkpointing="unsloth",  # Enable long context finetuning
        random_state=3407,
    )

This code loads the model in 4-bit quantization to save memory and applies LoRA (Low-Rank Adaptation) for efficient fine-tuning. The `target_modules` parameter specifies which layers of the model to fine-tune, and `use_gradient_checkpointing` enables training with longer contexts.

We won’t cover the details of LoRA in this chapter, but you can learn more in Chapter 11.

##
Data Preparation

For this exercise, we’ll use the GSM8K dataset, which contains grade school math problems. We’ll format the data to encourage the model to show its reasoning before providing an answer.

First, we will define the format of the prompts and answers:

    # Define the system prompt that instructs the model to use a specific format
    SYSTEM_PROMPT = """
    Respond in the following format:
    <reasoning>
    ...
    </reasoning>
    <answer>
    ...
    </answer>
    """

    XML_COT_FORMAT = """\
    <reasoning>
    {reasoning}
    </reasoning>
    <answer>
    {answer}
    </answer>
    """

Now, let’s prepare the dataset:

    import re
    from datasets import load_dataset, Dataset

    # Helper functions to extract answers from different formats
    def extract_xml_answer(text: str) -> str:
        answer = text.split("<answer>")[-1]
        answer = answer.split("</answer>")[0]
        return answer.strip()

    def extract_hash_answer(text: str) -> str | None:
        if "####" not in text:
            return None
        return text.split("####")[1].strip()

    # Function to prepare the GSM8K dataset
    def get_gsm8k_questions(split="train") -> Dataset:
        data = load_dataset("openai/gsm8k", "main")[split]
        data = data.map(
            lambda x: {
                "prompt": [
                    {"role": "system", "content": SYSTEM_PROMPT},
                    {"role": "user", "content": x["question"]},
                ],
                "answer": extract_hash_answer(x["answer"]),
            }
        )
        return data

    dataset = get_gsm8k_questions()

The dataset is prepared by extracting the answer from the dataset and formatting it as a string.

##
Defining Reward Functions

As we discussed in an earlier page, GRPO can use reward functions to guide the model’s learning based on verifiable criteria like length and formatting.

In this exercise, we’ll define several reward functions that encourage different aspects of good reasoning. For example, we’ll reward the model for providing an integer answer, and for following the strict format.

    # Reward function that checks if the answer is correct
    def correctness_reward_func(prompts, completions, answer, **kwargs) -> list[float]:
        responses = [completion[0]["content"] for completion in completions]
        q = prompts[0][-1]["content"]
        extracted_responses = [extract_xml_answer(r) for r in responses]
        print(
            "-" * 20,
            f"Question:\n{q}",
            f"\nAnswer:\n{answer[0]}",
            f"\nResponse:\n{responses[0]}",
            f"\nExtracted:\n{extracted_responses[0]}",
        )
        return [2.0 if r == a else 0.0 for r, a in zip(extracted_responses, answer)]

    # Reward function that checks if the answer is an integer
    def int_reward_func(completions, **kwargs) -> list[float]:
        responses = [completion[0]["content"] for completion in completions]
        extracted_responses = [extract_xml_answer(r) for r in responses]
        return [0.5 if r.isdigit() else 0.0 for r in extracted_responses]

    # Reward function that checks if the completion follows the strict format
    def strict_format_reward_func(completions, **kwargs) -> list[float]:
        pattern = r"^<reasoning>\n.*?\n</reasoning>\n<answer>\n.*?\n</answer>\n$"
        responses = [completion[0]["content"] for completion in completions]
        matches = [re.match(pattern, r) for r in responses]
        return [0.5 if match else 0.0 for match in matches]

    # Reward function that checks if the completion follows a more relaxed format
    def soft_format_reward_func(completions, **kwargs) -> list[float]:
        pattern = r"<reasoning>.*?</reasoning>\s*<answer>.*?</answer>"
        responses = [completion[0]["content"] for completion in completions]
        matches = [re.match(pattern, r) for r in responses]
        return [0.5 if match else 0.0 for match in matches]

    # Reward function that counts XML tags and penalizes extra content
    def count_xml(text) -> float:
        count = 0.0
        if text.count("<reasoning>\n") == 1:
            count += 0.125
        if text.count("\n</reasoning>\n") == 1:
            count += 0.125
        if text.count("\n<answer>\n") == 1:
            count += 0.125
            count -= len(text.split("\n</answer>\n")[-1]) * 0.001
        if text.count("\n</answer>") == 1:
            count += 0.125
            count -= (len(text.split("\n</answer>")[-1]) - 1) * 0.001
        return count

    def xmlcount_reward_func(completions, **kwargs) -> list[float]:
        contents = [completion[0]["content"] for completion in completions]
        return [count_xml(c) for c in contents]

These reward functions serve different purposes:

Reward Function | Purpose
--- | ---
`correctness_reward_func` | Rewards the model when its answer matches the correct answer
`int_reward_func` | Rewards the model for providing a numeric answer
`strict_format_reward_func` and `soft_format_reward_func` | Reward the model for following the specified format
`xmlcount_reward_func` | Rewards proper XML tag usage and penalizes extra content after the closing tags

##
Training with GRPO

Now we’ll set up the GRPO trainer with our model, tokenizer, and reward functions. This part follows the same approach as the previous exercise.

    from trl import GRPOConfig, GRPOTrainer

    max_prompt_length = 256

    training_args = GRPOConfig(
        learning_rate=5e-6,
        adam_beta1=0.9,
        adam_beta2=0.99,
        weight_decay=0.1,
        warmup_ratio=0.1,
        lr_scheduler_type="cosine",
        optim="paged_adamw_8bit",
        logging_steps=1,
        per_device_train_batch_size=1,
        gradient_accumulation_steps=1,  # Increase to 4 for smoother training
        num_generations=6,  # Decrease if out of memory
        max_prompt_length=max_prompt_length,
        max_completion_length=max_seq_length - max_prompt_length,
        # num_train_epochs = 1, # Set to 1 for a full training run
        max_steps=250,
        save_steps=250,
        max_grad_norm=0.1,
        report_to="none",  # Can use Weights & Biases
        output_dir="outputs",
    )

    trainer = GRPOTrainer(
        model=model,
        processing_class=tokenizer,
        reward_funcs=[
            xmlcount_reward_func,
            soft_format_reward_func,
            strict_format_reward_func,
            int_reward_func,
            correctness_reward_func,
        ],
        args=training_args,
        train_dataset=dataset,
    )

The `GRPOConfig` sets various hyperparameters for training:

- `use_vllm`: Enables fast inference with vLLM
- `learning_rate`: Controls how quickly the model learns
- `num_generations`: Number of completions to generate for each prompt
- `max_steps`: Total number of training steps to perform

Now let’s start the training:

    trainer.train()

Training may take some time. You might not see rewards increase immediately - it can take 150-200 steps before you start seeing improvements. Be patient!

##
Testing the Model

After training, let’s test our model to see how it performs. First, we’ll save the LoRA weights:

    model.save_lora("grpo_saved_lora")

Now, let’s test the model with a new question:

    from vllm import SamplingParams

    text = tokenizer.apply_chat_template(
        [
            {"role": "system", "content": SYSTEM_PROMPT},
            {"role": "user", "content": "Calculate pi."},
        ],
        tokenize=False,
        add_generation_prompt=True,
    )

    sampling_params = SamplingParams(
        temperature=0.8,
        top_p=0.95,
        max_tokens=1024,
    )
    output = (
        model.fast_generate(
            text,
            sampling_params=sampling_params,
            lora_request=model.load_lora("grpo_saved_lora"),
        )[0]
        .outputs[0]
        .text
    )

    print(output)

You should see that the model now follows the specified format, showing its reasoning before providing an answer.

##
Saving the Model

Unsloth provides several options for saving your fine-tuned model, but we’ll focus on the most common.

    # Save to 16-bit precision
    model.save_pretrained_merged("model", tokenizer, save_method="merged_16bit")

##
Pushing to Hugging Face Hub

We’ll push the model to the Hugging Face Hub using the `push_to_hub_merged` method. This method allows us to push the model in multiple quantization formats.

    # Push to Hugging Face Hub (requires a token)
    model.push_to_hub_merged(
        "your-username/model-name", tokenizer, save_method="merged_16bit", token="your-token"
    )

Unsloth also supports saving to GGUF format for use with llama.cpp:

    model.push_to_hub_gguf(
        "your-username/model-name",
        tokenizer,
        quantization_method=["q4_k_m", "q8_0", "q5_k_m"],
        token="your-token",
    )

The GGUF files can be used with llama.cpp or UI-based systems like Jan or Open WebUI.

##
Conclusion

In this exercise, you’ve learned how to:

1. Set up Unsloth for accelerated fine-tuning
2. Prepare data for GRPO training
3. Define custom reward functions to guide the model’s learning
4. Train a model using GRPO
5. Test the fine-tuned model
6. Save the model in various formats

GRPO is a powerful technique for aligning language models with specific behaviors, and Unsloth makes it accessible even on limited hardware. By combining multiple reward functions, you can guide the model to follow a specific format while also improving its reasoning capabilities.

For more information and resources, check out:

- Unsloth Documentation
- Unsloth Discord
- Unsloth GitHub

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