diff --git a/examples/kfto-sft-llm/sft.ipynb b/examples/kfto-sft-llm/sft.ipynb
index a4f0067e4..445e5c9ed 100644
--- a/examples/kfto-sft-llm/sft.ipynb
+++ b/examples/kfto-sft-llm/sft.ipynb
@@ -42,9 +42,9 @@
     "# Model\n",
     "model_name_or_path: Meta-Llama/Meta-Llama-3.1-8B-Instruct\n",
     "model_revision: main\n",
-    "torch_dtype: bfloat16\n",
+    "dtype: bfloat16\n",
     "attn_implementation: flash_attention_2    # one of eager (default), sdpa or flash_attention_2\n",
-    "use_liger: false                          # use Liger kernels\n",
+    "use_liger_kernel: false                   # use Liger kernels\n",
     "\n",
     "# PEFT / LoRA\n",
     "use_peft: true\n",
@@ -69,9 +69,9 @@
     "  append_concat_token: false              # add additional separator token\n",
     "\n",
     "# SFT\n",
-    "max_seq_length: 1024                      # max sequence length for model and packing of the dataset\n",
-    "dataset_batch_size: 1000                  # samples to tokenize per batch\n",
+    "max_length: 1024                          # max sequence length for model and packing of the dataset\n",
     "packing: false\n",
+    "padding_free: false\n",
     "\n",
     "# Training\n",
     "num_train_epochs: 10                      # number of training epochs\n",
@@ -175,7 +175,7 @@
     "        revision=model_args.model_revision,\n",
     "        trust_remote_code=model_args.trust_remote_code,\n",
     "        attn_implementation=model_args.attn_implementation,\n",
-    "        torch_dtype=model_args.torch_dtype,\n",
+    "        dtype=model_args.dtype,\n",
     "        use_cache=False if training_args.gradient_checkpointing or\n",
     "                           training_args.fsdp_config.get(\"activation_checkpointing\",\n",
     "                                                         False) else True,\n",
@@ -294,8 +294,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# IMPORTANT: Labels and annotations support in create_job() method requires kubeflow-training v1.9.2+. Skip this cell if using RHOAI 2.21 or later.\n",
-    "%pip install -U kubeflow-training "
+    "%pip install --ignore-installed git+https://github.com/astefanutti/sdk.git@options\n",
+    "%pip install --ignore-installed git+https://github.com/kubeflow/trainer.git@master#subdirectory=api/python_api"
    ]
   },
   {
@@ -305,9 +305,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from kubeflow.trainer import TrainerClient, CustomTrainer, KubernetesBackendConfig\n",
+    "from kubeflow.trainer.options import PodTemplateOverrides, PodTemplateOverride, PodTemplateSpecOverride, ContainerOverride, Labels\n",
     "from kubernetes import client\n",
-    "from kubeflow.training import TrainingClient\n",
-    "from kubeflow.training.models import V1Volume, V1VolumeMount, V1PersistentVolumeClaimVolumeSource\n",
     "\n",
     "api_server = \"https://kubernetes.default.svc\"\n",
     "token = \"<TOKEN>\"\n",
@@ -318,7 +318,7 @@
     "# Un-comment if your cluster API server uses a self-signed certificate or an un-trusted CA\n",
     "#configuration.verify_ssl = False\n",
     "api_client = client.ApiClient(configuration)\n",
-    "client = TrainingClient(client_configuration=api_client.configuration)"
+    "client = TrainerClient(backend_config=KubernetesBackendConfig(client_configuration=api_client.configuration))"
    ]
   },
   {
@@ -333,8 +333,7 @@
     "* Check the number of worker nodes\n",
     "* Amend the resources per worker according to the job requirements\n",
     "* If you use AMD accelerators:\n",
-    "  * Change `nvidia.com/gpu` to `amd.com/gpu` in `resources_per_worker`\n",
-    "  * Change `base_image` to `quay.io/modh/training:py311-rocm62-torch251`\n",
+    "  * Change `runtime` to `training-rocm64-torch28-py312`\n",
     "* Update the PVC name to the one you've attached to the workbench if needed"
    ]
   },
@@ -345,38 +344,52 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "client.create_job(\n",
-    "    job_kind=\"PyTorchJob\",\n",
-    "    name=\"sft\",\n",
-    "    train_func=main,\n",
-    "    num_workers=8,\n",
-    "    num_procs_per_worker=\"1\",\n",
-    "    resources_per_worker={\n",
-    "        \"nvidia.com/gpu\": 1,\n",
-    "        \"memory\": \"64Gi\",\n",
-    "        \"cpu\": 4,\n",
-    "    },\n",
-    "    base_image=\"quay.io/modh/training:py311-cuda124-torch251\",\n",
-    "    env_vars={\n",
-    "        # HuggingFace\n",
-    "        \"HF_HOME\": \"/mnt/shared/.cache\",\n",
-    "        \"HF_TOKEN\": \"\",\n",
-    "        # CUDA / ROCm (HIP)\n",
-    "        \"PYTORCH_CUDA_ALLOC_CONF\": \"expandable_segments:True\",\n",
-    "        \"PYTORCH_HIP_ALLOC_CONF\": \"expandable_segments:True\",\n",
-    "        # NCCL / RCCL\n",
-    "        \"NCCL_DEBUG\": \"INFO\",\n",
-    "    },\n",
-    "    # labels={\"kueue.x-k8s.io/queue-name\": \"<LOCAL_QUEUE_NAME>\"}, # Optional: Add local queue name and uncomment these lines if using Kueue for resource management\n",
-    "    parameters=parameters,\n",
-    "    volumes=[\n",
-    "        V1Volume(name=\"shared\",\n",
-    "                 persistent_volume_claim=V1PersistentVolumeClaimVolumeSource(claim_name=\"shared\")),\n",
+    "job_name = client.train(\n",
+    "    trainer=CustomTrainer(\n",
+    "        func=main,\n",
+    "        func_args=parameters,\n",
+    "        num_nodes=4,\n",
+    "        resources_per_node={\n",
+    "            \"cpu\": 4,\n",
+    "            \"memory\": \"128Gi\",\n",
+    "            \"gpu\": 1,\n",
+    "        },\n",
+    "        env={\n",
+    "            # HuggingFace\n",
+    "            \"HF_HOME\": \"/mnt/shared/.cache\",\n",
+    "            \"HF_TOKEN\": \"\",\n",
+    "            # CUDA / ROCm (HIP)\n",
+    "            \"PYTORCH_CUDA_ALLOC_CONF\": \"expandable_segments:True\",\n",
+    "            #\"PYTORCH_HIP_ALLOC_CONF\": \"expandable_segments:True\",\n",
+    "            # NCCL / RCCL\n",
+    "            \"NCCL_DEBUG\": \"INFO\",\n",
+    "        },\n",
+    "        packages_to_install=[\n",
+    "            \"transformers==4.57.1\",\n",
+    "            \"trl==0.24.0\"\n",
+    "        ],\n",
+    "    ),\n",
+    "    options=[\n",
+    "        PodTemplateOverrides(pod_template_overrides=[\n",
+    "            PodTemplateOverride(\n",
+    "                target_jobs=[\"node\"],\n",
+    "                spec=PodTemplateSpecOverride(\n",
+    "                    volumes=[\n",
+    "                        {\"name\": \"shared\", \"persistentVolumeClaim\": {\"claimName\": \"shared\"}}\n",
+    "                    ],\n",
+    "                    containers=[\n",
+    "                        ContainerOverride(\n",
+    "                            name=\"node\",\n",
+    "                            volume_mounts=[\n",
+    "                                {\"name\": \"shared\", \"mountPath\": \"/mnt/shared\"},\n",
+    "                            ]\n",
+    "                        )\n",
+    "                    ],\n",
+    "                )\n",
+    "            )\n",
+    "        ])\n",
     "    ],\n",
-    "    volume_mounts=[\n",
-    "        V1VolumeMount(name=\"shared\", mount_path=\"/mnt/shared\"),\n",
-    "    ],\n",
-    ")"
+    "    runtime=client.get_runtime(\"training-cuda128-torch28-py312\"),"
    ]
   },
   {
@@ -394,11 +407,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "_ = client.get_job_logs(\n",
-    "    name=\"sft\",\n",
-    "    job_kind=\"PyTorchJob\",\n",
-    "    follow=True,\n",
-    ")"
+    "for logline in client.get_job_logs(job_name, follow=True):\n",
+    "    print(logline)\""
    ]
   },
   {
@@ -650,7 +660,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "client.delete_job(name=\"sft\")"
+    "client.delete_job(job_name)"
    ]
   },
   {