diff --git a/.gitignore b/.gitignore
index f72c7e06..7246f210 100644
--- a/.gitignore
+++ b/.gitignore
@@ -17,6 +17,7 @@ __pycache__/
 
 /doc/_apidoc/
 /build
+/hyperpod-pytorch-job-template/build
 
 /sagemaker-hyperpod/build
 /sagemaker-hyperpod/.coverage
diff --git a/hyperpod-pytorch-job-template/hyperpod_pytorch_job_template/v1_0/recipes/hf/__init__.py b/hyperpod-pytorch-job-template/hyperpod_pytorch_job_template/v1_0/recipes/hf/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/hyperpod-pytorch-job-template/hyperpod_pytorch_job_template/v1_0/recipes/hf/model.py b/hyperpod-pytorch-job-template/hyperpod_pytorch_job_template/v1_0/recipes/hf/model.py
new file mode 100644
index 00000000..cec37765
--- /dev/null
+++ b/hyperpod-pytorch-job-template/hyperpod_pytorch_job_template/v1_0/recipes/hf/model.py
@@ -0,0 +1,360 @@
+from typing import Any, Dict, List, Optional, Union
+from pydantic import BaseModel, ConfigDict
+
+
+class RunConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    name: Optional[str] = None
+    results_dir: Optional[str] = None
+    time_limit: Optional[str] = None
+    model_type: Optional[str] = None
+    dependency: Optional[str] = None
+
+
+class TrainerConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    devices: Optional[int] = None
+    num_nodes: Optional[int] = None
+    accelerator: Optional[str] = None
+    precision: Optional[str] = None
+    max_steps: Optional[int] = None
+    log_every_n_steps: Optional[int] = None
+    val_check_interval: Optional[Union[int, float]] = None
+    limit_val_batches: Optional[Union[int, float]] = None
+    logger: Optional[bool] = None
+    enable_checkpointing: Optional[bool] = None
+    use_distributed_sampler: Optional[bool] = None
+    max_epochs: Optional[int] = None
+    max_time: Optional[str] = None
+    limit_test_batches: Optional[int] = None
+    accumulate_grad_batches: Optional[int] = None
+    gradient_clip_val: Optional[float] = None
+
+
+class CheckpointCallbackParamsConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    save_top_k: Optional[int] = None
+    every_n_train_steps: Optional[int] = None
+    monitor: Optional[str] = None
+    mode: Optional[str] = None
+    save_last: Optional[bool] = None
+
+
+class AutoCheckpointConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    enabled: Optional[bool] = None
+
+
+class ExportFullModelConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    every_n_train_steps: Optional[int] = None
+    save_last: Optional[bool] = None
+
+
+class TokenizerConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    library: Optional[str] = None
+    type: Optional[str] = None
+    model: Optional[str] = None
+    delimiter: Optional[str] = None
+    vocab_file: Optional[str] = None
+    merge_file: Optional[str] = None
+    sentencepiece_legacy: Optional[bool] = None
+
+
+class StepTimingKwargsConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    sync_cuda: Optional[bool] = None
+    buffer_size: Optional[int] = None
+
+
+class ExpManagerConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    exp_dir: Optional[str] = None
+    name: Optional[str] = None
+    create_tensorboard_logger: Optional[bool] = None
+    summary_writer_kwargs: Optional[Dict[str, Any]] = None
+    create_mlflow_logger: Optional[bool] = None
+    mlflow_logger_kwargs: Optional[Dict[str, Any]] = None
+    create_wandb_logger: Optional[bool] = None
+    wandb_logger_kwargs: Optional[Dict[str, Any]] = None
+    create_checkpoint_callback: Optional[bool] = None
+    checkpoint_callback_params: Optional[CheckpointCallbackParamsConfig] = None
+    checkpoint_dir: Optional[str] = None
+    resume_from_checkpoint: Optional[str] = None
+    auto_checkpoint: Optional[AutoCheckpointConfig] = None
+    export_full_model: Optional[ExportFullModelConfig] = None
+    explicit_log_dir: Optional[str] = None
+    resume_if_exists: Optional[bool] = None
+    resume_ignore_no_checkpoint: Optional[bool] = None
+    log_step_timing: Optional[bool] = None
+    step_timing_kwargs: Optional[StepTimingKwargsConfig] = None
+
+
+class RopeScalingConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    rope_type: Optional[str] = None
+    factor: Optional[float] = None
+    high_freq_factor: Optional[float] = None
+    low_freq_factor: Optional[float] = None
+    original_max_position_embeddings: Optional[int] = None
+
+
+class PeftConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    peft_type: Optional[str] = None
+    rank: Optional[int] = None
+    alpha: Optional[int] = None
+    dropout: Optional[float] = None
+    target_modules: Optional[List[str]] = None
+
+
+class SchedulerConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    name: Optional[str] = None
+    warmup_steps: Optional[int] = None
+    constant_steps: Optional[int] = None
+    min_lr: Optional[float] = None
+
+
+class OptimizerConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    name: Optional[str] = None
+    lr: Optional[float] = None
+    weight_decay: Optional[float] = None
+    betas: Optional[List[float]] = None
+    sched: Optional[SchedulerConfig] = None
+    bucket_cap_mb: Optional[int] = None
+    overlap_grad_sync: Optional[bool] = None
+    overlap_param_sync: Optional[bool] = None
+    contiguous_grad_buffer: Optional[bool] = None
+    contiguous_param_buffer: Optional[bool] = None
+
+
+class DataConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    train_dir: Optional[str] = None
+    val_dir: Optional[str] = None
+    dataset_type: Optional[str] = None
+    use_synthetic_data: Optional[bool] = None
+    tokenizer_name: Optional[str] = None
+    zipped_data: Optional[bool] = None
+    data_impl: Optional[str] = None
+    splits_string: Optional[str] = None
+    seq_length: Optional[int] = None
+    skip_warmup: Optional[bool] = None
+    num_workers: Optional[int] = None
+    dataloader_type: Optional[str] = None
+    reset_position_ids: Optional[bool] = None
+    reset_attention_mask: Optional[bool] = None
+    eod_mask_loss: Optional[bool] = None
+    index_mapping_dir: Optional[str] = None
+    data_prefix: Optional[List[str]] = None
+
+
+class VizTracerConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    enabled: Optional[bool] = None
+
+
+class DpoConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    enabled: Optional[bool] = None
+    beta: Optional[float] = None
+    label_smoothing: Optional[float] = None
+
+
+class ModelConfig(BaseModel):
+    model_config = ConfigDict(extra="allow")
+
+    model_type: Optional[str] = None
+    train_batch_size: Optional[int] = None
+    val_batch_size: Optional[int] = None
+    seed: Optional[int] = None
+    grad_clip: Optional[float] = None
+    log_reduced_training_loss: Optional[bool] = None
+
+    # Additional model-specific fields found in recipes
+    max_window_layers: Optional[int] = None
+    rms_norm_eps: Optional[float] = None
+    tie_word_embeddings: Optional[bool] = None
+    use_sliding_window: Optional[bool] = None
+
+    # Memory saving/distributed training configs
+    tensor_model_parallel_degree: Optional[int] = None
+    expert_model_parallel_degree: Optional[int] = None
+    context_parallel_degree: Optional[int] = None
+    moe: Optional[bool] = None
+    sliding_window: Optional[int] = None
+    num_experts_per_tok: Optional[int] = None
+    num_local_experts: Optional[int] = None
+    moe_load_balancing: Optional[str] = None
+    global_token_shuffle: Optional[bool] = None
+    moe_all_to_all_dispatcher: Optional[bool] = None
+    activation_checkpointing: Optional[bool] = None
+    activation_loading_horizon: Optional[int] = None
+    delayed_param: Optional[bool] = None
+    offload_activations: Optional[bool] = None
+    multi_modal: Optional[bool] = None
+
+    # FSDP Configs
+    sharding_strategy: Optional[str] = None
+    forward_prefetch: Optional[bool] = None
+    shard_degree: Optional[int] = None
+    backward_fetch_policy: Optional[str] = None
+    auto_wrap_policy: Optional[str] = None
+    limit_all_gathers: Optional[bool] = None
+    use_orig_param: Optional[bool] = None
+
+    # FP8 config
+    fp8: Optional[bool] = None
+    fp8_amax_history_len: Optional[int] = None
+    fp8_amax_compute_algo: Optional[str] = None
+
+    # Model architecture
+    max_context_width: Optional[int] = None
+    max_position_embeddings: Optional[int] = None
+    num_hidden_layers: Optional[int] = None
+    hidden_size: Optional[int] = None
+    num_attention_heads: Optional[int] = None
+    intermediate_size: Optional[int] = None
+    initializer_range: Optional[float] = None
+    layernorm_epsilon: Optional[float] = None
+    vocab_size: Optional[int] = None
+    num_key_value_heads: Optional[int] = None
+    use_flash_attention: Optional[bool] = None
+    rope_theta: Optional[float] = None
+    rope_scaling: Optional[RopeScalingConfig] = None
+
+    # Finetuning config
+    do_finetune: Optional[bool] = None
+    hf_model_name_or_path: Optional[str] = None
+    hf_access_token: Optional[str] = None
+    peft: Optional[PeftConfig] = None
+
+    precision: Optional[str] = None
+    lr_decay_iters: Optional[int] = None
+    optim: Optional[OptimizerConfig] = None
+    data: Optional[DataConfig] = None
+    viztracer: Optional[VizTracerConfig] = None
+    dpo: Optional[DpoConfig] = None
+
+    # Megatron-specific fields
+    mcore_gpt: Optional[bool] = None
+    micro_batch_size: Optional[int] = None
+    global_batch_size: Optional[int] = None
+    rampup_batch_size: Optional[int] = None
+    pipeline_model_parallel_size: Optional[int] = None
+    virtual_pipeline_model_parallel_size: Optional[int] = None
+    encoder_seq_length: Optional[int] = None
+    ffn_hidden_size: Optional[int] = None
+    num_query_groups: Optional[int] = None
+    init_method_std: Optional[float] = None
+    use_scaled_init_method: Optional[bool] = None
+    kv_channels: Optional[int] = None
+    apply_query_key_layer_scaling: Optional[bool] = None
+    normalization: Optional[str] = None
+    do_layer_norm_weight_decay: Optional[bool] = None
+    make_vocab_size_divisible_by: Optional[int] = None
+    pre_process: Optional[bool] = None
+    post_process: Optional[bool] = None
+    persist_layer_norm: Optional[bool] = None
+    bias: Optional[bool] = None
+    activation: Optional[str] = None
+    headscale: Optional[bool] = None
+    transformer_block_type: Optional[str] = None
+    openai_gelu: Optional[bool] = None
+    normalize_attention_scores: Optional[bool] = None
+    position_embedding_type: Optional[str] = None
+    rotary_percentage: Optional[float] = None
+    apply_rope_fusion: Optional[bool] = None
+    cross_entropy_loss_fusion: Optional[bool] = None
+    attention_type: Optional[str] = None
+    share_embeddings_and_output_weights: Optional[bool] = None
+    scale_positional_embedding: Optional[bool] = None
+    tokenizer: Optional[TokenizerConfig] = None
+    native_amp_init_scale: Optional[int] = None
+    native_amp_growth_interval: Optional[int] = None
+    hysteresis: Optional[int] = None
+    fp32_residual_connection: Optional[bool] = None
+    fp16_lm_cross_entropy: Optional[bool] = None
+    megatron_amp_O2: Optional[bool] = None
+    grad_allreduce_chunk_size_mb: Optional[int] = None
+    grad_div_ar_fusion: Optional[bool] = None
+    gradient_accumulation_fusion: Optional[bool] = None
+    bias_activation_fusion: Optional[bool] = None
+    bias_dropout_add_fusion: Optional[bool] = None
+    masked_softmax_fusion: Optional[bool] = None
+    resume_from_checkpoint: Optional[str] = None
+    use_cpu_initialization: Optional[bool] = None
+    onnx_safe: Optional[bool] = None
+    apex_transformer_log_level: Optional[int] = None
+    gradient_as_bucket_view: Optional[bool] = None
+    sync_batch_comm: Optional[bool] = None
+    activations_checkpoint_granularity: Optional[str] = None
+    activations_checkpoint_method: Optional[str] = None
+    activations_checkpoint_num_layers: Optional[int] = None
+    num_micro_batches_with_partial_activation_checkpoints: Optional[int] = None
+    activations_checkpoint_layers_per_pipeline: Optional[int] = None
+    sequence_parallel: Optional[bool] = None
+    deterministic_mode: Optional[bool] = None
+    transformer_engine: Optional[bool] = None
+    fp8_e4m3: Optional[bool] = None
+    fp8_hybrid: Optional[bool] = None
+    fp8_margin: Optional[int] = None
+    fp8_interval: Optional[int] = None
+    use_emha: Optional[bool] = None
+    ub_tp_comm_overlap: Optional[bool] = None
+
+
+class EvaluationConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    task: Optional[str] = None
+    strategy: Optional[str] = None
+    metric: Optional[str] = None
+    subtask: Optional[str] = None
+
+
+class InferenceConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    max_new_tokens: Optional[int] = None
+    top_k: Optional[int] = None
+    top_p: Optional[float] = None
+    temperature: Optional[float] = None
+
+
+class HfRecipeSchema(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    # Common configurations
+    run: RunConfig
+
+    # Training and fine-tuning specific configurations
+    trainer: Optional[TrainerConfig] = None
+    exp_manager: Optional[ExpManagerConfig] = None
+    use_smp_model: Optional[bool] = None
+    distributed_backend: Optional[str] = None
+    model: Optional[ModelConfig] = None
+
+    # Evaluation specific configurations
+    evaluation: Optional[EvaluationConfig] = None
+
+    # Inference specific configurations
+    inference: Optional[InferenceConfig] = None
diff --git a/hyperpod-pytorch-job-template/hyperpod_pytorch_job_template/v1_0/recipes/neuron_hf/__init__.py b/hyperpod-pytorch-job-template/hyperpod_pytorch_job_template/v1_0/recipes/neuron_hf/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/hyperpod-pytorch-job-template/hyperpod_pytorch_job_template/v1_0/recipes/neuron_hf/model.py b/hyperpod-pytorch-job-template/hyperpod_pytorch_job_template/v1_0/recipes/neuron_hf/model.py
new file mode 100644
index 00000000..3e1a1d88
--- /dev/null
+++ b/hyperpod-pytorch-job-template/hyperpod_pytorch_job_template/v1_0/recipes/neuron_hf/model.py
@@ -0,0 +1,182 @@
+from typing import Any, Dict, List, Optional, Union
+from pydantic import BaseModel, ConfigDict
+
+
+class RunConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    name: Optional[str] = None
+    results_dir: Optional[str] = None
+    time_limit: Optional[str] = None
+    model_type: Optional[str] = None
+    compile: Optional[int] = None
+
+
+class TrainerConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    devices: Optional[int] = None
+    num_nodes: Optional[int] = None
+    max_epochs: Optional[int] = None
+    max_steps: Optional[int] = None
+    log_every_n_steps: Optional[int] = None
+    val_check_interval: Optional[int] = None
+    check_val_every_n_epoch: Optional[int] = None
+    num_sanity_val_steps: Optional[int] = None
+    limit_val_batches: Optional[float] = None
+    limit_test_batches: Optional[float] = None
+    gradient_clip_val: Optional[float] = None
+
+
+class CheckpointCallbackParamsConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    monitor: Optional[str] = None
+    save_top_k: Optional[int] = None
+    mode: Optional[str] = None
+    save_last: Optional[bool] = None
+    filename: Optional[str] = None
+    model_parallel_size: Optional[int] = None
+    every_n_train_steps: Optional[int] = None
+
+
+class ExpManagerConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    log_local_rank_0_only: Optional[bool] = None
+    create_tensorboard_logger: Optional[bool] = None
+    summary_writer_kwargs: Optional[Dict[str, str]] = None
+    create_mlflow_logger: Optional[bool] = None
+    mlflow_logger_kwargs: Optional[Dict[str, str]] = None
+    create_wandb_logger: Optional[bool] = None
+    wandb_logger_kwargs: Optional[Dict[str, str]] = None
+    explicit_log_dir: Optional[str] = None
+    exp_dir: Optional[str] = None
+    name: Optional[str] = None
+    resume_if_exists: Optional[bool] = None
+    resume_ignore_no_checkpoint: Optional[bool] = None
+    create_checkpoint_callback: Optional[bool] = None
+    checkpoint_callback_params: Optional[CheckpointCallbackParamsConfig] = None
+    log_parameter_norm: Optional[bool] = None
+    log_gradient_norm: Optional[bool] = None
+    enable_recovery_time_instrumentation: Optional[bool] = None
+    save_xser: Optional[bool] = None
+    load_xser: Optional[bool] = None
+    save_bf16: Optional[bool] = None
+    async_checkpointing: Optional[bool] = None
+    resume_from_checkpoint: Optional[str] = None
+
+
+class DistributedStrategyConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    tensor_model_parallel_size: Optional[int] = None
+    pipeline_model_parallel_size: Optional[int] = None
+    virtual_pipeline_model_parallel_size: Optional[int] = None
+    zero1: Optional[bool] = None
+    sequence_parallel: Optional[bool] = None
+    kv_replicator: Optional[int] = None
+
+
+class TokenizerConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    type: Optional[str] = None
+
+
+class DataConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    micro_batch_size: Optional[int] = None
+    global_batch_size: Optional[int] = None
+    train_dir: Optional[str] = None
+    val_dir: Optional[str] = None
+    packing: Optional[bool] = None
+    use_sft_style_data_module: Optional[bool] = None
+    dev_choose_samples: Optional[int] = None
+    seq_length: Optional[int] = None
+    tokenizer: Optional[TokenizerConfig] = None
+
+
+class SchedulerConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    name: Optional[str] = None
+    warmup_steps: Optional[int] = None
+    max_steps: Optional[int] = None
+
+
+class OptimizerConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    name: Optional[str] = None
+    lr: Optional[float] = None
+    weight_decay: Optional[float] = None
+    capturable: Optional[bool] = None
+    betas: Optional[List[float]] = None
+    sched: Optional[SchedulerConfig] = None
+
+
+class FusionsConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    softmax: Optional[bool] = None
+    flash_attention: Optional[bool] = None
+
+
+class ModelConfig(BaseModel):
+    # Allow additional field as some recipes have "model_config" field and
+    # this name is not allowed in Pydantic
+    model_config = ConfigDict(extra="allow")
+
+    encoder_seq_length: Optional[int] = None
+    max_position_embeddings: Optional[int] = None
+    num_layers: Optional[int] = None
+    hidden_size: Optional[int] = None
+    qkv_linear: Optional[bool] = None
+    rope_theta: Optional[float] = None
+    use_cpu_initialization: Optional[bool] = None
+    weight_init_only: Optional[bool] = None
+    activations_checkpoint_granularity: Optional[str] = None
+    fusions: Optional[FusionsConfig] = None
+    do_layer_norm_weight_decay: Optional[bool] = None
+    optim: Optional[OptimizerConfig] = None
+
+
+class PrecisionConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    type: Optional[str] = None
+    master_weights: Optional[bool] = None
+    fp32_grad_acc: Optional[bool] = None
+    xla_use_bf16: Optional[str] = None
+    xla_downcast_bf16: Optional[str] = None
+    neuron_rt_stochastic_rounding_en: Optional[str] = None
+
+
+class NeuronHfRecipeSchema(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    """Schema for neuron-hf SageMaker HyperPod recipes."""
+
+    # Common configurations
+    run: RunConfig
+    name: Optional[str] = None
+    model_source: Optional[str] = None
+    seed: Optional[int] = None
+
+    # Training configurations
+    trainer: Optional[TrainerConfig] = None
+    exp_manager: Optional[ExpManagerConfig] = None
+    distributed_strategy: Optional[DistributedStrategyConfig] = None
+    data: Optional[DataConfig] = None
+    model: Optional[ModelConfig] = None
+    precision: Optional[PrecisionConfig] = None
+
+    # Neuron-specific configurations
+    compiler_flags: Optional[str] = None
+    compiler_cache_url: Optional[str] = None
+    aync_exec_max_inflight_requests: Optional[int] = None
+    bucket_size_collectives: Optional[int] = None
+    neuron_rt_exec_timeout: Optional[int] = None
+    neuron_experimental_compress_rg: Optional[bool] = None
diff --git a/hyperpod-pytorch-job-template/hyperpod_pytorch_job_template/v1_0/recipes/nova/__init__.py b/hyperpod-pytorch-job-template/hyperpod_pytorch_job_template/v1_0/recipes/nova/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/hyperpod-pytorch-job-template/hyperpod_pytorch_job_template/v1_0/recipes/nova/model.py b/hyperpod-pytorch-job-template/hyperpod_pytorch_job_template/v1_0/recipes/nova/model.py
new file mode 100644
index 00000000..7c75cb79
--- /dev/null
+++ b/hyperpod-pytorch-job-template/hyperpod_pytorch_job_template/v1_0/recipes/nova/model.py
@@ -0,0 +1,167 @@
+from typing import List, Optional
+from pydantic import BaseModel, ConfigDict
+
+
+class RunConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    name: Optional[str] = None
+    model_type: Optional[str] = None
+    model_name_or_path: Optional[str] = None
+    replicas: Optional[int] = None
+    data_s3_path: Optional[str] = None
+    output_s3_path: Optional[str] = None
+
+    # PPO-specific replica configurations
+    actor_train_replicas: Optional[int] = None
+    rm_replicas: Optional[int] = None
+    cm_replicas: Optional[int] = None
+    actor_generation_replicas: Optional[int] = None
+    am_replicas: Optional[int] = None
+
+
+class TrainerConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    max_epochs: Optional[int] = None
+    num_nodes: Optional[int] = None
+
+
+class SchedulerConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    warmup_steps: Optional[int] = None
+    constant_steps: Optional[int] = None
+    min_lr: Optional[float] = None
+
+
+class OptimizerConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    name: Optional[str] = None
+    lr: Optional[float] = None
+    adam_w_mode: Optional[bool] = None
+    eps: Optional[float] = None
+    weight_decay: Optional[float] = None
+    betas: Optional[List[float]] = None
+    sched: Optional[SchedulerConfig] = None
+
+
+class DpoConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    beta: Optional[float] = None
+
+
+class LoraTuningConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    loraplus_lr_ratio: Optional[float] = None
+    alpha: Optional[float] = None
+    adapter_dropout: Optional[float] = None
+
+
+class PeftConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    peft_scheme: Optional[str] = None
+    lora_tuning: Optional[LoraTuningConfig] = None
+
+
+class ModelConfig(BaseModel):
+    hidden_dropout: Optional[float] = None
+    attention_dropout: Optional[float] = None
+    ffn_dropout: Optional[float] = None
+    optim: Optional[OptimizerConfig] = None
+    dpo_cfg: Optional[DpoConfig] = None
+    peft: Optional[PeftConfig] = None
+    global_batch_size: Optional[int] = None
+    ent_coeff: Optional[float] = None
+    clip_ratio: Optional[float] = None
+    lam: Optional[float] = None
+    kl_loss_coeff: Optional[float] = None
+    kl_loss_type: Optional[str] = None
+    kl_reward_penalty_coeff: Optional[float] = None
+
+
+class TrainingConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    max_length: Optional[int] = None
+    global_batch_size: Optional[int] = None
+    trainer: Optional[TrainerConfig] = None
+    model: Optional[ModelConfig] = None
+
+    # Distillation-specific fields
+    distillation_data: Optional[str] = None
+    maxNumberOfPrompts: Optional[str] = None
+    maxResponseLength: Optional[str] = None
+    minNumberOfPrompts: Optional[str] = None
+    maxInputFileSizeInGB: Optional[str] = None
+    maxLineLengthInKB: Optional[str] = None
+    maxStudentModelFineTuningContextLengthInTokens: Optional[str] = None
+    teacherModelId: Optional[str] = None
+    temperature: Optional[str] = None
+    top_p: Optional[str] = None
+    customer_bucket: Optional[str] = None
+    kms_key: Optional[str] = None
+
+
+class PpoRewardConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    max_length: Optional[int]
+    trainer: Optional[TrainerConfig] = None
+    model: Optional[ModelConfig] = None
+
+
+class PpoCriticConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    max_length: Optional[int]
+    trainer: Optional[TrainerConfig] = None
+    model: Optional[ModelConfig] = None
+
+
+class PpoAnchorConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    max_length: Optional[int]
+    trainer: Optional[TrainerConfig] = None
+    model: Optional[ModelConfig] = None
+
+
+class PpoActorGenerationConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    actor_model_max_length: Optional[int]
+    trainer: Optional[TrainerConfig] = None
+
+
+class PpoActorTrainConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    max_length: Optional[int] = None
+    max_steps: Optional[int] = None
+    actor_model_max_length: Optional[int] = None
+    reward_model_max_length: Optional[int] = None
+    trajectory_buffer_scale: Optional[int] = None
+    trainer: Optional[TrainerConfig] = None
+    model: Optional[ModelConfig] = None
+
+
+class NovaRecipeSchema(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    # Common configurations
+    run: RunConfig
+
+    # Training and fine-tuning specific configurations
+    training_config: Optional[TrainingConfig] = None
+
+    # PPO-specific configurations
+    ppo_reward: Optional[PpoRewardConfig] = None
+    ppo_critic: Optional[PpoCriticConfig] = None
+    ppo_anchor: Optional[PpoAnchorConfig] = None
+    ppo_actor_generation: Optional[PpoActorGenerationConfig] = None
+    ppo_actor_train: Optional[PpoActorTrainConfig] = None
diff --git a/hyperpod-pytorch-job-template/hyperpod_pytorch_job_template/v1_0/recipes/nova_evaluation/__init__.py b/hyperpod-pytorch-job-template/hyperpod_pytorch_job_template/v1_0/recipes/nova_evaluation/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/hyperpod-pytorch-job-template/hyperpod_pytorch_job_template/v1_0/recipes/nova_evaluation/model.py b/hyperpod-pytorch-job-template/hyperpod_pytorch_job_template/v1_0/recipes/nova_evaluation/model.py
new file mode 100644
index 00000000..82dcf714
--- /dev/null
+++ b/hyperpod-pytorch-job-template/hyperpod_pytorch_job_template/v1_0/recipes/nova_evaluation/model.py
@@ -0,0 +1,39 @@
+from typing import Optional
+from pydantic import BaseModel, ConfigDict
+
+
+class RunConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    name: Optional[str] = None
+    model_type: Optional[str] = None
+    model_name_or_path: Optional[str] = None
+    replicas: Optional[int] = None
+    data_s3_path: Optional[str] = None
+    output_s3_path: Optional[str] = None
+
+
+class EvaluationConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    task: Optional[str] = None
+    strategy: Optional[str] = None
+    metric: Optional[str] = None
+    subtask: Optional[str] = None
+
+
+class InferenceConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    top_k: Optional[int] = None
+    top_p: Optional[float] = None
+    temperature: Optional[float] = None
+    max_new_tokens: Optional[int] = None
+
+
+class NovaEvaluationRecipeSchema(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    run: RunConfig
+    evaluation: EvaluationConfig
+    inference: Optional[InferenceConfig] = None
diff --git a/src/sagemaker/hyperpod/cli/recipe_utils/recipe_loader.py b/src/sagemaker/hyperpod/cli/recipe_utils/recipe_loader.py
index 0f06d79b..e6feab8e 100644
--- a/src/sagemaker/hyperpod/cli/recipe_utils/recipe_loader.py
+++ b/src/sagemaker/hyperpod/cli/recipe_utils/recipe_loader.py
@@ -1,62 +1,109 @@
 import os
 import yaml
-from typing import Dict, Any, Optional
+from hyperpod_pytorch_job_template.v1_0.recipes.hf.model import HfRecipeSchema
+from hyperpod_pytorch_job_template.v1_0.recipes.neuron_hf.model import (
+    NeuronHfRecipeSchema,
+)
+from hyperpod_pytorch_job_template.v1_0.recipes.nova.model import NovaRecipeSchema
+from hyperpod_pytorch_job_template.v1_0.recipes.nova_evaluation.model import (
+    NovaEvaluationRecipeSchema,
+)
 
-from sagemaker.hyperpod.cli.recipe_utils.recipe_schema import RecipeSchema
 
-
-# from recipe_schema import RecipeSchema
-
-
-def load_recipe(recipe_path: str) -> RecipeSchema:
+def load_recipe(
+    recipe_path: str,
+) -> (
+    HfRecipeSchema
+    | NeuronHfRecipeSchema
+    | NovaRecipeSchema
+    | NovaEvaluationRecipeSchema
+):
     """
     Load and validate a recipe YAML file using the RecipeSchema.
-    
+
     Args:
         recipe_path: Path to the recipe YAML file
-        
+
     Returns:
         RecipeSchema: Validated recipe object
-        
+
     Raises:
         FileNotFoundError: If the recipe file doesn't exist
         ValueError: If the recipe file is invalid
     """
     if not os.path.exists(recipe_path):
         raise FileNotFoundError(f"Recipe file not found: {recipe_path}")
-    
+
     try:
-        with open(recipe_path, 'r') as f:
+        with open(recipe_path, "r") as f:
             recipe_data = yaml.safe_load(f)
-        
-        # Validate and return the recipe
-        return RecipeSchema(**recipe_data)
+
+            if "run" in recipe_data and "model_type" in recipe_data["run"]:
+                model_type = recipe_data["run"]["model_type"]
+
+                if model_type == "hf":
+                    return HfRecipeSchema(**recipe_data)
+                elif model_type == "neuron-hf":
+                    return NeuronHfRecipeSchema(**recipe_data)
+                elif "nova" in model_type and "evaluation" in recipe_data:
+                    return NovaEvaluationRecipeSchema(**recipe_data)
+                elif "nova" in model_type:
+                    return NovaRecipeSchema(**recipe_data)
+                else:
+                    raise Exception("Invalid model_type {model_type}")
+            else:
+                # there are 3 yaml without model_type
+                try:
+                    # recipes/training/llama/megatron_llama3_1_8b_nemo.yaml
+                    return HfRecipeSchema(**recipe_data)
+                except:
+                    pass
+
+                try:
+                    # recipes/fine-tuning/nova/nova_premier_r5_cpu_distill.yaml
+                    # recipes/fine-tuning/nova/nova_pro_r5_cpu_distill.yaml
+                    return NovaRecipeSchema(**recipe_data)
+                except:
+                    pass
+
+                raise Exception(
+                    "Cannot validate recipe with existing templates. Check your recipe.yaml file."
+                )
+
     except yaml.YAMLError as e:
         raise ValueError(f"Invalid YAML in recipe file: {e}")
     except Exception as e:
         raise ValueError(f"Error validating recipe: {e}")
 
 
-def save_recipe(recipe: RecipeSchema, output_path: str) -> None:
+def save_recipe(
+    recipe: (
+        HfRecipeSchema
+        | NeuronHfRecipeSchema
+        | NovaRecipeSchema
+        | NovaEvaluationRecipeSchema
+    ),
+    output_path: str,
+) -> None:
     """
     Save a recipe object to a YAML file.
-    
+
     Args:
-        recipe: RecipeSchema object
+        recipe: schema object
         output_path: Path to save the YAML file
-        
+
     Raises:
         ValueError: If the recipe can't be saved
     """
     try:
         # Convert to dict, excluding None values
         recipe_dict = recipe.model_dump(exclude_none=True)
-        
+
         # Create directory if it doesn't exist
         os.makedirs(os.path.dirname(output_path), exist_ok=True)
-        
+
         # Save to YAML
-        with open(output_path, 'w') as f:
+        with open(output_path, "w") as f:
             yaml.dump(recipe_dict, f, default_flow_style=False)
     except Exception as e:
-        raise ValueError(f"Error saving recipe: {e}")
\ No newline at end of file
+        raise ValueError(f"Error saving recipe: {e}")