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name: sagemaker-llm-training-skill description: > Standard Operating Procedure (SOP) for training and fine-tuning LLMs on Amazon SageMaker. Use when the user wants to: fine-tune, continued pretraining, CPT, preference optimization, DPO, RLHF, LoRA, QLoRA, Spectrum, full fine-tuning, SFT on SageMaker. Also use when choosing between SageMaker Training Jobs vs HyperPod clusters, selecting training containers (DLC), or GPU vs Trainium instances. Generates runnable notebooks or Python scripts. Triggers: "train llm", "fine-tune", "sagemaker training", "lora training", "qlora", "hyperpod training", "trainium training", "dlc container", "training job". argument-hint: "[model-id or 'start']"

SageMaker LLM Training Operator (SOP)

Guide users from intent → executable SageMaker training launcher using official AWS recipes when possible.

Guardrails

• Scope: AWS SageMaker Training only (Training Jobs or HyperPod)
• Produce artifacts: Deliver runnable code, not just advice
• Dynamic info: Fetch current container images and instance availability - do not hardcode versions
• Python version: SageMaker SDK v3 requires Python ≤3.13 (incompatible with 3.14+)
• DLC images source: https://aws.github.io/deep-learning-containers/reference/available_images/ (authoritative)
• Neuron compatibility: https://huggingface.co/docs/optimum-neuron/en/supported_architectures (authoritative)
• Primary recipe source: https://github.com/aws-samples/amazon-sagemaker-generativeai/tree/feature/gpro-rlvr-recipes/0_model_customization_recipes

Wizard Mode (CRITICAL)

Ask ONE question at a time. Wait for response before proceeding.

Use AskUserQuestion for choices. Use plain text for open-ended questions.

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Wizard Steps

Step 1: Model

Ask: "Which model do you want to train?"

• Examples: meta-llama/Llama-3.1-8B-Instruct, Qwen/Qwen2.5-7B-Instruct
• Accept: HuggingFace ID or S3 path

→ Wait for response.

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Step 2: Region

Ask: "Which AWS region should we use for training?"

• Suggest: us-east-1, us-west-2 (best GPU/Trainium availability)
• If user says "default" or doesn't specify, use us-east-1

→ Wait for response.

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Step 3: Training Objective

Use AskUserQuestion:

• Instruction SFT - Fine-tune on instruction-response pairs
• Continued Pretraining (CPT) - Extend training on domain corpus
• Preference Optimization (DPO) - Align with human preferences

→ Wait for response.

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Step 4: Code Readiness

Use AskUserQuestion:

• No, I need a recipe - Use AWS sample recipes (recommended)
• Yes, I have my own code - Bring existing training script

→ If No: Go to Step 5A (Recipe path) → If Yes: Go to Step 5B (Custom code path)

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Step 5A: Recipe Selection (No existing code)

IMPORTANT: Check what recipes actually exist before offering technique choices.

1. Fetch available recipes from the primary recipe repo:

   https://github.com/aws-samples/amazon-sagemaker-generativeai/tree/feature/gpro-rlvr-recipes/0_model_customization_recipes
   

2. Match model family to available recipes:

   • Look for folder matching model family (llama, qwen, gemma, phi, deepseek, etc.)
   • Note which techniques are available (QLoRA, Spectrum, Full)

3. Present ONLY available techniques using AskUserQuestion:

   • Only show techniques that exist in the repo for this model family
   • Example: Gemma has QLoRA and Full, but NOT Spectrum or LoRA

Model Family	Available Techniques
Llama	QLoRA, Spectrum, Full
Qwen	QLoRA, Spectrum, Full
Gemma	QLoRA, Full
Phi	QLoRA, Spectrum, Full
DeepSeek	QLoRA, Spectrum, Full

→ Continue to Step 6.

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Step 5B: Custom Code Path (Has existing code)

1. Ask: "Path to your training script and requirements.txt?"
2. Inspect requirements: python scripts/inspect_requirements.py <path>
3. Determine accelerator from dependencies per references/container-selection.md

→ Continue to Step 6.

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Step 6: Infrastructure

Use AskUserQuestion:

• SageMaker Training Jobs - Transient, pay-per-use, simpler setup
• SageMaker HyperPod - Persistent cluster, Slurm/EKS orchestration

→ Wait for response.

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Step 7: Accelerator

Trainium support is limited. Only these exact architectures are supported for training on Neuron:

Supported	NOT Supported (variants)
llama	llama_vl, mllama, etc.
qwen3	qwen3_vl, qwen2, qwen2_5, etc.
granite	granite_vl, etc.

IMPORTANT: Variants are different architectures! Check the exact model_type value:

• qwen3 → Supported
• qwen3_vl → NOT supported (Vision-Language variant)
• llama → Supported
• mllama → NOT supported (Multimodal Llama)

Source: https://huggingface.co/docs/optimum-neuron/en/supported_architectures

Decision logic:

1. Fetch the model's architecture using WebFetch:

   WebFetch: https://huggingface.co/<model-id>/raw/main/config.json
   Prompt: "What is the model_type value in this config?"
   

   Example: https://huggingface.co/aisingapore/Apertus-SEA-LION-v4-8B-IT/raw/main/config.json

2. If model_type is llama, qwen3, or granite → Offer both GPU and Trainium

3. Otherwise → Use GPU only (do NOT offer Trainium)

Note: Model names don't always indicate architecture. Always check config.json.

If architecture IS supported - Use AskUserQuestion:

• GPU (NVIDIA) - Broad support, QLoRA compatible
• Trainium - Cost-effective for Llama/Qwen3/Granite (no 4-bit quantization)

If architecture is NOT supported - Inform user:

"This model architecture (<model_type>) is not supported on Trainium for training. Only Llama, Qwen3, and Granite are currently supported. Using GPU instead."

→ If Trainium + QLoRA selected: Error - QLoRA not supported on Trainium → Wait for response.

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Step 8: Dataset

Ask: "Where is your training dataset?"

• Accept: HuggingFace Hub dataset name (e.g., databricks/dolly-15k)
• Accept: S3 URI with JSONL/Parquet files
• Validate format per references/data-contract.md

→ Wait for response.

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Step 9: Context Length

Use AskUserQuestion:

• 4k tokens - Standard, lower memory
• 8k tokens - Balanced
• 16k+ tokens - Long context (higher memory)

→ Wait for response.

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Step 10: Instance Sizing

Based on collected info, recommend instances per references/instance-sizing.md.

1. Get recommendations:

   python scripts/fetch_instance_info.py --model-size <B> --technique <tech> --accelerator <gpu|trainium>
   

2. Check quota availability for recommended instances:

   aws service-quotas list-service-quotas --service-code sagemaker --region <region> \
     --query "Quotas[?contains(QuotaName, 'training') && contains(QuotaName, '<instance-type>')].{Name:QuotaName, Value:Value}"
   

3. Present recommendations with quota status:

   • Show primary and alternative instances
   • Indicate which have quota > 0
   • If primary has no quota, suggest requesting increase or using alternative

4. If no quota in selected region, check other regions:

   for region in us-west-2 eu-west-1; do
     aws service-quotas list-service-quotas --service-code sagemaker --region $region ...
   done
   

→ Wait for response.

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Step 11: Spot Instances (Cost Optimization)

Use AskUserQuestion:

• Yes, use Spot instances - 50-70% cost savings, may be interrupted
• No, use On-Demand - Higher cost, guaranteed capacity

→ If Spot: Enable checkpointing for fault tolerance → Wait for response.

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Step 12: S3 Output Bucket

Ask: "Where should we save the trained model? (S3 bucket or path)"

Options for discovery:

1. Ask user for S3 bucket/path
2. If user says "default": Use sagemaker-{region}-{account_id} bucket
3. Optionally discover existing SageMaker buckets:

   aws s3 ls | grep sagemaker
   

4. If multiple buckets found, confirm with user

→ Wait for response.

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Step 13: Execution Role

Ask: "Which IAM role should SageMaker use? (ARN or 'discover')"

Options for discovery:

1. Ask user for role ARN directly
2. If user says "discover" or "find": List SageMaker execution roles:

   aws iam list-roles --query "Roles[?contains(RoleName, 'SageMaker') || contains(RoleName, 'sagemaker')]"
   

3. If multiple roles found, present options and confirm
4. Verify role has required permissions (S3, ECR access)

→ Wait for response.

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Step 14: Output Format

Use AskUserQuestion:

• Jupyter Notebook - For SageMaker Studio
• Python Script - Standalone launcher, CI/CD friendly

→ Wait for response.

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Step 15: Generate Artifacts

1. Fetch current container images from the official AWS DLC page:

   WebFetch URL: https://aws.github.io/deep-learning-containers/reference/available_images/
   Prompt: "Find the latest PyTorch training <gpu|neuron> container image URI for SageMaker in <region>"
   

   Fallback (may have stale versions):

   python scripts/fetch_dlc_images.py --framework pytorch --region <region>
   

2. Select appropriate template from templates/:

   GPU templates:

   • lora_peft.py - LoRA with PEFT
   • qlora_peft.py - QLoRA (4-bit)
   • sft_trl.py - Full SFT
   • dpo_trl.py - DPO
   • cpt_hf.py - Continued pretraining

   Trainium templates:

   • trainium/lora_neuron.py - LoRA for Trainium (eager attention, no device_map)
   • trainium/sft_neuron.py - Full SFT for Trainium

   Launcher:

   • launch_training_job.py - SDK v3 ModelTrainer launcher

   HyperPod:

   • hyperpod/ - HyperPod configs

3. Generate launcher using references/output-artifacts.md

4. Run references/checklist.md before delivering

Python Compatibility: SageMaker SDK v3 requires Python ≤3.13 (incompatible with 3.14+)

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Reference Files

File	Use When
references/best-practices.md	Default recommendations, technique selection, hyperparameters
references/recipe-sources.md	Finding AWS sample recipes
references/instance-sizing.md	Selecting instance types
references/container-selection.md	Choosing container images
references/neuron-validation.md	Verifying Trainium compatibility
references/data-contract.md	Dataset format requirements
references/output-artifacts.md	Generating final artifacts
references/checklist.md	Pre-delivery verification

Scripts

Script	Purpose
scripts/fetch_dlc_images.py	Get current container images
scripts/fetch_instance_info.py	Get instance specs and recommendations
scripts/inspect_requirements.py	Analyze user's requirements.txt
scripts/validate_dataset.py	Validate dataset format

Templates

Template	Training Method	Accelerator
templates/lora_peft.py	LoRA with PEFT	GPU
templates/qlora_peft.py	QLoRA (4-bit)	GPU
templates/sft_trl.py	Full SFT with TRL	GPU
templates/dpo_trl.py	DPO preference training	GPU
templates/cpt_hf.py	Continued pretraining	GPU
templates/trainium/lora_neuron.py	LoRA for Neuron SDK	Trainium
templates/trainium/sft_neuron.py	Full SFT for Neuron SDK	Trainium
templates/launch_training_job.py	SDK v3 ModelTrainer launcher	Both
templates/hyperpod/	HyperPod recipes	Both

Note: Trainium templates use attn_implementation="eager" and no device_map="auto" for Neuron compatibility.