import{s as Yt,n as Ht,o as $t}from"../chunks/scheduler.56725da7.js";import{S as Lt,i as zt,e as M,s as a,c as p,h as qt,a as i,d as e,b as n,f as kt,g as T,j as o,k as vt,l as Dt,m as s,n as U,t as r,o as m,p as J}from"../chunks/index.18a26576.js";import{C as Pt}from"../chunks/CopyLLMTxtMenu.cb10b98b.js";import{C as u}from"../chunks/CodeBlock.e5842034.js";import{H as Ul}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.d674d2fe.js";function Ot(Ut){let y,Jl,rl,ul,c,yl,d,cl,j,rt="This tutorial shows how to fine-tune the Llama 3.1 model on AWS Trainium accelerators using optimum-neuron.",dl,h,mt='<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>',jl,w,hl,f,Jt="We’ll use a <code>trn1.32xlarge</code> instance with 16 Trainium Accelerators (32 Neuron Cores) and the Hugging Face Neuron Deep Learning AMI.",wl,b,ut="The Hugging Face AMI includes all required libraries pre-installed:",fl,I,yt="<li><code>datasets</code>, <code>transformers</code>, <code>optimum-neuron</code></li> <li>Neuron SDK packages</li> <li>No additional environment setup needed</li>",bl,R,ct='To create your instance, follow the guide <a href="https://huggingface.co/docs/optimum-neuron/ec2-setup" rel="nofollow">here</a>.',Il,C,dt='<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:',Rl,E,Cl,Z,El,g,jt='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.',Zl,B,gl,V,ht="To load the dataset we use the <code>load_dataset()</code> method from the <code>datasets</code> library.",Bl,_,Vl,W,wt="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.",_l,G,ft="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.",Wl,N,Gl,F,bt='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.',Nl,X,Fl,S,It='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>.',Xl,Q,Rt="On AWS Trainium, <code>optimum-neuron</code> provides <code>NeuronSFTTrainer</code> as a drop-in replacement.",Sl,A,Ct=`<strong>Distributed Training on Trainium:</strong>
Since Llama 3.1 8B doesn’t fit on a single accelerator, we use distributed training techniques:`,Ql,x,Et="<li>Data Parallel (DDP)</li> <li>Tensor Parallelism</li>",Al,k,Zt="Model loading and LoRA configuration work similarly to other accelerators.",xl,v,gt="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:",kl,Y,vl,H,Bt='📝 <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>.',Yl,$,Vt="To launch training, just run the following command in your AWS Trainium instance:",Hl,L,$l,z,_t='🔧 <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:',Ll,q,zl,D,ql,P,Wt="Optimum Neuron saves model shards separately during distributed training. These need to be consolidated before use.",Dl,O,Gt="Use the Optimum CLI to consolidate:",Pl,K,Ol,ll,Nt="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:",Kl,tl,lt,el,Ft="Once this step is done, it is possible to test the model with a new prompt.",tt,sl,Xt="You have successfully created a fine-tuned model from Llama 3.1!",et,al,st,nl,St="Share your fine-tuned model with the community by uploading it to the Hugging Face Hub.",at,Ml,Qt="<strong>Step 1: Authentication</strong>",nt,il,Mt,ol,At="<strong>Step 2: Upload your model</strong>",it,pl,ot,Tl,xt="🎉 <strong>Your fine-tuned Llama 3.1 model is now available on the Hub for others to use!</strong>",pt,ml,Tt;return c=new Pt({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),d=new Ul({props:{title:"🚀 Instruction Fine-Tuning of Llama 3.1 8B with LoRA",local:"-instruction-fine-tuning-of-llama-31-8b-with-lora",headingTag:"h1"}}),w=new Ul({props:{title:"1. 🛠️ Setup AWS Environment",local:"1--setup-aws-environment",headingTag:"h2"}}),E=new u({props:{code:"aHVnZ2luZ2ZhY2UtY2xpJTIwbG9naW4=",highlighted:"huggingface-cli login",wrap:!1}}),Z=new Ul({props:{title:"2. 📊 Load and Prepare the Dataset",local:"2--load-and-prepare-the-dataset",headingTag:"h2"}}),B=new u({props:{code:"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",highlighted:`{
  <span class="hljs-comment">&quot;instruction&quot;</span>: <span class="hljs-comment">&quot;What is world of warcraft&quot;</span>,
  <span class="hljs-comment">&quot;context&quot;</span>: <span class="hljs-comment">&quot;&quot;</span>,
  <span class="hljs-comment">&quot;response&quot;</span>: (
        <span class="hljs-comment">&quot;World of warcraft is a massive online multi player role playing game. &quot;</span>
        <span class="hljs-comment">&quot;It was released in 2004 by bizarre entertainment&quot;</span>
    )
}`,wrap:!1}}),_=new u({props:{code:"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",highlighted:`<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">&quot;databricks/databricks-dolly-15k&quot;</span>
dataset = load_dataset(dataset_id, split=<span class="hljs-string">&quot;train&quot;</span>)

dataset_size = <span class="hljs-built_in">len</span>(dataset)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;dataset size: <span class="hljs-subst">{dataset_size}</span>&quot;</span>)
<span class="hljs-comment"># dataset size: 15011</span>`,wrap:!1}}),N=new u({props:{code:"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",highlighted:`<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">&quot;&quot;&quot;Format Dolly dataset examples using the tokenizer&#x27;s chat template.&quot;&quot;&quot;</span>
    user_content = example[<span class="hljs-string">&quot;instruction&quot;</span>]
    <span class="hljs-keyword">if</span> <span class="hljs-built_in">len</span>(example[<span class="hljs-string">&quot;context&quot;</span>]) &gt; <span class="hljs-number">0</span>:
        user_content += <span class="hljs-string">f&quot;\\n\\nContext: <span class="hljs-subst">{example[<span class="hljs-string">&#x27;context&#x27;</span>]}</span>&quot;</span>

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

    <span class="hljs-keyword">return</span> tokenizer.apply_chat_template(messages, tokenize=<span class="hljs-literal">False</span>)`,wrap:!1}}),X=new Ul({props:{title:"3. 🎯 Fine-tune Llama 3.1 with NeuronSFTTrainer and PEFT",local:"3--fine-tune-llama-31-with-neuronsfttrainer-and-peft",headingTag:"h2"}}),Y=new u({props:{code:" ",highlighted:`model_id = <span class="hljs-string">&quot;meta-llama/Llama-3.1-8B&quot;</span>

<span class="hljs-comment"># Define the training arguments</span>
output_dir = <span class="hljs-string">&quot;Llama-3.1-8B-finetuned&quot;</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">&quot;flash_attention_2&quot;</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">&quot;embed_tokens&quot;</span>, <span class="hljs-string">&quot;q_proj&quot;</span>, <span class="hljs-string">&quot;k_proj&quot;</span>, <span class="hljs-string">&quot;v_proj&quot;</span>, <span class="hljs-string">&quot;o_proj&quot;</span>, <span class="hljs-string">&quot;gate_proj&quot;</span>, <span class="hljs-string">&quot;up_proj&quot;</span>, <span class="hljs-string">&quot;down_proj&quot;</span>],
    bias=<span class="hljs-string">&quot;none&quot;</span>,
    task_type=<span class="hljs-string">&quot;CAUSAL_LM&quot;</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_seq_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">&quot;&lt;|finetune_right_pad_id|&gt;&quot;</span>

<span class="hljs-comment"># Set chat template for Llama 3.1 format</span>
tokenizer.chat_template = (
    <span class="hljs-string">&quot;{% for message in messages %}&quot;</span>
    <span class="hljs-string">&quot;{% if message[&#x27;role&#x27;] == &#x27;system&#x27; %}&quot;</span>
    <span class="hljs-string">&quot;&lt;|begin_of_text|&gt;&lt;|start_header_id|&gt;system&lt;|end_header_id|&gt;\\n\\n{{ message[&#x27;content&#x27;] }}&lt;|eot_id|&gt;&quot;</span>
    <span class="hljs-string">&quot;{% elif message[&#x27;role&#x27;] == &#x27;user&#x27; %}&quot;</span>
    <span class="hljs-string">&quot;&lt;|start_header_id|&gt;user&lt;|end_header_id|&gt;\\n\\n{{ message[&#x27;content&#x27;] }}&lt;|eot_id|&gt;&quot;</span>
    <span class="hljs-string">&quot;{% elif message[&#x27;role&#x27;] == &#x27;assistant&#x27; %}&quot;</span>
    <span class="hljs-string">&quot;&lt;|start_header_id|&gt;assistant&lt;|end_header_id|&gt;\\n\\n{{ message[&#x27;content&#x27;] }}&lt;|eot_id|&gt;&quot;</span>
    <span class="hljs-string">&quot;{% endif %}&quot;</span>
    <span class="hljs-string">&quot;{% endfor %}&quot;</span>
    <span class="hljs-string">&quot;{% if add_generation_prompt %}&quot;</span>
    <span class="hljs-string">&quot;&lt;|start_header_id|&gt;assistant&lt;|end_header_id|&gt;\\n\\n&quot;</span>
    <span class="hljs-string">&quot;{% endif %}&quot;</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,
    tokenizer=tokenizer,
    train_dataset=dataset,
    formatting_func=<span class="hljs-keyword">lambda</span> example: format_dolly(example, tokenizer),
)
trainer.train()`,wrap:!1}}),L=new u({props:{code:"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",highlighted:`<span class="hljs-comment"># Flags for Neuron compilation</span>
<span class="hljs-built_in">export</span> NEURON_CC_FLAGS=<span class="hljs-string">&quot;--model-type transformer --retry_failed_compilation&quot;</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">&quot;meta-llama/Llama-3.1-8B&quot;</span> <span class="hljs-comment"># Change this to the desired model name</span>
OUTPUT_DIR=<span class="hljs-string">&quot;<span class="hljs-subst">$(echo $MODEL_NAME | cut -d&#x27;/&#x27; -f2)</span>-finetuned&quot;</span>
DISTRIBUTED_ARGS=<span class="hljs-string">&quot;--nproc_per_node <span class="hljs-variable">$PROCESSES_PER_NODE</span>&quot;</span>

<span class="hljs-keyword">if</span> [ <span class="hljs-string">&quot;<span class="hljs-variable">$NEURON_EXTRACT_GRAPHS_ONLY</span>&quot;</span> = <span class="hljs-string">&quot;1&quot;</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`,wrap:!1}}),q=new u({props:{code:"LiUyRmZpbmV0dW5lX2xsYW1hLnNo",highlighted:"./finetune_llama.sh",wrap:!1}}),D=new Ul({props:{title:"4. 🔄 Consolidate and Test the Fine-Tuned Model",local:"4--consolidate-and-test-the-fine-tuned-model",headingTag:"h2"}}),K=new u({props:{code:"b3B0aW11bS1jbGklMjBuZXVyb24lMjBjb25zb2xpZGF0ZSUyMExsYW1hLTMuMS04Qi1maW5ldHVuZWQlMjBMbGFtYS0zLjEtOEItZmluZXR1bmVkJTJGYWRhcHRlcl9kZWZhdWx0",highlighted:"optimum-cli neuron consolidate Llama-3.1-8B-finetuned Llama-3.1-8B-finetuned/adapter_default",wrap:!1}}),tl=new u({props:{code:"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",highlighted:`<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">&quot;meta-llama/Llama-3.1-8B&quot;</span>
ADAPTER_PATH = <span class="hljs-string">&quot;Llama-3.1-8B-finetuned/adapter_default&quot;</span>
MERGED_MODEL_PATH = <span class="hljs-string">&quot;Llama-3.1-8B-dolly&quot;</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">&quot;Saving tokenizer&quot;</span>)
tokenizer.save_pretrained(MERGED_MODEL_PATH)
<span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Saving model&quot;</span>)
finetuned_model = finetuned_model.merge_and_unload()
finetuned_model.save_pretrained(MERGED_MODEL_PATH)`,wrap:!1}}),al=new Ul({props:{title:"5. 🤗 Push to Hugging Face Hub",local:"5--push-to-hugging-face-hub",headingTag:"h2"}}),il=new u({props:{code:"aHVnZ2luZ2ZhY2UtY2xpJTIwbG9naW4=",highlighted:"huggingface-cli login",wrap:!1}}),pl=new u({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbEZvckNhdXNhbExNJTJDJTIwQXV0b1Rva2VuaXplciUwQSUwQU1FUkdFRF9NT0RFTF9QQVRIJTIwJTNEJTIwJTIyTGxhbWEtMy4xLThCLWRvbGx5JTIyJTBBSFVCX01PREVMX05BTUUlMjAlM0QlMjAlMjJ5b3VyLXVzZXJuYW1lJTJGbGxhbWEzLjEtOGItZG9sbHklMjIlMEElMEElMjMlMjBMb2FkJTIwYW5kJTIwcHVzaCUyMHRva2VuaXplciUwQXRva2VuaXplciUyMCUzRCUyMEF1dG9Ub2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKE1FUkdFRF9NT0RFTF9QQVRIKSUwQXRva2VuaXplci5wdXNoX3RvX2h1YihIVUJfTU9ERUxfTkFNRSklMEElMEElMjMlMjBMb2FkJTIwYW5kJTIwcHVzaCUyMG1vZGVsJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JDYXVzYWxMTS5mcm9tX3ByZXRyYWluZWQoTUVSR0VEX01PREVMX1BBVEgpJTBBbW9kZWwucHVzaF90b19odWIoSFVCX01PREVMX05BTUUp",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, AutoTokenizer

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