#
🚀 Fine-Tune Qwen3 8B with LoRA

This tutorial shows how to fine-tune the Qwen3 model on AWS Trainium accelerators using optimum-neuron.

This is based on the Qwen3 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.

##
2. 📊 Load and Prepare the Dataset

We’ll use the simple recipes dataset to fine-tune our model for recipe generation.

    {
        'recipes': "- Preheat oven to 350 degrees\n- Butter two 9x5' loaf pans\n- Cream the sugar and the butter until light and whipped\n- Add the bananas, eggs, lemon juice, orange rind\n- Beat until blended uniformly\n- Be patient, and beat until the banana lumps are gone\n- Sift the dry ingredients together\n- Fold lightly and thoroughly into the banana mixture\n- Pour the batter into prepared loaf pans\n- Bake for 45 to 55 minutes, until the loaves are firm in the middle and the edges begin to pull away from the pans\n- Cool the loaves on racks for 30 minutes before removing from the pans\n- Freezes well",
        'names': 'Beat this banana bread'
    }

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 = "tengomucho/simple_recipes"
    recipes = load_dataset(dataset_id, split="train")

    dataset_size = len(recipes)
    print(f"dataset size: {dataset_size}")
    print(recipes[randrange(dataset_size)])
    # dataset size: 20000

To tune our model we need to convert our structured examples into a collection of quotes with a given context, so we define our tokenization function that we will be able to map on 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. We will make use of the model’s tokenizer chat template and preprocess the dataset to be fed to the trainer.

    # Preprocesses the dataset
    def preprocess_dataset_with_eos(eos_token):
        def preprocess_function(examples):
            recipes = examples["recipes"]
            names = examples["names"]

            chats = []
            for recipe, name in zip(recipes, names):
                # Append the EOS token to the response
                recipe += eos_token

                chat = [
                    {"role": "user", "content": f"How can I make {name}?"},
                    {"role": "assistant", "content": recipe},
                ]

                chats.append(chat)
            return {"messages": chats}

        dataset = recipes.map(preprocess_function, batched=True, remove_columns=recipes.column_names)
        return dataset

    # Structures the dataset into prompt-expected output pairs.
    def formatting_function(examples):
        return tokenizer.apply_chat_template(examples["messages"], tokenize=False, add_generation_prompt=False)

Note: these functions make references of `eos_token` and `tokenizer`, they are well-defined in the Python script to run this tutorial.

##
3. 🎯 Fine-tune Qwen3 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 Qwen3 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 Qwen3 on AWS Trainium:

    model_id = "Qwen/Qwen3-8B"

    # Define the training arguments
    output_dir = "qwen3-finetuned-recipes"
    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=8,
        learning_rate=5e-4,
        bf16=True,
        tensor_parallel_size=8,
        logging_steps=2,
        lr_scheduler_type="cosine",
        overwrite_output_dir=True,
    )

    # Load the model with the NeuronModelForCausalLM class.
    # It will load the model with a custom modeling speficically 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,
        torch_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",
            "v_proj",
            "o_proj",
            "k_proj",
            "up_proj",
            "down_proj",
            "gate_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_seq_length=4096,
        packing=True,
        **args,
    )

    tokenizer = AutoTokenizer.from_pretrained(model_id)
    dataset = preprocess_dataset_with_eos(tokenizer.eos_token)

     def formatting_function(examples):
         return tokenizer.apply_chat_template(examples["messages"], tokenize=False, add_generation_prompt=False)

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

📝 Complete script available: All steps above are combined in a ready-to-use script finetune_qwen3.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=8
    LOGGING_STEPS=2
    MODEL_NAME="Qwen/Qwen3-8B" # Change this to the desired model name
    OUTPUT_DIR="$(echo $MODEL_NAME | cut -d'/' -f2)-finetuned"
    DISTRIBUTED_ARGS="--nproc_per_node $PROCESSES_PER_NODE"
    SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )

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

    torchrun --nproc_per_node $PROCESSES_PER_NODE finetune_qwen3.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 8e-4 \
      --bf16 \
      --tensor_parallel_size $TP_DEGREE \
      --zero_1 \
      --async_save \
      --logging_steps $LOGGING_STEPS \
      --output_dir $OUTPUT_DIR \
      --lr_scheduler_type "cosine" \
      --overwrite_output_dir

🔧 Single command execution: The complete bash training script finetune_qwen3.sh is available:

    ./finetune_qwen3.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 Qwen3-8B-finetuned Qwen3-8B-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 = "Qwen/Qwen3-8B"
    ADAPTER_PATH = "Qwen3-8B-finetuned/adapter_default"
    MERGED_MODEL_PATH = "Qwen3-8B-recipes"

    # 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 Qwen3!

##
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 = "Qwen3-8B-recipes"
    HUB_MODEL_NAME = "your-username/qwen3-8b-recipes"

    # 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 Qwen3 model is now available on the Hub for others to use!