# Model customization
Amazon SageMaker AI model customization lets you adapt foundation models to your specific use case by training them on your data. You choose a *customization technique* (how the model learns), a *training type* (how much of the model to update), and an *infrastructure option* (where to run the job).
## Overview
**Customization techniques** define *how* your model learns from data:
+ **SFT** (Supervised Fine-Tuning) — Train on labeled prompt-response pairs. See [SFT](customizing-models-sft.md).
+ **DPO** (Direct Preference Optimization) — Train on preferred vs rejected response pairs. See [DPO](customizing-models-dpo.md).
+ **RFT** (Reinforcement Fine-Tuning) — Optimize via reward signals (RLVR or RLAIF). See [RFT](customizing-models-rft.md).
+ **MTRL** (Multi-Turn Reinforcement Learning) — Train agents for multi-step agentic tasks. See [Multi-turn reinforcement learning](model-customize-mtrl.md).
+ **Continuous Customization** — Chain techniques sequentially. See [Continuous customization](customizing-models-continuous.md).
**Training types** determine *how much* of the model to update:
+ **LoRA** (Low-Rank Adaptation) — Trains a small set of adapter weights. Lower cost, faster training. See [LoRA](customizing-models-lora.md).
+ **FFT** (Full Fine-Tuning) — Updates all model weights. Higher compute, deeper customization. See [FFT](customizing-models-fft.md).
**Infrastructure options** control *where* your training job runs:
+ **Serverless** — Fully managed, no instance selection. Best for quick experimentation and production jobs without ops overhead. Serverless model customization also supports training session APIs, which give you full control over your training loop without managing GPU infrastructure. See [Serverless model customization](customize-model.md) and [Training session APIs](customizing-models-training-sessions.md).
+ **SageMaker AI Training Jobs** — Ephemeral jobs where you select instance type. Uses **Recipes** for pre-configured training. See [SageMaker AI Training Jobs](customizing-models-training-jobs.md).
+ **HyperPod** — Persistent clusters with fault recovery. Uses **Recipes** for distributed training on EKS or Slurm. See [HyperPod](customizing-models-hyperpod.md).
## Choosing your approach
Before fine-tuning, consider whether a simpler approach meets your needs:
Prompt Engineering
Your model gives acceptable answers but needs better tone, format, or style. No training required — fast iteration. See [Prompt engineering for foundation models](https://docs.aws.amazon.com/sagemaker/latest/dg/jumpstart-foundation-models-customize-prompt-engineering.html).
RAG (Retrieval Augmented Generation)
Your model lacks knowledge about your specific domain. Augment with external knowledge sources without retraining. See [Retrieval Augmented Generation](https://docs.aws.amazon.com/sagemaker/latest/dg/jumpstart-foundation-models-customize-rag.html).
Fine-tuning
Your model needs to consistently learn new behaviors, styles, or domain expertise. Train on your data to change model weights. Continue reading this guide.
## Supported models for fine-tuning
The following models are available for customization across serverless, SageMaker AI Training Jobs, and HyperPod infrastructure.
| Provider | Model | Model ID | Serverless | Training Jobs | HyperPod |
| --- | --- | --- | --- | --- | --- |
| Alibaba | Qwen3.8 27B | huggingface-vlm-qwen3-8-27b | ✓ | ✓ | ✓ |
| Alibaba | Qwen3.6 27B | huggingface-vlm-qwen3-6-27b | ✓ | ✓ | ✓ |
| Alibaba | Qwen3.6 A3B 35B | huggingface-vlm-qwen3-6-35b-a3b | ✓ | ✓ | ✓ |
| Alibaba | Qwen3.5 27B | huggingface-vlm-qwen3-5-27b | ✓ | ✓ | ✓ |
| Alibaba | Qwen3.5 9B | huggingface-vlm-qwen3-5-9b | ✓ | ✓ | ✓ |
| Alibaba | Qwen3.5 4B | huggingface-vlm-qwen3-5-4b | ✓ | ✓ | ✓ |
| Alibaba | Qwen3 32B | huggingface-reasoning-qwen3-32b | ✓ | ✓ | ✓ |
| Alibaba | Qwen3 14B | huggingface-reasoning-qwen3-14b | ✓ | ✓ | ✓ |
| Alibaba | Qwen3 8B | huggingface-reasoning-qwen3-8b | ✓ | ✓ | ✓ |
| Alibaba | Qwen3 4B | huggingface-reasoning-qwen3-4b | ✓ | ✓ | ✓ |
| Alibaba | Qwen3 1.7B | huggingface-reasoning-qwen3-1-7b | ✓ | ✓ | ✓ |
| Alibaba | Qwen3 0.6B | huggingface-reasoning-qwen3-06b | ✓ | ✓ | ✓ |
| Alibaba | Qwen2.5 Instruct 72B | huggingface-llm-qwen2-5-72b-instruct | ✓ | ✓ | ✓ |
| Alibaba | Qwen2.5 Instruct 32B | huggingface-llm-qwen2-5-32b-instruct | ✓ | ✓ | ✓ |
| Alibaba | Qwen2.5 Instruct 14B | huggingface-llm-qwen2-5-14b-instruct | ✓ | ✓ | ✓ |
| Alibaba | Qwen2.5 Instruct 7B | huggingface-llm-qwen2-5-7b-instruct | ✓ | ✓ | ✓ |
| DeepSeek | R1 Distill Qwen 32B | deepseek-llm-r1-distill-qwen-32b | ✓ | ✓ | ✓ |
| DeepSeek | R1 Distill Qwen 14B | deepseek-llm-r1-distill-qwen-14b | ✓ | ✓ | ✓ |
| DeepSeek | R1 Distill Qwen 7B | deepseek-llm-r1-distill-qwen-7b | ✓ | ✓ | ✓ |
| DeepSeek | R1 Distill Qwen 1.5B | deepseek-llm-r1-distill-qwen-1-5b | ✓ | ✓ | ✓ |
| DeepSeek | R1 Distill Llama 70B | deepseek-llm-r1-distill-llama-70b | ✓ | ✓ | ✓ |
| DeepSeek | R1 Distill Llama 8B | deepseek-llm-r1-distill-llama-8b | ✓ | ✓ | ✓ |
| Google | Gemma 4 31B | huggingface-vlm-gemma-4-31b-it | ✓ | ✓ | ✓ |
| Google | Gemma 4 26B A4B | huggingface-vlm-gemma-4-26b-a4b-it | ✓ | ✓ | ✓ |
| Google | Gemma 4 E4B | huggingface-vlm-gemma-4-e4b-it | ✓ | ✓ | ✓ |
| Meta | Llama 3.3 Instruct 70B | meta-textgeneration-llama-3-3-70b-instruct | ✓ | ✓ | ✓ |
| Meta | Llama 3.2 Instruct 3B | meta-textgeneration-llama-3-2-3b-instruct | ✓ | ✓ | ✓ |
| Meta | Llama 3.2 Instruct 1B | meta-textgeneration-llama-3-2-1b-instruct | ✓ | ✓ | ✓ |
| Meta | Llama 3.1 Instruct 8B | meta-textgeneration-llama-3-1-8b-instruct | ✓ | ✓ | ✓ |
| Mistral AI | Ministral 3 Instruct 2512 BF16 14B | huggingface-vlm-ministral-3-14b-instruct-2512-bf16 | ✓ | ✓ | ✓ |
| Mistral AI | Ministral 3 Instruct 2512 BF16 8B | huggingface-vlm-ministral-3-8b-instruct-2512-bf16 | ✓ | ✓ | ✓ |
| Mistral AI | Ministral 3 Instruct 2512 BF16 3B | huggingface-vlm-ministral-3-3b-instruct-2512-bf16 | ✓ | ✓ | ✓ |
| NVIDIA | Nemotron 3 Super 120B (A12B) | huggingface-llm-nvidia-nemotron-3-super-120b-a12b-bf16 | ✓ | ✓ | ✓ |
| NVIDIA | Nemotron 3 Nano 30B (A3B) | huggingface-reasoning-nvidia-nemotron-3-nano-30b-a3b-bf16 | ✓ | ✓ | ✓ |
| NVIDIA | Nemotron 3.5 Lightning 30B (A3B) | huggingface-reasoning-nemotron-3-5-lightning-30b-a3b-bf16 | ✓ | ✓ | ✓ |
| OpenAI | GPT OSS 120B | openai-reasoning-gpt-oss-120b | ✓ | ✓ | ✓ |
| OpenAI | GPT OSS 20B | openai-reasoning-gpt-oss-20b | ✓ | ✓ | ✓ |
For Amazon Nova models, see the Nova walkthrough under [Serverless model customization](customize-model.md).