#
🚀 Instruction Fine-Tuning of Llama 3.1 8B with LoRA

This tutorial shows how to fine-tune the Llama 3.1 model on AWS Trainium accelerators using optimum-neuron.

This is based on the Llama 3.1 fine-tuning example script.

##
1. 🛠️ Setup AWS Environment

We’ll use a `trn1.32xlarge` instance with 16 Trainium Accelerators (32 Neuron Cores) and the Hugging Face Neuron Deep Learning AMI.

The Hugging Face AMI includes all required libraries pre-installed:

- `datasets`, `transformers`, `optimum-neuron`
- Neuron SDK packages
- No additional environment setup needed

To create your instance, follow the guide here.

Model Access: The Llama 3.1 model is gated and requires access approval. You can request access at meta-llama/Llama-3.1-8B. Once approved, make sure to authenticate with the Hugging Face Hub:

    huggingface-cli login

##
2. 📊 Load and Prepare the Dataset

We’ll use the Dolly dataset, an open source dataset of instruction-following records on categories outlined in the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization.

    {
      "instruction": "What is world of warcraft",
      "context": "",
      "response": (
            "World of warcraft is a massive online multi player role playing game. "
            "It was released in 2004 by bizarre entertainment"
        )
    }

To load the dataset we use the `load_dataset()` method from the `datasets` library.

    from random import randrange

    from datasets import load_dataset

    # Load dataset from the hub
    dataset_id = "databricks/databricks-dolly-15k"
    dataset = load_dataset(dataset_id, split="train")

    dataset_size = len(dataset)
    print(f"dataset size: {dataset_size}")
    # dataset size: 15011

To instruct fine-tune our model we need to convert our structured examples into collection of tasks described via instructions. We define our formatting function to preprocess the dataset.

The dataset should be structured with input-output pairs, where each input is a prompt and the output is the expected response from the model.

    def format_dolly(example, tokenizer):
        """Format Dolly dataset examples using the tokenizer's chat template."""
        user_content = example["instruction"]
        if len(example["context"]) > 0:
            user_content += f"\n\nContext: {example['context']}"

        messages = [
            {
                "role": "system",
                "content": "Cutting Knowledge Date: December 2023\nToday Date: 29 Jul 2025\n\nYou are a helpful assistant",
            },
            {"role": "user", "content": user_content},
            {"role": "assistant", "content": example["response"]},
        ]

        return tokenizer.apply_chat_template(messages, tokenize=False)

Note: this function is well-defined in the Python script to run this tutorial.

##
3. 🎯 Fine-tune Llama 3.1 with NeuronSFTTrainer and PEFT

For standard PyTorch fine-tuning, you’d typically use PEFT with LoRA adapters and the `SFTTrainer`.

On AWS Trainium, `optimum-neuron` provides `NeuronSFTTrainer` as a drop-in replacement.

Distributed Training on Trainium: Since Llama 3.1 8B doesn’t fit on a single accelerator, we use distributed training techniques:

- Data Parallel (DDP)
- Tensor Parallelism

Model loading and LoRA configuration work similarly to other accelerators.

Combining all the pieces together, and assuming the dataset has already been loaded, we can write the following code to fine-tune Llama 3.1 on AWS Trainium:

    model_id = "meta-llama/Llama-3.1-8B"

    # Define the training arguments
    output_dir = "Llama-3.1-8B-finetuned"
    training_args = NeuronTrainingArguments(
        output_dir=output_dir,
        num_train_epochs=3,
        do_train=True,
        max_steps=-1,  # -1 means train until the end of the dataset
        per_device_train_batch_size=1,
        gradient_accumulation_steps=16,
        learning_rate=1e-4,
        bf16=True,
        tensor_parallel_size=8,
        logging_steps=1,
        warmup_steps=5,
        async_save=True,
        overwrite_output_dir=True,
    )

    # Load the model with the NeuronModelForCausalLM class.
    # It will load the model with a custom modeling specifically designed for AWS Trainium.
    trn_config = training_args.trn_config
    dtype = torch.bfloat16 if training_args.bf16 else torch.float32
    model = NeuronModelForCausalLM.from_pretrained(
        model_id,
        trn_config,
        dtype=dtype,
        # Use FlashAttention2 for better performance and to be able to use larger sequence lengths.
        attn_implementation="flash_attention_2",
    )

    lora_config = LoraConfig(
        r=64,
        lora_alpha=128,
        lora_dropout=0.05,
        target_modules=["embed_tokens", "q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
        bias="none",
        task_type="CAUSAL_LM",
    )

    # Converting the NeuronTrainingArguments to a dictionary to feed them to the NeuronSFTConfig.
    args = training_args.to_dict()

    sft_config = NeuronSFTConfig(
        max_length=2048,
        packing=True,
        **args,
    )

    tokenizer = AutoTokenizer.from_pretrained(model_id)
    tokenizer.pad_token = "<|finetune_right_pad_id|>"

    # Set chat template for Llama 3.1 format
    tokenizer.chat_template = (
        "{% for message in messages %}"
        "{% if message['role'] == 'system' %}"
        "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{{ message['content'] }}<|eot_id|>"
        "{% elif message['role'] == 'user' %}"
        "<|start_header_id|>user<|end_header_id|>\n\n{{ message['content'] }}<|eot_id|>"
        "{% elif message['role'] == 'assistant' %}"
        "<|start_header_id|>assistant<|end_header_id|>\n\n{{ message['content'] }}<|eot_id|>"
        "{% endif %}"
        "{% endfor %}"
        "{% if add_generation_prompt %}"
        "<|start_header_id|>assistant<|end_header_id|>\n\n"
        "{% endif %}"
    )

    # The NeuronSFTTrainer will use `format_dolly` to format the dataset and `lora_config` to apply LoRA on the
    # model.
    trainer = NeuronSFTTrainer(
        args=sft_config,
        model=model,
        peft_config=lora_config,
        processing_class=tokenizer,
        train_dataset=dataset,
        formatting_func=lambda example: format_dolly(example, tokenizer),
    )
    trainer.train()

📝 Complete script available: All steps above are combined in a ready-to-use script finetune_llama.py.

To launch training, just run the following command in your AWS Trainium instance:

    # Flags for Neuron compilation
    export NEURON_CC_FLAGS="--model-type transformer --retry_failed_compilation"
    export NEURON_FUSE_SOFTMAX=1
    export NEURON_RT_ASYNC_EXEC_MAX_INFLIGHT_REQUESTS=3 # Async Runtime
    export MALLOC_ARENA_MAX=64 # Host OOM mitigation

    # Variables for training
    PROCESSES_PER_NODE=32
    NUM_EPOCHS=3
    TP_DEGREE=8
    BS=1
    GRADIENT_ACCUMULATION_STEPS=16
    LOGGING_STEPS=1
    MODEL_NAME="meta-llama/Llama-3.1-8B" # Change this to the desired model name
    OUTPUT_DIR="$(echo $MODEL_NAME | cut -d'/' -f2)-finetuned"
    DISTRIBUTED_ARGS="--nproc_per_node $PROCESSES_PER_NODE"

    if [ "$NEURON_EXTRACT_GRAPHS_ONLY" = "1" ]; then
        MAX_STEPS=5
    else
        MAX_STEPS=-1
    fi

    torchrun --nproc_per_node $PROCESSES_PER_NODE finetune_llama.py \
      --model_id $MODEL_NAME \
      --num_train_epochs $NUM_EPOCHS \
      --do_train \
      --max_steps $MAX_STEPS \
      --per_device_train_batch_size $BS \
      --gradient_accumulation_steps $GRADIENT_ACCUMULATION_STEPS \
      --learning_rate 1e-4 \
      --bf16 \
      --tensor_parallel_size $TP_DEGREE \
      --async_save \
      --warmup_steps 5 \
      --logging_steps $LOGGING_STEPS \
      --output_dir $OUTPUT_DIR \
      --overwrite_output_dir

🔧 Single command execution: The complete bash training script finetune_llama.sh is available:

    ./finetune_llama.sh

##
4. 🔄 Consolidate and Test the Fine-Tuned Model

Optimum Neuron saves model shards separately during distributed training. These need to be consolidated before use.

Use the Optimum CLI to consolidate:

    optimum-cli neuron consolidate Llama-3.1-8B-finetuned Llama-3.1-8B-finetuned/adapter_default

This will create an `adapter_model.safetensors` file, the LoRA adapter weights that we trained in the previous step. We can now reload the model and merge it, so it can be loaded for evaluation:

    from transformers import AutoModelForCausalLM, AutoTokenizer
    from peft import PeftModel, PeftConfig

    MODEL_NAME = "meta-llama/Llama-3.1-8B"
    ADAPTER_PATH = "Llama-3.1-8B-finetuned/adapter_default"
    MERGED_MODEL_PATH = "Llama-3.1-8B-dolly"

    # Load base model
    model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)

    # Load adapter configuration and model
    adapter_config = PeftConfig.from_pretrained(ADAPTER_PATH)
    finetuned_model = PeftModel.from_pretrained(model, ADAPTER_PATH, config=adapter_config)

    print("Saving tokenizer")
    tokenizer.save_pretrained(MERGED_MODEL_PATH)
    print("Saving model")
    finetuned_model = finetuned_model.merge_and_unload()
    finetuned_model.save_pretrained(MERGED_MODEL_PATH)

Once this step is done, it is possible to test the model with a new prompt.

You have successfully created a fine-tuned model from Llama 3.1!

##
5. 🤗 Push to Hugging Face Hub

Share your fine-tuned model with the community by uploading it to the Hugging Face Hub.

Step 1: Authentication

    huggingface-cli login

Step 2: Upload your model

    from transformers import AutoModelForCausalLM, AutoTokenizer

    MERGED_MODEL_PATH = "Llama-3.1-8B-dolly"
    HUB_MODEL_NAME = "your-username/llama3.1-8b-dolly"

    # Load and push tokenizer
    tokenizer = AutoTokenizer.from_pretrained(MERGED_MODEL_PATH)
    tokenizer.push_to_hub(HUB_MODEL_NAME)

    # Load and push model
    model = AutoModelForCausalLM.from_pretrained(MERGED_MODEL_PATH)
    model.push_to_hub(HUB_MODEL_NAME)

🎉 Your fine-tuned Llama 3.1 model is now available on the Hub for others to use!