# Config Reference

A complete list of all configuration options.

    # This is the huggingface model that contains *.pt, *.safetensors, or *.bin files
    # This can also be a relative path to a model on disk
    base_model: ./llama-7b-hf
    # You can specify an ignore pattern if the model repo contains more than 1 model type (*.pt, etc)
    base_model_ignore_patterns:
    # If the base_model repo on hf hub doesn't include configuration .json files,
    # You can set that here, or leave this empty to default to base_model
    base_model_config: ./llama-7b-hf
    # You can specify to choose a specific model revision from huggingface hub
    revision_of_model:
    # Optional tokenizer configuration path in case you want to use a different tokenizer
    # than the one defined in the base model
    tokenizer_config:
    # If you want to specify the type of model to load, AutoModelForCausalLM is a good choice too
    model_type: AutoModelForCausalLM
    # Corresponding tokenizer for the model AutoTokenizer is a good choice
    tokenizer_type: AutoTokenizer
    # Trust remote code for untrusted source
    trust_remote_code:
    # use_fast option for tokenizer loading from_pretrained, default to True
    tokenizer_use_fast:
    # Whether to use the legacy tokenizer setting, defaults to True
    tokenizer_legacy:
    # Resize the model embeddings when new tokens are added to multiples of 32
    # This is reported to improve training speed on some models
    resize_token_embeddings_to_32x:
    # Optional[bool] Whether to shrink the embeddings to len(tokenizer). By default, we won't shrink.
    shrink_embeddings:
    # Whether to load the model with randomly initialized weights. Useful for
    # pre-training a model from scratch or debugging purposes.
    random_init_weights:

    # (Internal use only)
    # Used to identify which the model is based on
    is_falcon_derived_model:
    is_llama_derived_model:
    is_qwen_derived_model:
    # Please note that if you set this to true, `padding_side` will be set to "left" by default
    is_mistral_derived_model:

    # optional overrides to the base model configuration
    overrides_of_model_config:
      # RoPE Scaling https://github.com/huggingface/transformers/pull/24653
      rope_scaling:
        type: # linear | dynamic
        factor: # float

    # optional overrides the base model loading from_pretrained
    overrides_of_model_kwargs:
      # use_cache: False

    # optional overrides to the bnb 4bit quantization configuration
    # https://huggingface.co/docs/transformers/main/main_classes/quantization#transformers.BitsAndBytesConfig
    bnb_config_kwargs:
      # These are default values
      llm_int8_has_fp16_weight: false
      bnb_4bit_quant_type: nf4
      bnb_4bit_use_double_quant: true

    # Whether you are training a 4-bit GPTQ quantized model
    gptq: true

    # This will attempt to quantize the model down to 8 bits and use adam 8 bit optimizer
    load_in_8bit: true
    # Use bitsandbytes 4 bit
    load_in_4bit:

    # Use CUDA bf16
    bf16: true # bool or 'full' for `bf16_full_eval`. require >=ampere
    # Use CUDA fp16
    fp16: true
    # Use CUDA tf32
    tf32: true # require >=ampere

    # No AMP (automatic mixed precision)
    bfloat16: true # require >=ampere
    float16: true

    # Limit the memory for all available GPUs to this amount (if an integer, expressed in gigabytes); default: unset
    gpu_memory_limit: 20GiB
    # Do the LoRA/PEFT loading on CPU -- this is required if the base model is so large it takes up most or all of the available GPU VRAM, e.g. during a model and LoRA merge
    lora_on_cpu: true

    # List[str]. Add plugins to extend the pipeline.
    # See `src/axolotl/integrations` for the available plugins or doc below for more details.
    # https://axolotl-ai-cloud.github.io/axolotl/docs/custom_integrations.html
    plugins:
      # - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin

    # A list of one or more datasets to finetune the model with
    datasets:
      # HuggingFace dataset repo | s3://,gs:// path | "json" for local dataset, make sure to fill data_files
      - path: vicgalle/alpaca-gpt4
        # The type of prompt to use for training. [alpaca, gpteacher, oasst, reflection]
        type: alpaca # format | format:<prompt_style> (chat/instruct) | <prompt_strategies>.load_<load_fn>
        ds_type: # Optional[str] (json|arrow|parquet|text|csv) defines the datatype when path is a file
        data_files: # Optional[str] path to source data files

        shards: # Optional[int] split dataset into N pieces (use with shards_idx)
        shards_idx: # Optional[int] = 0 the index of sharded dataset to use

        preprocess_shards: # Optional[int] process dataset in N sequential chunks for memory efficiency (exclusive with `shards`)

        name: # Optional[str] name of dataset configuration to load
        train_on_split: train # Optional[str] name of dataset split to load from
        revision: # Optional[str] The specific revision of the dataset to use when loading from the Hugging Face Hub. This can be a commit hash, tag, or branch name. If not specified, the latest version will be used. This parameter is ignored for local datasets.
        trust_remote_code: # Optional[bool] Trust remote code for untrusted source

      # Custom user instruction prompt
      - path: repo
        type:
          # The below are defaults. only set what's needed if you use a different column name.
          system_prompt: ""
          system_format: "{system}"
          field_system: system
          field_instruction: instruction
          field_input: input
          field_output: output

          # Customizable to be single line or multi-line
          # Use {instruction}/{input} as key to be replaced
          # 'format' can include {input}
          format: |-
            User: {instruction} {input}
            Assistant:
          # 'no_input_format' cannot include {input}
          no_input_format: "{instruction} "

          # For `completion` datsets only, uses the provided field instead of `text` column
          field:

      # Using chat template
      - path: ...
        # Set type to `chat_template` to use this strategy
        type: chat_template
        # Specify the name of the chat template to use
        # The name of the chat template to use for training, following values are supported:
        # - tokenizer_default: Uses the chat template that is available in the tokenizer_config.json. If the chat template is not available in the tokenizer, it will raise an error. This is the default.
        # - alpaca/inst/chatml/gemma/cohere/llama3/phi_3/deepseek_v2/jamba: These chat templates are available in the axolotl codebase at src/axolotl/utils/chat_templates.py
        # - tokenizer_default_fallback_*: where * is the name of the chat template to fallback to if the tokenizer does not have a chat template else default to tokenizer. E.g. tokenizer_default_fallback_chatml.
        # - jinja: Uses a custom jinja template for the chat template. The custom jinja template should be provided in the chat_template_jinja field.
        chat_template: tokenizer_default

        # Custom jinja chat template. Used only if `chat_template: jinja` or empty.
        chat_template_jinja:

        # Key containing the messages (default: "messages")
        field_messages: messages

        # Mapping of properties from the input dataset to the chat template.
        # (default: message_property_mappings={'role':'role', 'content':'content'})
        # If a property exists in the template but not in this mapping, the system will attempt
        # to load it directly from the message using the property name as the key.
        # Example: In the mapping below, 'from' is loaded from input dataset and used as 'role',
        # while 'value' is loaded and used as 'content' in the chat template.
        message_property_mappings:
          role: from
          content: value
          # ...

        # Optional[Dict[str, List]]. Roles mapping in the messages. The default is:
        roles:
          user: ["human", "user"]
          assistant: ["gpt", "assistant"]
          system: ["system"]
          tool: ["tool"]

        # Optional[bool]. Whether to drop the system turn from the dataset. Only works with chat_template.
        # This does not drop the default system message from chat_template if it exists. If you wish to,
        # we recommend using a custom jinja template with the default system message removed or
        # adding a system turn with empty content.
        drop_system_message:

        # IMPORTANT: The following fields determine which parts of the conversation to train on.
        # Priority order: message_field_training > message_field_training_detail > train_on_inputs or role in roles_to_train
        # See examples at `docs/dataset-formats/conversation.qmd`
        # Note: If the below 4 fields are set to empty, defaults to training only on the last message.

        # Optional[List[str]]. Roles to train on. The tokens from these roles will be considered for the loss.
        roles_to_train: ["assistant"]  # default
        # Optional[str]. Which EOS tokens to train on in the conversation. Possible values are:
        # - all: train on all EOS tokens
        # - turn (default): train on the EOS token at the end of each trainable turn
        # - last: train on the last EOS token in the conversation
        # TIP: Please make sure that your `tokenizer.eos_token` is same as EOS/EOT token in template. Otherwise, set `eos_token` under `special_tokens`.
        train_on_eos: last
        # The key in the message turn that indicates via boolean whether tokens of a turn should be considered for training. Useful to selectively train on certain turns besides the `roles_to_train`.
        message_field_training: training
        # The key in the message turn that contains the training details. Useful to selectively train on certain tokens in a turn.
        # The value of the key is a List[Dict] containing `begin_offset` (start character index in content), `end_offset` (end character index in content), and `train` (boolean whether to train).
        message_field_training_detail: train_detail

    # If false, the datasets will not be shuffled and will keep their original order in `datasets`.
    # The same applies to the `test_datasets` option and the `pretraining_dataset` option. Default is true.
    shuffle_merged_datasets: true

    Deduplicates datasets and test_datasets with identical entries.
    dataset_exact_deduplication: true

    # A list of one or more datasets to eval the model with.
    # You can use either test_datasets, or val_set_size, but not both.
    test_datasets:
      - path: /workspace/data/eval.jsonl
        ds_type: json
        # You need to specify a split. For "json" datasets the default split is called "train".
        split: train
        type: completion
        data_files:
          - /workspace/data/eval.jsonl

    # use RL training: 'dpo', 'ipo', 'kto', 'simpo', 'orpo', 'grpo'
    rl:
    rl_beta:  # Optional[float]. The beta parameter for the RL training.

    # dpo
    dpo_use_weighting:  # Optional[bool]. Whether to perform weighting.
    rpo_alpha: # Optional[float]. Weighting of NLL term in loss from RPO paper.

    # orpo
    orpo_alpha: 0.1  # Parameter controlling the relative ratio loss weight in the ORPO loss. Passed to `beta` in `ORPOConfig` due to trl mapping.

    # kto
    kto_desirable_weight: # Optional[float]. Factor for desirable loss term in KTO loss.
    kto_undesirable_weight: # Optional[float]. Factor for undesirable loss term in KTO loss.

    # simpo
    cpo_alpha: 1.0  # Weight of the BC regularizer
    simpo_gamma: 0.5  # Target reward margin for the SimPO loss

    # grpo
    trl:
      use_vllm: # Optional[bool]. Whether to use VLLM for RL training.
      vllm_server_host: # Optional[str]. Host of the vLLM server to connect to.
      vllm_server_port: # Optional[int]. Port of the vLLM server to connect to.
      vllm_server_timeout: # Optional[int]. Total timeout (in seconds) to wait for the vLLM server to respond.
      vllm_guided_decoding_regex: # Optional[str]. Regex for vLLM guided decoding.

      beta: # Optional[float]. Beta parameter for the RL training. Same as `rl_beta`. Use
      max_completion_length: # Optional[int]. Maximum length of the completion for RL training.

      reward_funcs: # Optional[list[str]]. List of reward functions to load. Paths must be importable from current dir.
      reward_weights: # Optional[list[float]]. List of reward weights for the reward functions.

      num_generations: # Optional[int]. Number of generations to sample.
      log_completions: # Optional[bool]. Whether to log completions.

      sync_ref_model: # Optional[bool]. Whether to sync the reference model.
      ref_model_mixup_alpha: # Optional[float]. Mixup alpha for the reference model.
      ref_model_sync_steps: # Optional[int]. Sync steps for the reference model.

    # reward modelling: `True` or `False`
    reward_model:

    # process reward modelling: `True` or `False`
    process_reward_model:

    # The name of the chat template to use for training, following values are supported:
    # - tokenizer_default: Uses the chat template that is available in the tokenizer_config.json. If the chat template is not available in the tokenizer, it will raise an error. This is the default value.
    # - alpaca/inst/chatml/gemma/cohere/llama3/phi_3/deepseek_v2/jamba: These chat templates are available in the axolotl codebase at src/axolotl/utils/chat_templates.py
    # - tokenizer_default_fallback_*: where * is the name of the chat template to fallback to. E.g. tokenizer_default_fallback_chatml. This is useful when the chat template is not available in the tokenizer.
    # - jinja: Uses a custom jinja template for the chat template. The custom jinja template should be provided in the chat_template_jinja field.
    # The selected chat template will be saved to the tokenizer_config.json for easier inferencing
    # Note: It is recommended to set train_on_inputs to true when using a chat template that is different from the model's default chat template.
    chat_template: tokenizer_default
    # custom jinja template for chat template. This will be only used if chat_template is set to `jinja` or `null` (in which case chat_template is automatically set to `jinja`). Default is null.
    chat_template_jinja: null
    # Changes the default system message. Currently only supports chatml.
    default_system_message: You are a helpful assistant. Please give a long and detailed answer.
    # Axolotl attempts to save the dataset as an arrow after packing the data together so
    # subsequent training attempts load faster, relative path
    dataset_prepared_path: data/last_run_prepared
    # Push prepared dataset to hub
    push_dataset_to_hub: # Optional[str] repo_org/repo_name
    # The maximum number of processes to use while preprocessing your input dataset. This defaults to `os.cpu_count()`
    # if not set.
    dataset_processes: # defaults to os.cpu_count() if not set
    # Keep dataset in memory while preprocessing
    # Only needed if cached dataset is taking too much storage
    dataset_keep_in_memory:
    # push checkpoints to hub
    hub_model_id: # private repo path to push finetuned model
    # how to push checkpoints to hub
    # https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.hub_strategy
    hub_strategy:
    # Whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets
    # Required to be true when used in combination with `push_dataset_to_hub`
    hf_use_auth_token: # boolean
    # How much of the dataset to set aside as evaluation. 1 = 100%, 0.50 = 50%, etc. 0 for no eval.
    val_set_size: 0.04
    # Num shards for whole dataset
    dataset_shard_num:
    # Index of shard to use for whole dataset
    dataset_shard_idx:

    # The maximum length of an input to train with, this should typically be less than 2048
    # as most models have a token/context limit of 2048
    sequence_len: 2048
    # Pad inputs so each step uses constant sized buffers
    # This will reduce memory fragmentation and may prevent OOMs, by re-using memory more efficiently
    pad_to_sequence_len:
    # Use efficient multi-packing with block diagonal attention and per sequence position_ids. Recommend set to 'true'
    sample_packing:
    # Set to 'false' if getting errors during eval with sample_packing on.
    eval_sample_packing:
    # You can set these packing optimizations AFTER starting a training at least once.
    # The trainer will provide recommended values for these values.
    sample_packing_eff_est:
    total_num_tokens:
    # Increasing the following values helps with packing, but usually only slightly (<%1.)
    # The number of samples packed at a time.
    sample_packing_group_size: 100000
    # The number of samples which can be packed into one sequence. Increase if using a large sequence_len with many short samples.
    sample_packing_bin_size: 200
    # whether to concatenate samples during pretraining
    pretraining_sample_concatenation:

    # Use batch flattening for speedups when not using sample_packing
    batch_flattening:

    # Passed through to transformers when loading the model when launched without accelerate
    # Use `sequential` when training w/ model parallelism to limit memory
    device_map:
    # Defines the max memory usage per gpu on the system. Passed through to transformers when loading the model.
    max_memory:

    # If you want to use 'lora' or 'qlora' or leave blank to train all parameters in original model
    adapter: lora
    # If you already have a lora model trained that you want to load, put that here.
    # This means after training, if you want to test the model, you should set this to the value of `output_dir`.
    # Note that if you merge an adapter to the base model, a new subdirectory `merged` will be created under the `output_dir`.
    lora_model_dir:

    # LoRA hyperparameters
    # For more details about the following options, see:
    # https://www.anyscale.com/blog/fine-tuning-llms-lora-or-full-parameter-an-in-depth-analysis-with-llama-2
    lora_r: 8
    lora_alpha: 16
    lora_dropout: 0.05
    lora_target_modules:
      - q_proj
      - v_proj
    #  - k_proj
    #  - o_proj
    #  - gate_proj
    #  - down_proj
    #  - up_proj
    lora_target_linear: # If true, will target all linear modules
    peft_layers_to_transform: # The layer indices to transform, otherwise, apply to all layers

    # If you added new tokens to the tokenizer, you may need to save some LoRA modules because they need to know the new tokens.
    # For LLaMA and Mistral, you need to save `embed_tokens` and `lm_head`. It may vary for other models.
    # `embed_tokens` converts tokens to embeddings, and `lm_head` converts embeddings to token probabilities.
    # https://github.com/huggingface/peft/issues/334#issuecomment-1561727994
    lora_modules_to_save:
    #  - embed_tokens
    #  - lm_head

    lora_fan_in_fan_out: false

    # Apply custom LoRA autograd functions and activation function Triton kernels for
    # speed and memory savings
    # See: https://axolotl-ai-cloud.github.io/axolotl/docs/lora_optims.html
    lora_mlp_kernel: true
    lora_qkv_kernel: true
    lora_o_kernel: true

    # LoRA+ hyperparameters
    # For more details about the following options, see:
    # https://arxiv.org/abs/2402.12354  and `src/axolotl/core/train_builder.py`
    loraplus_lr_ratio: # loraplus learning rate ratio lr_B / lr_A. Recommended value is 2^4.
    loraplus_lr_embedding: #  loraplus learning rate for lora embedding layers. Default value is 1e-6.

    peft:
      # Configuration options for loftq initialization for LoRA
      # https://huggingface.co/docs/peft/developer_guides/quantization#loftq-initialization
      loftq_config:
        loftq_bits:  # typically 4 bits

    # ReLoRA configuration
    # Must use either 'lora' or 'qlora' adapter, and does not support fsdp or deepspeed
    relora_steps: # Number of steps per ReLoRA restart
    relora_warmup_steps: # Number of per-restart warmup steps
    relora_anneal_steps: # Number of anneal steps for each relora cycle
    relora_prune_ratio: # threshold for optimizer magnitude when pruning
    relora_cpu_offload: # True to perform lora weight merges on cpu during restarts, for modest gpu memory savings

    # wandb configuration if you're using it
    # Make sure your `WANDB_API_KEY` environment variable is set (recommended) or you login to wandb with `wandb login`.
    wandb_mode: # "offline" to save run metadata locally and not sync to the server, "disabled" to turn off wandb
    wandb_project: # Your wandb project name
    wandb_entity: # A wandb Team name if using a Team
    wandb_watch:
    wandb_name: # Set the name of your wandb run
    wandb_run_id: # Set the ID of your wandb run
    wandb_log_model: # "checkpoint" to log model to wandb Artifacts every `save_steps` or "end" to log only at the end of training

    # mlflow configuration if you're using it
    mlflow_tracking_uri: # URI to mlflow
    mlflow_experiment_name: # Your experiment name
    mlflow_run_name: # Your run name
    hf_mlflow_log_artifacts:  # set to true to copy each saved checkpoint on each save to mlflow artifact registry

    # Comet configuration if you're using it
    # Make sure your `COMET_API_KEY` environment variable is set (recommended) or you login to Comet with `comet login`.
    # Check out our documentation for more details https://www.comet.com/docs/v2/api-and-sdk/python-sdk/reference/Experiment-Creation/#comet_ml.start
    use_comet: # Enable or disable Comet integration.
    comet_api_key: # API key for Comet. Recommended to set via `comet login`.
    comet_workspace: # Workspace name in Comet. Defaults to the user's default workspace.
    comet_project_name: # Project name in Comet. Defaults to Uncategorized.
    comet_experiment_key: # Identifier for the experiment. Used to append data to an existing experiment or control the key of new experiments. Default to a random key.
    comet_mode: # Create a new experiment ("create") or log to an existing one ("get"). Default ("get_or_create") auto-selects based on configuration.
    comet_online: # Set to True to log data to Comet server, or False for offline storage. Default is True.
    comet_experiment_config: # Dictionary for additional configuration settings, see the doc for more details.

    # Tensorboard
    use_tensorboard: # Optional[bool]

    # Where to save the full-finetuned model to
    output_dir: ./completed-model

    # Whether to use torch.compile and which backend to use
    # setting to `auto` will enable torch compile when torch>=2.5.1
    torch_compile:  # Optional[Union[Literal["auto"], bool]]
    torch_compile_backend:  # Optional[str]

    # Training hyperparameters

    # If greater than 1, backpropagation will be skipped and the gradients will be accumulated for the given number of steps.
    gradient_accumulation_steps: 1
    # The number of samples to include in each batch. This is the number of samples sent to each GPU.
    # Batch size per gpu = micro_batch_size * gradient_accumulation_steps
    micro_batch_size: 2
    eval_batch_size:
    num_epochs: 4
    warmup_steps: 100  # cannot use with warmup_ratio
    warmup_ratio: 0.05  # cannot use with warmup_steps
    learning_rate: 0.00003
    lr_quadratic_warmup:
    logging_steps:
    eval_steps: # Leave empty to eval at each epoch, integer for every N steps. float for fraction of total steps
    evals_per_epoch: # number of times per epoch to run evals, mutually exclusive with eval_steps
    eval_strategy: # Set to `"no"` to skip evaluation, `"epoch"` at end of each epoch, leave empty to infer from `eval_steps`.
    save_strategy: # Set to `"no"` to skip checkpoint saves, `"epoch"` at end of each epoch, `"best"` when better result is achieved, leave empty to infer from `save_steps`.
    save_steps: # Leave empty to save at each epoch, integer for every N steps. float for fraction of total steps
    saves_per_epoch: # number of times per epoch to save a checkpoint, mutually exclusive with save_steps
    save_total_limit: # Checkpoints saved at a time
    # Maximum number of iterations to train for. It precedes num_epochs which means that
    # if both are set, num_epochs will not be guaranteed.
    # e.g., when 1 epoch is 1000 steps => `num_epochs: 2` and `max_steps: 100` will train for 100 steps
    max_steps:

    # bool of whether to include tokens trainer per second in the training metrics. This iterates over the entire dataset once, so it takes some time.
    include_tokens_per_second: # Optional[bool]

    # whether to find batch size that fits in memory. Passed to underlying transformers Trainer
    auto_find_batch_size: # Optional[bool]

    eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
    eval_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
    do_causal_lm_eval: # Whether to run causal language model evaluation for metrics in `eval_causal_lm_metrics`.
    eval_causal_lm_metrics: # HF evaluate metrics used during evaluation. Default is ["sacrebleu", "comet", "ter", "chrf", "perplexity"]

    profiler_steps: # enable the pytorch profiler to capture the first N steps of training to the output_dir.
                    # see https://pytorch.org/blog/understanding-gpu-memory-1/ for more information
                    # snapshots can be visualized @ https://pytorch.org/memory_viz

    loss_watchdog_threshold: # High loss value, indicating the learning has broken down (a good estimate is ~2 times the loss at the start of training)
    loss_watchdog_patience: # Number of high-loss steps in a row before the trainer aborts (default: 3)

    # Save model as safetensors (require safetensors package)
    save_safetensors:

    # Whether to mask out or include the human's prompt from the training labels
    train_on_inputs: false
    # Group similarly sized data to minimize padding.
    # May be slower to start, as it must download and sort the entire dataset.
    # Note that training loss may have an oscillating pattern with this enabled.
    group_by_length: false

    # Whether to use gradient checkpointing https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing
    gradient_checkpointing: false
    # additional kwargs to pass to the trainer for gradient checkpointing
    # gradient_checkpointing_kwargs:
    #   use_reentrant: true

    # Stop training after this many evaluation losses have increased in a row
    # https://huggingface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppingCallback
    early_stopping_patience: 3

    # Specify a scheduler and kwargs to use with the optimizer
    lr_scheduler: # 'one_cycle' | 'rex' | 'log_sweep' | empty for cosine
    lr_scheduler_kwargs:
    cosine_min_lr_ratio: # decay lr to some percentage of the peak lr, e.g. cosine_min_lr_ratio=0.1 for 10% of peak lr
    cosine_constant_lr_ratio: # freeze lr at some percentage of the step, e.g. cosine_constant_lr_ratio=0.8 means start cosine_min_lr at 80% of training step (https://arxiv.org/pdf/2308.04014.pdf)

    # For one_cycle optim
    lr_div_factor: # Learning rate div factor

    # Specify optimizer
    # Valid values are driven by the Transformers OptimizerNames class, see:
    # https://github.com/huggingface/transformers/blob/cbf924b76c03828101a34069a96d209314114fd5/src/transformers/training_args.py#L144-L189
    #
    # Note that not all optimizers may be available in your environment, ex: 'adamw_anyprecision' is part of
    # torchdistx, 'adamw_bnb_8bit' is part of bnb.optim.Adam8bit, etc. When in doubt, it is recommended to start with the optimizer used
    # in the examples/ for your model and fine-tuning use case.
    #
    # Valid values for 'optimizer' include:
    # - adamw_torch
    # - adamw_torch_fused
    # - adamw_torch_xla
    # - adamw_torch_npu_fused
    # - adamw_apex_fused
    # - adopt_adamw  (an EXPERIMENTAL optimizer, only for torch version >= 2.5.1)
    # - adafactor
    # - adamw_anyprecision
    # - adamw_torch_4bit
    # - ademamix
    # - sgd
    # - adagrad
    # - adamw_bnb_8bit
    # - adamw_8bit   # alias for adamw_bnb_8bit
    # - ademamix_8bit
    # - lion_8bit
    # - lion_32bit
    # - paged_adamw_32bit
    # - paged_adamw_8bit
    # - paged_ademamix_32bit
    # - paged_ademamix_8bit
    # - paged_lion_32bit
    # - paged_lion_8bit
    # - rmsprop
    # - rmsprop_bnb
    # - rmsprop_bnb_8bit
    # - rmsprop_bnb_32bit
    # - galore_adamw
    # - galore_adamw_8bit
    # - galore_adafactor
    # - galore_adamw_layerwise
    # - galore_adamw_8bit_layerwise
    # - galore_adafactor_layerwise
    # - lomo
    # - adalomo
    # - grokadamw
    # - schedule_free_adamw
    # - schedule_free_sgd
    # - apollo_adamw
    # - apollo_adamw_layerwise
    #
    # Additional custom optimizers include:
    # - optimi_adamw
    # - ao_adamw_8bit
    # - ao_adamw_fp8
    optimizer:
    # Dictionary of arguments to pass to the optimizer
    optim_args:
    # For Galore Optimizers the following optim_args are available
    # rank:  # type: int
    # update_proj_gap  # type: int
    # scale  # type: float
    # proj_type:  # type: str, default = std

    # The target modules to optimize, i.e. the module names that you would like to train, right now this is used only for GaLore algorithm
    optim_target_modules:
    # - self_attn  # for llama
    # - mlp

    # Specify weight decay
    weight_decay:
    # adamw hyperparams
    adam_beta1:
    adam_beta2:
    adam_epsilon:
    # Gradient clipping max norm
    max_grad_norm:

    # Augmentation techniques
    # NEFT https://arxiv.org/abs/2310.05914, set this to a number (paper default is 5) to add noise to embeddings
    # currently only supported on Llama and Mistral
    neftune_noise_alpha:

    # Optional[bool]. Whether to bettertransformers
    flash_optimum:

    # Note: Only one of the following attention patches can be used at a time.
    # For example, if you set `xformers_attention` to `true`, do not set `flash_attention` to `true`.

    # Optional[bool]. Whether to use xformers attention patch https://github.com/facebookresearch/xformers:
    xformers_attention:
    # Optional[bool]. Whether to use flash attention patch https://github.com/Dao-AILab/flash-attention:
    flash_attention:
    flash_attn_cross_entropy:  # Optional[bool]. Whether to use flash-attention cross entropy implementation - advanced use only
    flash_attn_rms_norm:  # Optional[bool]. Whether to use flash-attention rms norm implementation - advanced use only
    flash_attn_fuse_qkv: # Optional[bool]. Whether to fuse QKV into a single operation
    flash_attn_fuse_mlp: # Optional[bool]. Whether to fuse part of the MLP into a single operation
    # Optional[bool]. Whether to use scaled-dot-product attention
    # https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
    sdp_attention:
    # Optional[bool]. Shifted-sparse attention (only llama) - https://arxiv.org/pdf/2309.12307.pdf
    s2_attention:

    # Optional[bool]. Whether to use low_cpu_mem_usage
    low_cpu_mem_usage:
    # Optional[str]. Resume from a specific checkpoint dir
    resume_from_checkpoint:
    # Optional[bool]. If resume_from_checkpoint isn't set and you simply want it to start where it left off.
    # Be careful with this being turned on between different models.
    auto_resume_from_checkpoints: false

    ## Multimodal section
    # int | tuple[int, int] | None . Size to resize images to, width x height.
    # Will read from model/processor config if not set.
    image_size:
    # str. Algorithm to use for image resizing. "bilinear", "bicubic", "lanczos". Default is "bilinear".
    image_resize_algorithm: 'bilinear'
    ## End of multimodal section

    # Don't mess with this, it's here for accelerate and torchrun
    local_rank:

    # Add or change special tokens.
    # If you add tokens here, you don't need to add them to the `tokens` list.
    special_tokens:
      # bos_token: "<s>"
      # eos_token: "</s>"
      # unk_token: "<unk>"
      # pad_token: "[PAD]"

    # Add extra tokens.
    tokens:

    # Mapping token_id to new_token_string to override reserved added_tokens in the tokenizer.
    # Only works for tokens that are not part of the base vocab (aka are added_tokens).
    # Can be checked if they exist in tokenizer.json added_tokens.
    added_tokens_overrides:  # Dict[int, str]
    #  128041: "<|im_start|>"
    #  128042: "<|im_end|>"

    # FSDP
    fsdp:
    fsdp_config:

    # Deepspeed config path. e.g., deepspeed_configs/zero3.json
    deepspeed:

    # Advanced DDP Arguments
    ddp_timeout:
    ddp_bucket_cap_mb:
    ddp_broadcast_buffers:

    # Sequence parallelism
    # Set to a divisor of the number of GPUs available to split sequences into chunks of equal size.
    # Use in long context training to prevent OOM when sequences cannot fit into a single GPU's VRAM.
    # E.g., if 4 GPUs are available, set this value to 2 to split each sequence into two equal-sized
    # subsequences, or set to 4 to split into four equal-sized subsequences.
    # See https://axolotl-ai-cloud.github.io/axolotl/docs/sequence_parallelism.html for more details.
    sequence_parallel_degree:
    # Optional; strides across the key dimension. Larger values use more memory but should make training faster.
    # Must evenly divide the number of KV heads in your model.
    heads_k_stride: 1

    # Path to torch distx for optim 'adamw_anyprecision'
    torchdistx_path:

    # Set to HF dataset for type: 'completion' for streaming instead of pre-tokenize
    pretraining_dataset:

    # Debug mode
    debug:

    # Seed
    seed:

    # Allow overwrite yml config using from cli
    strict: