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	<link rel="modulepreload" href="/docs/optimum.neuron/v0.4.4/en/_app/immutable/chunks/CodeBlock.58e3e98b.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{"title":"🚀 Instruction Fine-Tuning of Llama 3.1 8B with LoRA","local":"-instruction-fine-tuning-of-llama-31-8b-with-lora","sections":[{"title":"1. 🛠️ Setup AWS Environment","local":"1--setup-aws-environment","sections":[],"depth":2},{"title":"2. 📊 Load and Prepare the Dataset","local":"2--load-and-prepare-the-dataset","sections":[],"depth":2},{"title":"3. 🎯 Fine-tune Llama 3.1 with NeuronSFTTrainer and PEFT","local":"3--fine-tune-llama-31-with-neuronsfttrainer-and-peft","sections":[],"depth":2},{"title":"4. 🔄 Consolidate and Test the Fine-Tuned Model","local":"4--consolidate-and-test-the-fine-tuned-model","sections":[],"depth":2},{"title":"5. 🤗 Push to Hugging Face Hub","local":"5--push-to-hugging-face-hub","sections":[],"depth":2}],"depth":1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <div class="items-center 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class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#-instruction-fine-tuning-of-llama-31-8b-with-lora"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>🚀 Instruction Fine-Tuning of Llama 3.1 8B with LoRA</span></h1> <p data-svelte-h="svelte-1mnesz9">This tutorial shows how to fine-tune the Llama 3.1 model on AWS Trainium accelerators using optimum-neuron.</p> <p data-svelte-h="svelte-1e1022f"><strong>This is based on the <a href="https://github.com/huggingface/optimum-neuron/tree/main/examples/training/llama" rel="nofollow">Llama 3.1 fine-tuning example script</a>.</strong></p> <h2 class="relative group"><a id="1--setup-aws-environment" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#1--setup-aws-environment"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>1. 🛠️ Setup AWS Environment</span></h2> <p data-svelte-h="svelte-43r1bv">We’ll use a <code>trn1.32xlarge</code> instance with 16 Trainium Accelerators (32 Neuron Cores) and the Hugging Face Neuron Deep Learning AMI.</p> <p data-svelte-h="svelte-1ktungo">The Hugging Face AMI includes all required libraries pre-installed:</p> <ul data-svelte-h="svelte-1efvabb"><li><code>datasets</code>, <code>transformers</code>, <code>optimum-neuron</code></li> <li>Neuron SDK packages</li> <li>No additional environment setup needed</li></ul> <p data-svelte-h="svelte-1gchww4">To create your instance, follow the guide <a href="https://huggingface.co/docs/optimum-neuron/ec2-setup" rel="nofollow">here</a>.</p> <p data-svelte-h="svelte-9yn5gx"><strong>Model Access:</strong> The Llama 3.1 model is gated and requires access approval. You can request access at <a href="https://huggingface.co/meta-llama/Llama-3.1-8B" rel="nofollow">meta-llama/Llama-3.1-8B</a>. Once approved, make sure to authenticate with the Hugging Face Hub:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START -->huggingface-cli login<!-- HTML_TAG_END --></pre></div> <h2 class="relative group"><a id="2--load-and-prepare-the-dataset" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#2--load-and-prepare-the-dataset"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>2. 📊 Load and Prepare the Dataset</span></h2> <p data-svelte-h="svelte-12qhil6">We’ll use the <a href="https://huggingface.co/datasets/databricks/databricks-dolly-15k" rel="nofollow">Dolly</a> dataset, an open source dataset of instruction-following records on categories outlined in the <a href="https://arxiv.org/abs/2203.02155" rel="nofollow">InstructGPT paper</a>, including brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START -->{
	<span class="hljs-comment">"instruction"</span>: <span class="hljs-comment">"What is world of warcraft"</span>,
	<span class="hljs-comment">"context"</span>: <span class="hljs-comment">""</span>,
	<span class="hljs-comment">"response"</span>: (
	<span class="hljs-comment">"World of warcraft is a massive online multi player role playing game. "</span>
	<span class="hljs-comment">"It was released in 2004 by bizarre entertainment"</span>
	)
	}<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-25b86y">To load the dataset we use the <code>load_dataset()</code> method from the <code>datasets</code> library.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> random <span class="hljs-keyword">import</span> randrange

	<span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset


	<span class="hljs-comment"># Load dataset from the hub</span>
	dataset_id = <span class="hljs-string">"databricks/databricks-dolly-15k"</span>
	dataset = load_dataset(dataset_id, split=<span class="hljs-string">"train"</span>)

	dataset_size = <span class="hljs-built_in">len</span>(dataset)
	<span class="hljs-built_in">print</span>(<span class="hljs-string">f"dataset size: <span class="hljs-subst">{dataset_size}</span>"</span>)
	<span class="hljs-comment"># dataset size: 15011</span><!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-w0b2m2">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.</p> <p data-svelte-h="svelte-1w4wk9l">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.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-keyword">def</span> <span class="hljs-title function_">format_dolly</span>(<span class="hljs-params">example, tokenizer</span>):
	<span class="hljs-string">"""Format Dolly dataset examples using the tokenizer's chat template."""</span>
	user_content = example[<span class="hljs-string">"instruction"</span>]
	<span class="hljs-keyword">if</span> <span class="hljs-built_in">len</span>(example[<span class="hljs-string">"context"</span>]) > <span class="hljs-number">0</span>:
	user_content += <span class="hljs-string">f"\n\nContext: <span class="hljs-subst">{example[<span class="hljs-string">'context'</span>]}</span>"</span>

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

	<span class="hljs-keyword">return</span> tokenizer.apply_chat_template(messages, tokenize=<span class="hljs-literal">False</span>)<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-1y9evj3">Note: this function is well-defined in the <a href="https://github.com/huggingface/optimum-neuron/blob/main/examples/training/llama/finetune_llama.py" rel="nofollow">Python script</a> to run this tutorial.</p> <h2 class="relative group"><a id="3--fine-tune-llama-31-with-neuronsfttrainer-and-peft" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#3--fine-tune-llama-31-with-neuronsfttrainer-and-peft"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>3. 🎯 Fine-tune Llama 3.1 with NeuronSFTTrainer and PEFT</span></h2> <p data-svelte-h="svelte-1iw1mwz">For standard PyTorch fine-tuning, you’d typically use <a href="https://github.com/huggingface/peft" rel="nofollow">PEFT</a> with LoRA adapters and the <a href="https://huggingface.co/docs/trl/en/sft_trainer" rel="nofollow"><code>SFTTrainer</code></a>.</p> <p data-svelte-h="svelte-dh3p5f">On AWS Trainium, <code>optimum-neuron</code> provides <code>NeuronSFTTrainer</code> as a drop-in replacement.</p> <p data-svelte-h="svelte-17bz54a"><strong>Distributed Training on Trainium:</strong>
	Since Llama 3.1 8B doesn’t fit on a single accelerator, we use distributed training techniques:</p> <ul data-svelte-h="svelte-1iqb34b"><li>Data Parallel (DDP)</li> <li>Tensor Parallelism</li></ul> <p data-svelte-h="svelte-1xxdciy">Model loading and LoRA configuration work similarly to other accelerators.</p> <p data-svelte-h="svelte-nfzf7p">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:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START -->model_id = <span class="hljs-string">"meta-llama/Llama-3.1-8B"</span>

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

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

	lora_config = LoraConfig(
	r=<span class="hljs-number">64</span>,
	lora_alpha=<span class="hljs-number">128</span>,
	lora_dropout=<span class="hljs-number">0.05</span>,
	target_modules=[<span class="hljs-string">"embed_tokens"</span>, <span class="hljs-string">"q_proj"</span>, <span class="hljs-string">"k_proj"</span>, <span class="hljs-string">"v_proj"</span>, <span class="hljs-string">"o_proj"</span>, <span class="hljs-string">"gate_proj"</span>, <span class="hljs-string">"up_proj"</span>, <span class="hljs-string">"down_proj"</span>],
	bias=<span class="hljs-string">"none"</span>,
	task_type=<span class="hljs-string">"CAUSAL_LM"</span>,
	)

	<span class="hljs-comment"># Converting the NeuronTrainingArguments to a dictionary to feed them to the NeuronSFTConfig.</span>
	args = training_args.to_dict()

	sft_config = NeuronSFTConfig(
	max_length=<span class="hljs-number">2048</span>,
	packing=<span class="hljs-literal">True</span>,
	**args,
	)

	tokenizer = AutoTokenizer.from_pretrained(model_id)
	tokenizer.pad_token = <span class="hljs-string">"<\|finetune_right_pad_id\|>"</span>

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

	<span class="hljs-comment"># The NeuronSFTTrainer will use `format_dolly` to format the dataset and `lora_config` to apply LoRA on the</span>
	<span class="hljs-comment"># model.</span>
	trainer = NeuronSFTTrainer(
	args=sft_config,
	model=model,
	peft_config=lora_config,
	processing_class=tokenizer,
	train_dataset=dataset,
	formatting_func=<span class="hljs-keyword">lambda</span> example: format_dolly(example, tokenizer),
	)
	trainer.train()<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-jkg1sh">📝 <strong>Complete script available:</strong> All steps above are combined in a ready-to-use script <a href="https://github.com/huggingface/optimum-neuron/blob/main/examples/training/llama/finetune_llama.py" rel="nofollow">finetune_llama.py</a>.</p> <p data-svelte-h="svelte-pvqso5">To launch training, just run the following command in your AWS Trainium instance:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-comment"># Flags for Neuron compilation</span>
	<span class="hljs-built_in">export</span> NEURON_CC_FLAGS=<span class="hljs-string">"--model-type transformer --retry_failed_compilation"</span>
	<span class="hljs-built_in">export</span> NEURON_FUSE_SOFTMAX=1
	<span class="hljs-built_in">export</span> NEURON_RT_ASYNC_EXEC_MAX_INFLIGHT_REQUESTS=3 <span class="hljs-comment"># Async Runtime</span>
	<span class="hljs-built_in">export</span> MALLOC_ARENA_MAX=64 <span class="hljs-comment"># Host OOM mitigation</span>

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

	<span class="hljs-keyword">if</span> [ <span class="hljs-string">"<span class="hljs-variable">$NEURON_EXTRACT_GRAPHS_ONLY</span>"</span> = <span class="hljs-string">"1"</span> ]; <span class="hljs-keyword">then</span>
	MAX_STEPS=5
	<span class="hljs-keyword">else</span>
	MAX_STEPS=-1
	<span class="hljs-keyword">fi</span>

	torchrun --nproc_per_node <span class="hljs-variable">$PROCESSES_PER_NODE</span> finetune_llama.py \
	--model_id <span class="hljs-variable">$MODEL_NAME</span> \
	--num_train_epochs <span class="hljs-variable">$NUM_EPOCHS</span> \
	--do_train \
	--max_steps <span class="hljs-variable">$MAX_STEPS</span> \
	--per_device_train_batch_size <span class="hljs-variable">$BS</span> \
	--gradient_accumulation_steps <span class="hljs-variable">$GRADIENT_ACCUMULATION_STEPS</span> \
	--learning_rate 1e-4 \
	--bf16 \
	--tensor_parallel_size <span class="hljs-variable">$TP_DEGREE</span> \
	--async_save \
	--warmup_steps 5 \
	--logging_steps <span class="hljs-variable">$LOGGING_STEPS</span> \
	--output_dir <span class="hljs-variable">$OUTPUT_DIR</span> \
	--overwrite_output_dir<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-1mvw2gf">🔧 <strong>Single command execution:</strong> The complete bash training script <a href="https://github.com/huggingface/optimum-neuron/blob/main/examples/training/llama/finetune_llama.sh" rel="nofollow">finetune_llama.sh</a> is available:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START -->./finetune_llama.sh<!-- HTML_TAG_END --></pre></div> <h2 class="relative group"><a id="4--consolidate-and-test-the-fine-tuned-model" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#4--consolidate-and-test-the-fine-tuned-model"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>4. 🔄 Consolidate and Test the Fine-Tuned Model</span></h2> <p data-svelte-h="svelte-46jexq">Optimum Neuron saves model shards separately during distributed training. These need to be consolidated before use.</p> <p data-svelte-h="svelte-9a9g75">Use the Optimum CLI to consolidate:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START -->optimum-cli neuron consolidate Llama-3.1-8B-finetuned Llama-3.1-8B-finetuned/adapter_default<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-1cigzjn">This will create an <code>adapter_model.safetensors</code> 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:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, AutoTokenizer
	<span class="hljs-keyword">from</span> peft <span class="hljs-keyword">import</span> PeftModel, PeftConfig


	MODEL_NAME = <span class="hljs-string">"meta-llama/Llama-3.1-8B"</span>
	ADAPTER_PATH = <span class="hljs-string">"Llama-3.1-8B-finetuned/adapter_default"</span>
	MERGED_MODEL_PATH = <span class="hljs-string">"Llama-3.1-8B-dolly"</span>

	<span class="hljs-comment"># Load base model</span>
	model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
	tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)

	<span class="hljs-comment"># Load adapter configuration and model</span>
	adapter_config = PeftConfig.from_pretrained(ADAPTER_PATH)
	finetuned_model = PeftModel.from_pretrained(model, ADAPTER_PATH, config=adapter_config)

	<span class="hljs-built_in">print</span>(<span class="hljs-string">"Saving tokenizer"</span>)
	tokenizer.save_pretrained(MERGED_MODEL_PATH)
	<span class="hljs-built_in">print</span>(<span class="hljs-string">"Saving model"</span>)
	finetuned_model = finetuned_model.merge_and_unload()
	finetuned_model.save_pretrained(MERGED_MODEL_PATH)<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-vght15">Once this step is done, it is possible to test the model with a new prompt.</p> <p data-svelte-h="svelte-1s2sm2h">You have successfully created a fine-tuned model from Llama 3.1!</p> <h2 class="relative group"><a id="5--push-to-hugging-face-hub" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#5--push-to-hugging-face-hub"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>5. 🤗 Push to Hugging Face Hub</span></h2> <p data-svelte-h="svelte-a60mly">Share your fine-tuned model with the community by uploading it to the Hugging Face Hub.</p> <p data-svelte-h="svelte-z9yml0"><strong>Step 1: Authentication</strong></p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START -->huggingface-cli login<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-h2brza"><strong>Step 2: Upload your model</strong></p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, AutoTokenizer

	MERGED_MODEL_PATH = <span class="hljs-string">"Llama-3.1-8B-dolly"</span>
	HUB_MODEL_NAME = <span class="hljs-string">"your-username/llama3.1-8b-dolly"</span>

	<span class="hljs-comment"># Load and push tokenizer</span>
	tokenizer = AutoTokenizer.from_pretrained(MERGED_MODEL_PATH)
	tokenizer.push_to_hub(HUB_MODEL_NAME)

	<span class="hljs-comment"># Load and push model</span>
	model = AutoModelForCausalLM.from_pretrained(MERGED_MODEL_PATH)
	model.push_to_hub(HUB_MODEL_NAME)<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-1okzo1f">🎉 <strong>Your fine-tuned Llama 3.1 model is now available on the Hub for others to use!</strong></p> <p></p>

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