From 8e92c65700541b354582b3883c1ef765a15dab83 Mon Sep 17 00:00:00 2001
From: Quarto GHA Workflow Runner <quarto-github-actions-publish@example.com>
Date: Tue, 17 Mar 2026 03:55:08 +0000
Subject: [PATCH] Built site for gh-pages

---
 .nojekyll                  |   2 +-
 docs/config-reference.html |   4 +-
 index.html                 |   2 +-
 search.json                |   4 +-
 sitemap.xml                | 474 ++++++++++++++++++-------------------
 5 files changed, 243 insertions(+), 243 deletions(-)

diff --git a/.nojekyll b/.nojekyll
index 73268f02f..6fe214890 100644
--- a/.nojekyll
+++ b/.nojekyll
@@ -1 +1 @@
-3ace7c03
\ No newline at end of file
+4641f014
\ No newline at end of file
diff --git a/docs/config-reference.html b/docs/config-reference.html
index 18b714de3..23fbc0cc3 100644
--- a/docs/config-reference.html
+++ b/docs/config-reference.html
@@ -1506,8 +1506,8 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
 <span id="cb1-724"><a href="#cb1-724" aria-hidden="true" tabindex="-1"></a><span class="fu">bfloat16</span><span class="kw">:</span><span class="at"> bool | None</span></span>
 <span id="cb1-725"><a href="#cb1-725" aria-hidden="true" tabindex="-1"></a><span class="co"># No AMP (automatic mixed precision)</span></span>
 <span id="cb1-726"><a href="#cb1-726" aria-hidden="true" tabindex="-1"></a><span class="fu">float16</span><span class="kw">:</span><span class="at"> bool | None</span></span>
-<span id="cb1-727"><a href="#cb1-727" aria-hidden="true" tabindex="-1"></a><span class="co"># Use CUDA tf32 - require &gt;=ampere</span></span>
-<span id="cb1-728"><a href="#cb1-728" aria-hidden="true" tabindex="-1"></a><span class="fu">tf32</span><span class="kw">:</span><span class="at"> bool | None</span></span>
+<span id="cb1-727"><a href="#cb1-727" aria-hidden="true" tabindex="-1"></a><span class="co"># bool to use CUDA tf32 or 'auto' for automatic detection - require &gt;=ampere</span></span>
+<span id="cb1-728"><a href="#cb1-728" aria-hidden="true" tabindex="-1"></a><span class="fu">tf32</span><span class="kw">:</span><span class="at"> Literal['auto'] | bool | None = auto</span></span>
 <span id="cb1-729"><a href="#cb1-729" aria-hidden="true" tabindex="-1"></a><span class="fu">float32</span><span class="kw">:</span><span class="at"> bool | None</span></span>
 <span id="cb1-730"><a href="#cb1-730" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb1-731"><a href="#cb1-731" aria-hidden="true" tabindex="-1"></a><span class="co"># Whether to use gradient checkpointing. Available options are: true, false, 'offload',</span></span>
diff --git a/index.html b/index.html
index 2090ffb38..c3e544103 100644
--- a/index.html
+++ b/index.html
@@ -809,7 +809,7 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
 <ul>
 <li>2026/03:
 <ul>
-<li>New model support has been added in Axolotl for [<a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/qwen3.5">Qwen3.5, Qwen3.5 MoE</a>, <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/glm47-flash">GLM-4.7-Flash</a>, <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/glm46v">GLM-4.6V</a>, and <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/glm45">GLM-4.5-Air</a>.</li>
+<li>New model support has been added in Axolotl for <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/mistral4">Mistral Small 4</a>, <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/qwen3.5">Qwen3.5, Qwen3.5 MoE</a>, <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/glm47-flash">GLM-4.7-Flash</a>, <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/glm46v">GLM-4.6V</a>, and <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/glm45">GLM-4.5-Air</a>.</li>
 <li><a href="https://docs.axolotl.ai/docs/expert_quantization.html">MoE expert quantization</a> support (via <code>quantize_moe_experts: true</code>) greatly reduces VRAM when training MoE models (FSDP2 compat).</li>
 </ul></li>
 <li>2026/02:
diff --git a/search.json b/search.json
index d57266d48..5dec3784a 100644
--- a/search.json
+++ b/search.json
@@ -2660,7 +2660,7 @@
     "href": "docs/config-reference.html",
     "title": "Config Reference",
     "section": "",
-    "text": "# Allow overwrite yml config using from cli\nstrict: bool | None = False\n# Resume from a specific checkpoint dir\nresume_from_checkpoint: str | None\n# If resume_from_checkpoint isn't set and you simply want it to start where it left off.\n# Be careful with this being turned on between different models.\nauto_resume_from_checkpoints: bool | None\n# Resize the model embeddings when new tokens are added to multiples of 32. This is\n# reported to improve training speed on some models\nresize_token_embeddings_to_32x: bool | None\nmean_resizing_embeddings: bool | None = False\n\n# Whether to shrink the embeddings to len(tokenizer). By default, we won't shrink.\nshrink_embeddings: bool | None\n# Don't upcast the embeddings to float32 when using PEFT. Useful for low-VRAM GPUs\nembeddings_skip_upcast: bool | None\n# Reinitialize model weights randomly instead of loading pretrained weights\nreinit_weights: bool | None\n\n# module to custom trainer class to use for training\ntrainer_cls: str | None\n\n# Use RL training: 'dpo', 'ipo', 'kto', 'simpo', 'orpo', 'grpo'\nrl: RLType | None\n\ntrl: TRLConfig | None\n  # For TRLConfig:\n  # Beta parameter for the RL training. Same as `rl_beta`. Use\n  beta: float | None\n  # Maximum length of the completion for RL training.\n  max_completion_length: int | None\n\n  # Whether to use VLLM for RL training.\n  use_vllm: bool = False\n  # VLLM mode to use, one of 'server' or 'colocate'\n  vllm_mode: Literal['server', 'colocate'] | None\n  # Host of the vLLM server to connect to.\n  vllm_server_host: str | None = 0.0.0.0\n  # Port of the vLLM server to connect to.\n  vllm_server_port: int | None = 8000\n  # Total timeout (in seconds) to wait for the vLLM server to respond.\n  vllm_server_timeout: int | None\n  # Regex for vLLM guided decoding.\n  vllm_guided_decoding_regex: str | None\n\n  # List of reward functions to load. Paths must be importable from current dir.\n  reward_funcs: list[str] | None\n  # List of reward weights for the reward functions.\n  reward_weights: list[float] | None\n  # Number of generations to sample.\n  num_generations: int | None\n  # Whether to log completions.\n  log_completions: bool | None = False\n  # Number of completions to print when log_completions is True.\n  num_completions_to_print: int | None\n  # Controls whether importance sampling ratios are computed at the `'token'` or\n  # `'sequence'` level. For GSPO, use `sequence`, default is None which corresponds to\n  # the original GRPO paper.\n  importance_sampling_level: Literal['sequence', 'token'] | None\n\n  # Whether to sync the reference model.\n  sync_ref_model: bool | None = False\n  # Mixup alpha for the reference model.\n  ref_model_mixup_alpha: float | None = 0.9\n  # Sync steps for the reference model.\n  ref_model_sync_steps: int | None = 64\n  # Whether to scale rewards by their standard deviation.\n  scale_rewards: bool = True\n\n  # Sampling temperature for the GRPO policy.\n  temperature: float | None\n  # Top-p sampling probability for the generation policy.\n  top_p: float | None\n  # Top-k sampling for the generation policy.\n  top_k: int | None\n  # Minimum probability for the generation policy.\n  min_p: float | None\n  # Penalty for tokens that appear in prompt and generated text.\n  repetition_penalty: float | None\n  # Number of iterations per batch (μ) for GRPO.\n  num_iterations: int | None\n  # Epsilon value for clipping in the GRPO algorithm.\n  epsilon: float | None\n  # Upper-bound epsilon value for clipping in the GRPO algorithm.\n  epsilon_high: float | None\n  # Whether to use Liger loss for GRPO.\n  use_liger_loss: bool | None\n  # Loss formulation to use. Supported values: grpo, bnpo, dr_grpo.\n  loss_type: str | None\n  # Whether to exclude truncated completions from loss calculation.\n  mask_truncated_completions: bool = False\n  # Enable sleep mode for vLLM to offload VRAM when idle\n  vllm_enable_sleep_mode: bool | None\n  # Path to custom rollout function. Must be importable from current dir.\n  rollout_func: str | None\n  # Multi-objective reward aggregation strategy. 'sum_then_normalize' (GRPO default):\n  # weights and sums rewards first, then normalizes. 'normalize_then_sum' (GDPO):\n  # normalizes each reward independently, then sums.\n  multi_objective_aggregation: Literal['sum_then_normalize', 'normalize_then_sum'] | None\n\nvllm: VllmConfig | None\n  # For VllmConfig:\n  # Device to use for VLLM\n  device: str | None = auto\n  # Tensor parallel size for VLLM\n  tensor_parallel_size: int | None\n  # Data parallel size for VLLM\n  data_parallel_size: int | None\n  # GPU memory utilization for VLLM\n  gpu_memory_utilization: float | None = 0.9\n  # Data type for VLLM\n  dtype: str | None = auto\n  # Maximum length of the model context for VLLM\n  max_model_len: int | None\n  # Enable prefix caching for VLLM\n  enable_prefix_caching: bool | None\n  # Host for the vLLM server to start on\n  host: str | None = 0.0.0.0\n  # Port of the vLLM server to start on\n  port: int | None = 8000\n\n  # Enable reasoning for VLLM\n  enable_reasoning: bool | None\n  # Reasoning parser for VLLM\n  reasoning_parser: str | None\n\nqat: QATConfig | None\n  # For QATConfig:\n  # Fake quantization layout to use for activation quantization.\n  activation_dtype: TorchAOQuantDType | None\n  # Fake quantization layout to use for weight quantization.\n  weight_dtype: TorchAOQuantDType = TorchAOQuantDType.int8\n  # Quantize embedding\n  quantize_embedding: bool | None = False\n  # The number of elements in each group for per-group fake quantization\n  group_size: int | None = 32\n  # The number of steps to apply fake quantization after\n  fake_quant_after_n_steps: int | None\n\nquantization: PTQConfig | None\n  # For PTQConfig:\n  # Fake quantization layout to use for weight quantization.\n  weight_dtype: TorchAOQuantDType = TorchAOQuantDType.int8\n  # Fake quantization layout to use for activation quantization.\n  activation_dtype: TorchAOQuantDType | None\n  # Whether to quantize the embedding layer.\n  quantize_embedding: bool | None\n  # The number of elements in each group for per-group fake quantization\n  group_size: int | None = 32\n\n# Reward modelling: `True` or `False`\nreward_model: bool | None\n\n# Configuration for dynamic checkpointing (trigger by file or signal). Set 'enabled:\n# true' to activate this feature.\ndynamic_checkpoint: DynamicCheckpointConfig | None\n  # For DynamicCheckpointConfig:\n  # Enable dynamic checkpoint triggering during training. Create a file\n  # 'axolotl_checkpoint.save' in the configured `output_dir` to trigger.\n  enabled: bool = False\n  # Check for trigger file every N steps (reduces I/O overhead). Default: 100\n  check_interval: int = 10\n  # Custom trigger filename (optional). If not specified, defaults to\n  # 'axolotl_checkpoint.save'. Specify a filename (not a full path) to override the\n  # default.\n  trigger_file_path: str = \n\n# Process reward modelling: `True` or `False`\nprocess_reward_model: bool | None\n# Coefficient to incentivize the reward model to output mean-zero rewards (proposed by\n# https://huggingface.co/papers/2312.09244, Eq. 2). Recommended value: `0.01`.\ncenter_rewards_coefficient: float | None\nnum_labels: int | None\n\n# Whether to perform weighting in DPO trainer\ndpo_use_weighting: bool | None\ndpo_label_smoothing: float | None\ndpo_norm_loss: bool | None\n\n# Whether to use Liger kernel for DPO loss.\ndpo_use_liger_kernel: bool | None\n\ndpo_padding_free: bool | None\n\n# A list of one or more datasets to finetune the model with\ndatasets: Annotated[list[SFTDataset | DPODataset | KTODataset | StepwiseSupervisedDataset], MinLen(1)] | None\n  # For SFTDataset:\n  # HuggingFace dataset repo | s3:// | gs:// | path to local file or directory\n  path: str | None\n  # name of dataset split to load from\n  split: str | None\n  # The type of prompt to use for training. [alpaca, gpteacher, oasst, reflection]\n  type: str | UserDefinedPrompterType | None\n    # For UserDefinedPrompterType:\n    # Custom user instruction prompt\n    system_prompt: str | None\n    # Use {system} as key to be replaced\n    system_format: str | None\n    field_system: str | None\n    field_instruction: str | None\n    field_input: str | None\n    field_output: str | None\n\n    # Customizable to be single line or multi-line. Use {instruction}/{input} as key to\n    # be replaced. 'format' can include {input}\n    format: str | None\n    # 'no_input_format' cannot include {input}\n    no_input_format: str | None\n  input_transform: str | None\n  # split dataset into N pieces (use with shards_idx)\n  shards: int | None\n  # the index of sharded dataset to use\n  shards_idx: int | None\n  # process dataset in N sequential chunks for memory efficiency (exclusive with\n  # `shards`)\n  preprocess_shards: int | None\n  conversation: str | None\n\n  # The name of the chat template to use for training, following values are supported:\n  # tokenizer_default: Uses the chat template that is available in the\n  # tokenizer_config.json. If the chat template is not available in the tokenizer, it\n  # will raise an error. This is the default.\n  # alpaca/inst/chatml/gemma/cohere/llama3/phi_3/deepseek_v2/jamba: These chat templates\n  # are available in the axolotl codebase at src/axolotl/utils/chat_templates.py.\n  # tokenizer_default_fallback_*: where * is the name of the chat template to fallback\n  # to if the tokenizer does not have a chat template else default to tokenizer. E.g.\n  # tokenizer_default_fallback_chatml. jinja: Uses a custom jinja template for the chat\n  # template. The custom jinja template should be provided in the chat_template_jinja\n  # field.\n  chat_template: ChatTemplate | str | None\n  # Custom jinja chat template or path to jinja file. Used only if `chat_template:\n  # jinja` or empty.\n  chat_template_jinja: str | None\n  # path to source data files\n  data_files: str | list[str] | None\n  input_format: str | None\n  # name of dataset configuration to load\n  name: str | None\n  # defines the datatype when path is a file\n  ds_type: str | None\n  # For `completion` datasets only, uses the provided field instead of `text` column\n  field: str | None\n  field_human: str | None\n  field_model: str | None\n  # Key containing the messages (default: \"messages\")\n  field_messages: str | None\n  # Key containing the tools (default: \"tools\"). Must be a list[dict] and follow [JSON\n  # schema](https://json-schema.org/learn/getting-started-step-by-step).\n  field_tools: str | None\n  # Key containing the reasoning trace (default: \"reasoning_content\").\n  field_thinking: str | None\n  # The key the chat template expects that indicates the reasoning trace.\n  template_thinking_key: str | None\n\n  message_field_role: str | None\n\n  message_field_content: str | None\n  # Mapping of properties from the input dataset to the chat template. (default:\n  # message_property_mappings={'role':'role', 'content':'content'}) If a property exists\n  # in the template but not in this mapping, the system will attempt to load it directly\n  # from the message using the property name as the key. Example: In the mapping below,\n  # 'from' is loaded from input dataset and used as 'role', while 'value' is loaded and\n  # used as 'content' in the chat template.\n  message_property_mappings: dict[str, str] | None\n  # The key in the message turn that indicates via boolean whether tokens of a turn\n  # should be considered for training. Useful to selectively train on certain turns\n  # besides the `roles_to_train`.\n  message_field_training: str | None\n  # The key in the message turn that contains the training details. Useful to\n  # selectively train on certain tokens in a turn. The value of the key is a List[Dict]\n  # containing `begin_offset` (start character index in content), `end_offset` (end\n  # character index in content), and `train` (boolean whether to train).\n  message_field_training_detail: str | None\n  # (for Qwen3 template only) Whether to split the assistant content based on a\n  # reasoning trace inside delimited tags\n  split_thinking: bool | None\n  logprobs_field: str | None\n  temperature: float | None\n  # Roles to train on. The tokens from these roles will be considered for the loss.\n  roles_to_train: list[str] | None\n  # Which EOS tokens to train on in the conversation. Possible values are: all: train on\n  # all EOS tokens, turn (default): train on the EOS token at the end of each trainable\n  # turn, last: train on the last EOS token in the conversation\n  train_on_eos: Literal['all', 'turn', 'last'] | None\n  # Roles mapping in the messages. The format is {target_role: [source_roles]}. All\n  # source roles will be mapped to the target role. The default is: user: [\"human\",\n  # \"user\"], assistant: [\"gpt\", \"assistant\"], system: [\"system\"], tool: [\"tool\"]\n  roles: dict[str, list[str]] | None\n  # Whether to drop the system turn from the dataset. Only works with chat_template.\n  # This does not drop the default system message from chat_template if it exists. If\n  # you wish to, we recommend using a custom jinja template with the default system\n  # message removed or adding a system turn with empty content.\n  drop_system_message: bool | None\n  # Trust remote code for untrusted source\n  trust_remote_code: bool | None = False\n  # The specific revision of the dataset to use when loading from the Hugging Face Hub.\n  # This can be a commit hash, tag, or branch name. If not specified, the latest version\n  # will be used. This parameter is ignored for local datasets.\n  revision: str | None\n\n  # For DPODataset:\n  path: str | None\n  split: str | None\n  type: UserDefinedDPOType | str | None\n    # For UserDefinedDPOType:\n    field_system: str | None\n    field_prompt: str | None\n    field_chosen: str | None\n    field_rejected: str | None\n    prompt_format: str | None\n    chosen_format: str | None\n    rejected_format: str | None\n  data_files: list[str] | None\n  revision: str | None\n  field_messages: str | None\n\n  # For KTODataset:\n  path: str | None\n  split: str | None\n  type: UserDefinedKTOType | str | None\n    # For UserDefinedKTOType:\n    field_system: str | None\n    field_prompt: str | None\n    field_completion: str | None\n    field_label: bool | None\n    prompt_format: str | None\n    completion_format: str | None\n  data_files: list[str] | None\n  trust_remote_code: bool | None = False\n  revision: str | None\n\n  # For StepwiseSupervisedDataset:\n  path: str | None\n  split: str | None\n  data_files: list[str] | None\n  revision: str | None\n  step_separator: str | None\n  max_completion_length: int | None\n  train_on_last_step_only: bool | None\n\n# A list of one or more datasets to eval the model with. You can use either\n# test_datasets, or val_set_size, but not both.\ntest_datasets: Annotated[list[SFTDataset | DPODataset | KTODataset | StepwiseSupervisedDataset], MinLen(1)] | None\n  # For SFTDataset:\n  # HuggingFace dataset repo | s3:// | gs:// | path to local file or directory\n  path: str | None\n  # name of dataset split to load from\n  split: str | None\n  # The type of prompt to use for training. [alpaca, gpteacher, oasst, reflection]\n  type: str | UserDefinedPrompterType | None\n    # For UserDefinedPrompterType:\n    # Custom user instruction prompt\n    system_prompt: str | None\n    # Use {system} as key to be replaced\n    system_format: str | None\n    field_system: str | None\n    field_instruction: str | None\n    field_input: str | None\n    field_output: str | None\n\n    # Customizable to be single line or multi-line. Use {instruction}/{input} as key to\n    # be replaced. 'format' can include {input}\n    format: str | None\n    # 'no_input_format' cannot include {input}\n    no_input_format: str | None\n  input_transform: str | None\n  # split dataset into N pieces (use with shards_idx)\n  shards: int | None\n  # the index of sharded dataset to use\n  shards_idx: int | None\n  # process dataset in N sequential chunks for memory efficiency (exclusive with\n  # `shards`)\n  preprocess_shards: int | None\n  conversation: str | None\n\n  # The name of the chat template to use for training, following values are supported:\n  # tokenizer_default: Uses the chat template that is available in the\n  # tokenizer_config.json. If the chat template is not available in the tokenizer, it\n  # will raise an error. This is the default.\n  # alpaca/inst/chatml/gemma/cohere/llama3/phi_3/deepseek_v2/jamba: These chat templates\n  # are available in the axolotl codebase at src/axolotl/utils/chat_templates.py.\n  # tokenizer_default_fallback_*: where * is the name of the chat template to fallback\n  # to if the tokenizer does not have a chat template else default to tokenizer. E.g.\n  # tokenizer_default_fallback_chatml. jinja: Uses a custom jinja template for the chat\n  # template. The custom jinja template should be provided in the chat_template_jinja\n  # field.\n  chat_template: ChatTemplate | str | None\n  # Custom jinja chat template or path to jinja file. Used only if `chat_template:\n  # jinja` or empty.\n  chat_template_jinja: str | None\n  # path to source data files\n  data_files: str | list[str] | None\n  input_format: str | None\n  # name of dataset configuration to load\n  name: str | None\n  # defines the datatype when path is a file\n  ds_type: str | None\n  # For `completion` datasets only, uses the provided field instead of `text` column\n  field: str | None\n  field_human: str | None\n  field_model: str | None\n  # Key containing the messages (default: \"messages\")\n  field_messages: str | None\n  # Key containing the tools (default: \"tools\"). Must be a list[dict] and follow [JSON\n  # schema](https://json-schema.org/learn/getting-started-step-by-step).\n  field_tools: str | None\n  # Key containing the reasoning trace (default: \"reasoning_content\").\n  field_thinking: str | None\n  # The key the chat template expects that indicates the reasoning trace.\n  template_thinking_key: str | None\n\n  message_field_role: str | None\n\n  message_field_content: str | None\n  # Mapping of properties from the input dataset to the chat template. (default:\n  # message_property_mappings={'role':'role', 'content':'content'}) If a property exists\n  # in the template but not in this mapping, the system will attempt to load it directly\n  # from the message using the property name as the key. Example: In the mapping below,\n  # 'from' is loaded from input dataset and used as 'role', while 'value' is loaded and\n  # used as 'content' in the chat template.\n  message_property_mappings: dict[str, str] | None\n  # The key in the message turn that indicates via boolean whether tokens of a turn\n  # should be considered for training. Useful to selectively train on certain turns\n  # besides the `roles_to_train`.\n  message_field_training: str | None\n  # The key in the message turn that contains the training details. Useful to\n  # selectively train on certain tokens in a turn. The value of the key is a List[Dict]\n  # containing `begin_offset` (start character index in content), `end_offset` (end\n  # character index in content), and `train` (boolean whether to train).\n  message_field_training_detail: str | None\n  # (for Qwen3 template only) Whether to split the assistant content based on a\n  # reasoning trace inside delimited tags\n  split_thinking: bool | None\n  logprobs_field: str | None\n  temperature: float | None\n  # Roles to train on. The tokens from these roles will be considered for the loss.\n  roles_to_train: list[str] | None\n  # Which EOS tokens to train on in the conversation. Possible values are: all: train on\n  # all EOS tokens, turn (default): train on the EOS token at the end of each trainable\n  # turn, last: train on the last EOS token in the conversation\n  train_on_eos: Literal['all', 'turn', 'last'] | None\n  # Roles mapping in the messages. The format is {target_role: [source_roles]}. All\n  # source roles will be mapped to the target role. The default is: user: [\"human\",\n  # \"user\"], assistant: [\"gpt\", \"assistant\"], system: [\"system\"], tool: [\"tool\"]\n  roles: dict[str, list[str]] | None\n  # Whether to drop the system turn from the dataset. Only works with chat_template.\n  # This does not drop the default system message from chat_template if it exists. If\n  # you wish to, we recommend using a custom jinja template with the default system\n  # message removed or adding a system turn with empty content.\n  drop_system_message: bool | None\n  # Trust remote code for untrusted source\n  trust_remote_code: bool | None = False\n  # The specific revision of the dataset to use when loading from the Hugging Face Hub.\n  # This can be a commit hash, tag, or branch name. If not specified, the latest version\n  # will be used. This parameter is ignored for local datasets.\n  revision: str | None\n\n  # For DPODataset:\n  path: str | None\n  split: str | None\n  type: UserDefinedDPOType | str | None\n    # For UserDefinedDPOType:\n    field_system: str | None\n    field_prompt: str | None\n    field_chosen: str | None\n    field_rejected: str | None\n    prompt_format: str | None\n    chosen_format: str | None\n    rejected_format: str | None\n  data_files: list[str] | None\n  revision: str | None\n  field_messages: str | None\n\n  # For KTODataset:\n  path: str | None\n  split: str | None\n  type: UserDefinedKTOType | str | None\n    # For UserDefinedKTOType:\n    field_system: str | None\n    field_prompt: str | None\n    field_completion: str | None\n    field_label: bool | None\n    prompt_format: str | None\n    completion_format: str | None\n  data_files: list[str] | None\n  trust_remote_code: bool | None = False\n  revision: str | None\n\n  # For StepwiseSupervisedDataset:\n  path: str | None\n  split: str | None\n  data_files: list[str] | None\n  revision: str | None\n  step_separator: str | None\n  max_completion_length: int | None\n  train_on_last_step_only: bool | None\n\n# If false, the datasets will not be shuffled and will keep their original order in\n# `datasets`. The same applies to the `test_datasets` option and the\n# `pretraining_dataset` option. Default is true.\nshuffle_merged_datasets: bool | None = True\n# If true, each dataset in `datasets` will be shuffled before merging. This allows\n# curriculum learning strategies to be applied at the dataset level. Default is false.\nshuffle_before_merging_datasets: bool | None = False\n# Axolotl attempts to save the dataset as an arrow after packing the data together so\n# subsequent training attempts load faster, relative path\ndataset_prepared_path: str | None\n# Num shards for whole dataset\ndataset_shard_num: int | None\n# Index of shard to use for whole dataset\ndataset_shard_idx: int | None\nskip_prepare_dataset: bool | None = False\n# Number of shards to save the prepared dataset\nnum_dataset_shards_to_save: int | None\n\n# Set to HF dataset for type: 'completion' for streaming instead of pre-tokenize\npretraining_dataset: Annotated[list[PretrainingDataset | SFTDataset], MinLen(1)] | None\n  # For PretrainingDataset:\n  name: str | None\n  path: str | None\n  split: str | None = train\n  text_column: str | None = text\n  type: str | None = pretrain\n  trust_remote_code: bool | None = False\n  data_files: str | None\n  skip: int | None\n\n  # For SFTDataset:\n  # HuggingFace dataset repo | s3:// | gs:// | path to local file or directory\n  path: str | None\n  # name of dataset split to load from\n  split: str | None\n  # The type of prompt to use for training. [alpaca, gpteacher, oasst, reflection]\n  type: str | UserDefinedPrompterType | None\n    # For UserDefinedPrompterType:\n    # Custom user instruction prompt\n    system_prompt: str | None\n    # Use {system} as key to be replaced\n    system_format: str | None\n    field_system: str | None\n    field_instruction: str | None\n    field_input: str | None\n    field_output: str | None\n\n    # Customizable to be single line or multi-line. Use {instruction}/{input} as key to\n    # be replaced. 'format' can include {input}\n    format: str | None\n    # 'no_input_format' cannot include {input}\n    no_input_format: str | None\n  input_transform: str | None\n  # split dataset into N pieces (use with shards_idx)\n  shards: int | None\n  # the index of sharded dataset to use\n  shards_idx: int | None\n  # process dataset in N sequential chunks for memory efficiency (exclusive with\n  # `shards`)\n  preprocess_shards: int | None\n  conversation: str | None\n\n  # The name of the chat template to use for training, following values are supported:\n  # tokenizer_default: Uses the chat template that is available in the\n  # tokenizer_config.json. If the chat template is not available in the tokenizer, it\n  # will raise an error. This is the default.\n  # alpaca/inst/chatml/gemma/cohere/llama3/phi_3/deepseek_v2/jamba: These chat templates\n  # are available in the axolotl codebase at src/axolotl/utils/chat_templates.py.\n  # tokenizer_default_fallback_*: where * is the name of the chat template to fallback\n  # to if the tokenizer does not have a chat template else default to tokenizer. E.g.\n  # tokenizer_default_fallback_chatml. jinja: Uses a custom jinja template for the chat\n  # template. The custom jinja template should be provided in the chat_template_jinja\n  # field.\n  chat_template: ChatTemplate | str | None\n  # Custom jinja chat template or path to jinja file. Used only if `chat_template:\n  # jinja` or empty.\n  chat_template_jinja: str | None\n  # path to source data files\n  data_files: str | list[str] | None\n  input_format: str | None\n  # name of dataset configuration to load\n  name: str | None\n  # defines the datatype when path is a file\n  ds_type: str | None\n  # For `completion` datasets only, uses the provided field instead of `text` column\n  field: str | None\n  field_human: str | None\n  field_model: str | None\n  # Key containing the messages (default: \"messages\")\n  field_messages: str | None\n  # Key containing the tools (default: \"tools\"). Must be a list[dict] and follow [JSON\n  # schema](https://json-schema.org/learn/getting-started-step-by-step).\n  field_tools: str | None\n  # Key containing the reasoning trace (default: \"reasoning_content\").\n  field_thinking: str | None\n  # The key the chat template expects that indicates the reasoning trace.\n  template_thinking_key: str | None\n\n  message_field_role: str | None\n\n  message_field_content: str | None\n  # Mapping of properties from the input dataset to the chat template. (default:\n  # message_property_mappings={'role':'role', 'content':'content'}) If a property exists\n  # in the template but not in this mapping, the system will attempt to load it directly\n  # from the message using the property name as the key. Example: In the mapping below,\n  # 'from' is loaded from input dataset and used as 'role', while 'value' is loaded and\n  # used as 'content' in the chat template.\n  message_property_mappings: dict[str, str] | None\n  # The key in the message turn that indicates via boolean whether tokens of a turn\n  # should be considered for training. Useful to selectively train on certain turns\n  # besides the `roles_to_train`.\n  message_field_training: str | None\n  # The key in the message turn that contains the training details. Useful to\n  # selectively train on certain tokens in a turn. The value of the key is a List[Dict]\n  # containing `begin_offset` (start character index in content), `end_offset` (end\n  # character index in content), and `train` (boolean whether to train).\n  message_field_training_detail: str | None\n  # (for Qwen3 template only) Whether to split the assistant content based on a\n  # reasoning trace inside delimited tags\n  split_thinking: bool | None\n  logprobs_field: str | None\n  temperature: float | None\n  # Roles to train on. The tokens from these roles will be considered for the loss.\n  roles_to_train: list[str] | None\n  # Which EOS tokens to train on in the conversation. Possible values are: all: train on\n  # all EOS tokens, turn (default): train on the EOS token at the end of each trainable\n  # turn, last: train on the last EOS token in the conversation\n  train_on_eos: Literal['all', 'turn', 'last'] | None\n  # Roles mapping in the messages. The format is {target_role: [source_roles]}. All\n  # source roles will be mapped to the target role. The default is: user: [\"human\",\n  # \"user\"], assistant: [\"gpt\", \"assistant\"], system: [\"system\"], tool: [\"tool\"]\n  roles: dict[str, list[str]] | None\n  # Whether to drop the system turn from the dataset. Only works with chat_template.\n  # This does not drop the default system message from chat_template if it exists. If\n  # you wish to, we recommend using a custom jinja template with the default system\n  # message removed or adding a system turn with empty content.\n  drop_system_message: bool | None\n  # Trust remote code for untrusted source\n  trust_remote_code: bool | None = False\n  # The specific revision of the dataset to use when loading from the Hugging Face Hub.\n  # This can be a commit hash, tag, or branch name. If not specified, the latest version\n  # will be used. This parameter is ignored for local datasets.\n  revision: str | None\n\n# The maximum number of processes to use while preprocessing your input dataset. This\n# defaults to `os.cpu_count()` if not set. For Runpod VMs, it will default to number of\n# vCPUs via RUNPOD_CPU_COUNT.\ndataset_processes: int | None\n# The maximum number of processes to use while preprocessing your input dataset. This\n# defaults to `os.cpu_count()` if not set. For Runpod VMs, it will default to number of\n# vCPUs via RUNPOD_CPU_COUNT.\ndataset_num_proc: int | None\n\n# Deduplicates datasets and test_datasets with identical entries\ndataset_exact_deduplication: bool | None\n# Keep dataset in memory while preprocessing. Only needed if cached dataset is taking\n# too much storage\ndataset_keep_in_memory: bool | None\ndataloader_pin_memory: bool | None\ndataloader_num_workers: int | None\ndataloader_prefetch_factor: int | None\ndataloader_drop_last: bool | None\n\naccelerator_config: dict[str, Any] | None\n\nremove_unused_columns: bool | None\n\n# Push prepared dataset to hub - repo_org/repo_name\npush_dataset_to_hub: str | None\n# Whether to use hf `use_auth_token` for loading datasets. Useful for fetching private\n# datasets. Required to be true when used in combination with `push_dataset_to_hub`\nhf_use_auth_token: bool | None\n\ndevice: Any | None\n# Passed through to transformers when loading the model when launched without\n# accelerate. Use `sequential` when training w/ model parallelism to limit memory\ndevice_map: Any | None\nworld_size: int | None\n# Don't mess with this, it's here for accelerate and torchrun\nlocal_rank: int | None\nddp: bool | None\n\n# Seed for reproducibility\nseed: int | None\n# Advanced DDP Arguments - timeout\nddp_timeout: int | None\n# Advanced DDP Arguments - bucket cap in MB\nddp_bucket_cap_mb: int | None\n# Advanced DDP Arguments - broadcast buffers\nddp_broadcast_buffers: bool | None\nddp_find_unused_parameters: bool | None\n\n# Whether to run causal language model evaluation for metrics in\n# `eval_causal_lm_metrics`\ndo_causal_lm_eval: bool | None\n# HF evaluate metrics used during evaluation. Default is ['sacrebleu', 'comet', 'ter',\n# 'chrf', 'perplexity']\neval_causal_lm_metrics: list[str] | None\ndo_bench_eval: bool | None\nbench_dataset: str | None\nbench_split: str | None\nmetric_for_best_model: str | None\ngreater_is_better: bool | None\n\n# High loss value, indicating the learning has broken down (a good estimate is ~2 times\n# the loss at the start of training)\nloss_watchdog_threshold: float | None\n# Number of high-loss steps in a row before the trainer aborts (default: 3)\nloss_watchdog_patience: int | None\n\n# Run garbage collection every `gc_steps` steps. -1 will run on epoch end and before\n# evaluations. Default is 0 (disabled).\ngc_steps: int | None\n\n# Use CUDA bf16. bool or 'full' for `bf16_full_eval`, or 'auto' for automatic detection.\n# require &gt;=ampere\nbf16: Literal['auto'] | bool | None = auto\n# Use CUDA fp16\nfp16: bool | None\n# Enable FP8 mixed precision training using TorchAO. Best used in combination with\n# torch.compile.\nfp8: bool | None\n# Enable FSDP float8 all-gather optimization for FP8 training. Can improve training\n# speed by 10-15% when FSDP is enabled.\nfp8_enable_fsdp_float8_all_gather: bool | None\n# No AMP (automatic mixed precision) - require &gt;=ampere\nbfloat16: bool | None\n# No AMP (automatic mixed precision)\nfloat16: bool | None\n# Use CUDA tf32 - require &gt;=ampere\ntf32: bool | None\nfloat32: bool | None\n\n# Whether to use gradient checkpointing. Available options are: true, false, 'offload',\n# 'offload_disk'.\n# https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing\ngradient_checkpointing: Literal['offload', 'offload_disk'] | bool | None = False\n# Additional kwargs to pass to the trainer for gradient checkpointing\ngradient_checkpointing_kwargs: dict[str, Any] | None\n# Whether to offload activations. Available options are: true, false, 'legacy', 'disk'.\nactivation_offloading: Literal['legacy', 'disk'] | bool | None = False\n\n# List of regex patterns for parameter names to keep unfrozen. All other parameters will\n# be frozen via requires_grad=False. Note: range-based patterns (e.g.\n# embed_tokens.weight$[:32000]) use gradient zeroing rather than a true freeze, so\n# weight decay will still apply to the frozen portion and optimizer states are allocated\n# for the full parameter.\nunfrozen_parameters: list[str] | None\n\n# The maximum length of an input to train with, this should typically be less than 2048\n# as most models have a token/context limit of 2048\nsequence_len: int = 512\n# What to do when a tokenized row exceeds sequence_len. 'drop' removes the row;\n# 'truncate' slices tensors to sequence_len; 'raise' raises a ValueError. Defaults to\n# 'drop' for backward compatibility.\nexcess_length_strategy: Literal['drop', 'truncate', 'raise'] | None\n# The maximum length of an input for evaluation. If not specified, defaults to\n# sequence_len\neval_sequence_len: int | None\nmin_sample_len: int | None\n# maximum prompt length for RL training\nmax_prompt_len: int | None\n# Use efficient multi-packing with block diagonal attention and per sequence\n# position_ids. Recommend set to 'true'\nsample_packing: bool | None\n# The number of samples packed at a time. Increasing the following values helps with\n# packing, but usually only slightly (&lt;%1.)\nsample_packing_group_size: int | None = 100000\n# The number of samples which can be packed into one sequence. Increase if using a large\n# sequence_len with many short samples.\nsample_packing_bin_size: int | None = 200\n# Whether to pack samples sequentially\nsample_packing_sequentially: bool | None\n# The multiprocessing start method to use for packing. Should be 'fork', 'spawn' or\n# 'forkserver'\nsample_packing_mp_start_method: str | None\n# Set to 'false' if getting errors during eval with sample_packing on\neval_sample_packing: bool | None\n# Pad inputs so each step uses constant sized buffers. This will reduce memory\n# fragmentation and may prevent OOMs, by re-using memory more efficiently. Defaults to\n# True if `sample_packing` enabled\npad_to_sequence_len: bool | None\n# Whether to use sequential sampling for curriculum learning\ncurriculum_sampling: bool | None\nmultipack_real_batches: bool | None\n\n# Use batch flattening for speedups when not using sample_packing\nbatch_flattening: Literal['auto'] | bool | None\n\nuse_pose: bool | None\npose_split_on_token_ids: list[int] | None\npose_max_context_len: int | None\npose_num_chunks: int | None\n\npretrain_multipack_buffer_size: int | None\n# whether to prevent cross attention for packed sequences during pretraining\npretrain_multipack_attn: bool | None = True\n# whether to concatenate samples during pretraining\npretraining_sample_concatenation: bool | None\n\n# Use streaming mode for loading datasets\nstreaming: bool | None\n# Buffer size for multipack streaming datasets\nstreaming_multipack_buffer_size: int | None = 10000\n\n# Whether to use xformers attention patch https://github.com/facebookresearch/xformers\nxformers_attention: bool | None\n# Whether to use scaled-dot-product attention https://pytorch.org/docs/stable/generated/\n# torch.nn.functional.scaled_dot_product_attention.html\nsdp_attention: bool | None\n# Shifted-sparse attention (only llama) - https://arxiv.org/pdf/2309.12307.pdf\ns2_attention: bool | None\nflex_attention: bool | None\nflex_attn_compile_kwargs: dict[str, Any] | None\n# Whether to use flash attention patch https://github.com/Dao-AILab/flash-attention\nflash_attention: bool | None\n# Whether to use flash-attention cross entropy implementation - advanced use only\nflash_attn_cross_entropy: bool | None\n# Whether to use flash-attention rms norm implementation - advanced use only\nflash_attn_rms_norm: bool | None\n# Whether to fuse part of the MLP into a single operation\nflash_attn_fuse_mlp: bool | None\n# Whether to use bettertransformers\nflash_optimum: bool | None\n# Whether to use SageAttention https://github.com/thu-ml/SageAttention\nsage_attention: bool | None\n\neager_attention: bool | None\n\n# Specify a custom attention implementation, used mostly for kernels.\nattn_implementation: str | None\n\n# Which experts implementation to use for MoE models,\nexperts_implementation: str | None\n\n# Quantize MoE expert weights on load to reduce VRAM. Requires adapter (lora/qlora) with\n# load_in_4bit or load_in_8bit. Requires CUDA (not compatible with ROCm or other\n# backends). Note: total parameter count may be reported incorrectly when enabled\n# (trainable param count is correct).\nquantize_moe_experts: bool = False\n\n# Whether to use Scaled Softmax (SSMax) attention. Ref: https://arxiv.org/abs/2501.19399\nscaling_softmax: bool | None\n# Scaling factor for SSMax attention. Default is 0.43\nscaling_softmax_factor: float | None\n# Bias for SSMax attention. Default is 0.0. Note: The paper recommends bias=0 for better\n# length generalization.\nscaling_softmax_bias: float | None\n\nunsloth_cross_entropy_loss: bool | None\nunsloth_lora_mlp: bool | None\nunsloth_lora_qkv: bool | None\nunsloth_lora_o: bool | None\nunsloth_rms_norm: bool | None\nunsloth_rope: bool | None\n\n# Apply custom LoRA autograd functions and activation function Triton kernels for speed\n# and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html\nlora_mlp_kernel: bool | None\n# Apply custom LoRA autograd functions and activation function Triton kernels for speed\n# and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html\nlora_qkv_kernel: bool | None\n# Apply custom LoRA autograd functions and activation function Triton kernels for speed\n# and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html\nlora_o_kernel: bool | None\n\n# Whether to use chunked cross entropy loss for memory efficiency\nchunked_cross_entropy: bool | None\n# Number of chunks to use for chunked cross entropy loss\nchunked_cross_entropy_num_chunks: int | None\n# Enable Entropy-Aware Focal Training loss (EAFT)\nuse_eaft: bool | None\n# Exponent for entropy weighting in EAFT (default: 1.0)\neaft_alpha: float | None = 1.0\n# Number of top logits for entropy approximation (default: 20)\neaft_k: int | None = 20\n\n# Whether to use ALST tiled mlp for memory efficient long context\ntiled_mlp: bool | None\n\n# Number of shards to use for ALST tiled mlp. If unset, it will be set based on\n# seqlen/hidden_size\ntiled_mlp_num_shards: int | None\n\n# Whether to use original mlp for ALST tiled mlp. Otherwise uses a generic MLP based on\n# llama.\ntiled_mlp_use_original_mlp: bool | None = True\n\nllama4_linearized_experts: bool | None\n\n# Deepspeed config path. e.g., deepspeed_configs/zero3.json\ndeepspeed: str | dict[str, Any] | None\n# Whether to use deepcompile for faster training with deepspeed\ndeepcompile: bool | None\n# FSDP configuration\nfsdp: list[str] | None\n\n# FSDP configuration options\nfsdp_config: FSDPConfig | None\n  # For FSDPConfig:\n  # FSDP version\n  fsdp_version: int | None\n  # Enable activation checkpointing to reduce memory usage during forward passes\n  activation_checkpointing: bool | None\n  # Offload parameters to CPU to reduce GPU memory usage\n  offload_params: bool | None\n  # Synchronize module states across all processes\n  sync_module_states: bool | None\n  # Enable CPU RAM efficient loading to reduce memory usage during model loading\n  cpu_ram_efficient_loading: bool | None\n  # Disabling this enables swap memory usage for resource-constrained setups when\n  # offload_params is enabled.\n  cpu_offload_pin_memory: bool | None\n  # Use original parameters instead of flattened parameters\n  use_orig_params: bool | None\n\n  # Type of state dict to use for saving/loading checkpoints\n  state_dict_type: Literal['FULL_STATE_DICT', 'LOCAL_STATE_DICT', 'SHARDED_STATE_DICT'] | None\n  # Final state dict type to use after training completion\n  final_state_dict_type: Literal['FULL_STATE_DICT', 'LOCAL_STATE_DICT', 'SHARDED_STATE_DICT'] | None\n\n  # Policy for automatically wrapping modules with FSDP\n  auto_wrap_policy: Literal['TRANSFORMER_BASED_WRAP', 'SIZE_BASED_WRAP'] | None\n  # Class name of transformer layers to wrap (e.g., 'LlamaDecoderLayer')\n  transformer_layer_cls_to_wrap: str | None\n\n  # Reshard parameters after forward pass to save memory\n  reshard_after_forward: bool | None\n  # Mixed precision policy for FSDP (e.g., 'fp16', 'bf16')\n  mixed_precision_policy: str | None\n\n# FSDP version\nfsdp_version: int | None\nfsdp_final_state_dict_type: Literal['FULL_STATE_DICT', 'LOCAL_STATE_DICT', 'SHARDED_STATE_DICT'] | None\n\n# How much of the dataset to set aside as evaluation. 1 = 100%, 0.50 = 50%, etc. 0 for\n# no eval.\nval_set_size: float | None = 0.0\n\n# Number of devices to shard across. If not set, will use all available devices.\ndp_shard_size: int | None\n# Number of devices to replicate across.\ndp_replicate_size: int | None\n# Deprecated: use `context_parallel_size` instead\nsequence_parallel_degree: int | None\n# Set to a divisor of the number of GPUs available to split sequences into chunks of\n# equal size. Use in long context training to prevent OOM when sequences cannot fit into\n# a single GPU's VRAM. E.g., if 4 GPUs are available, set this value to 2 to split each\n# sequence into two equal-sized subsequences, or set to 4 to split into four equal-sized\n# subsequences. See https://docs.axolotl.ai/docs/sequence_parallelism.html for more\n# details.\ncontext_parallel_size: int | None\n# Optional; strides across the key dimension. Larger values use more memory but should\n# make training faster. Must evenly divide the number of KV heads in your model.\nheads_k_stride: int | None\n# One of 'varlen_llama3', 'batch_ring', 'batch_zigzag', 'batch_stripe'. Defaults to\n# 'varlen_llama3' in the sample packing case, and 'batch_ring' in the non-sample packing\n# case.\nring_attn_func: RingAttnFunc | None\n# Number of tensor parallel processes in TP group. Only supported with DeepSpeed AutoTP.\ntensor_parallel_size: int | None\n\n# Add or change special tokens. If you add tokens here, you don't need to add them to\n# the `tokens` list.\nspecial_tokens: SpecialTokensConfig | None\n  # For SpecialTokensConfig:\n  bos_token: str | None\n  eos_token: str | None\n  pad_token: str | None\n  unk_token: str | None\n  additional_special_tokens: list[str] | None\n\n# Add extra tokens to the tokenizer\ntokens: list[str] | None\n# Mapping token_id to new_token_string to override reserved added_tokens in the\n# tokenizer. Only works for tokens that are not part of the base vocab (aka are\n# added_tokens). Can be checked if they exist in tokenizer.json added_tokens.\nadded_tokens_overrides: dict[int, str] | None\n\n# Whether to use torch.compile and which backend to use. setting to `auto` will enable\n# torch compile when torch&gt;=2.6.0\ntorch_compile: Literal['auto'] | bool | None\n# Backend to use for torch.compile\ntorch_compile_backend: str | None\ntorch_compile_mode: Literal['default', 'reduce-overhead', 'max-autotune'] | None\n\n# Maximum number of iterations to train for. It precedes num_epochs which means that if\n# both are set, num_epochs will not be guaranteed. e.g., when 1 epoch is 1000 steps =&gt;\n# `num_epochs: 2` and `max_steps: 100` will train for 100 steps\nmax_steps: int | None\n# Number of warmup steps. Cannot use with warmup_ratio\nwarmup_steps: int | None\n# Warmup ratio. Cannot use with warmup_steps\nwarmup_ratio: float | None\n# Leave empty to eval at each epoch, integer for every N steps. float for fraction of\n# total steps\neval_steps: int | float | None\n# Number of times per epoch to run evals, mutually exclusive with eval_steps\nevals_per_epoch: int | None\n# Set to `no` to skip evaluation, `epoch` at end of each epoch, leave empty to infer\n# from `eval_steps`\neval_strategy: str | None\n\n# Leave empty to save at each epoch, integer for every N steps. float for fraction of\n# total steps\nsave_steps: int | float | None\n# Number of times per epoch to save a checkpoint, mutually exclusive with save_steps\nsaves_per_epoch: int | None\n# Set to `no` to skip checkpoint saves, `epoch` at end of each epoch, `best` when better\n# result is achieved, leave empty to infer from `save_steps`\nsave_strategy: str | None\n# Checkpoints saved at a time\nsave_total_limit: int | None\n# Whether to checkpoint a model after the first step of training. Defaults to False.\nsave_first_step: bool | None\n\n# Logging frequency\nlogging_steps: int | None\n# Stop training after this many evaluation losses have increased in a row. https://huggi\n# ngface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppin\n# gCallback\nearly_stopping_patience: int | None\nload_best_model_at_end: bool | None = False\n# Save only the model weights, skipping the optimizer. Using this means you can't resume\n# from checkpoints.\nsave_only_model: bool | None = False\n# Use tensorboard for logging\nuse_tensorboard: bool | None\n# Enable the pytorch profiler to capture the first N steps of training to the\n# output_dir. see https://pytorch.org/blog/understanding-gpu-memory-1/ for more\n# information. Snapshots can be visualized @ https://pytorch.org/memory_viz\nprofiler_steps: int | None\n# Which step to start the profiler at. Useful for only capturing a few steps mid-run.\nprofiler_steps_start: int | None = 0\n# bool of whether to report tokens per second at the end of training. This is not\n# supported with pre-training datasets.\ninclude_tokens_per_second: bool | None\n# bool of whether to report tokens per second per-gpu during training by measuring\n# throughput of non-padding tokens.\ninclude_tkps: bool | None = True\n# NEFT https://arxiv.org/abs/2310.05914, set this to a number (paper default is 5) to\n# add noise to embeddings. Currently only supported on Llama and Mistral\nneftune_noise_alpha: float | None\n\n# Parameter controlling the relative ratio loss weight in the ORPO loss. Passed to\n# `beta` in `ORPOConfig` due to trl mapping.\norpo_alpha: float | None\n# Weighting of NLL term in loss from RPO paper\nrpo_alpha: float | None\n# Target reward margin for the SimPO loss\nsimpo_gamma: float | None\n# Weight of the BC regularizer\ncpo_alpha: float | None\n\n# Factor for desirable loss term in KTO loss\nkto_desirable_weight: float | None\n# Factor for undesirable loss term in KTO loss\nkto_undesirable_weight: float | None\n# The beta parameter for the RL training\nrl_beta: float | None\n\n# Defines the max memory usage per gpu on the system. Passed through to transformers\n# when loading the model.\nmax_memory: dict[int | Literal['cpu', 'disk'], int | str] | None\n# Limit the memory for all available GPUs to this amount (if an integer, expressed in\n# gigabytes); default: unset\ngpu_memory_limit: int | str | None\n# Whether to use low_cpu_mem_usage\nlow_cpu_mem_usage: bool | None\n\n# The name of the chat template to use for training, following values are supported:\n# tokenizer_default: Uses the chat template that is available in the\n# tokenizer_config.json. If the chat template is not available in the tokenizer, it will\n# raise an error. This is the default value.\n# alpaca/inst/chatml/gemma/cohere/llama3/phi_3/deepseek_v2/jamba: These chat templates\n# are available in the axolotl codebase at src/axolotl/utils/chat_templates.py.\n# tokenizer_default_fallback_*: where * is the name of the chat template to fallback to.\n# E.g. tokenizer_default_fallback_chatml. This is useful when the chat template is not\n# available in the tokenizer. jinja: Uses a custom jinja template for the chat template.\n# The custom jinja template should be provided in the chat_template_jinja field. The\n# selected chat template will be saved to the tokenizer_config.json for easier\n# inferencing\nchat_template: ChatTemplate | Annotated[str, StringConstraints(pattern='^tokenizer_default_fallback_')] | None\n# Custom jinja template or path to jinja file for chat template. This will be only used\n# if chat_template is set to `jinja` or `null` (in which case chat_template is\n# automatically set to `jinja`). Default is null.\nchat_template_jinja: str | None\n# Additional kwargs to pass to the chat template. This is useful for customizing the\n# chat template. For example, you can pass `thinking=False` to add a generation prompt\n# to the chat template.\nchat_template_kwargs: dict[str, Any] | None\n# Custom EOT (End-of-Turn) tokens to mask/unmask during training. These tokens mark the\n# boundaries between conversation turns. For example: ['/INST', '&lt;/s&gt;',\n# '[/SYSTEM_PROMPT]']. If not specified, defaults to just the model's eos_token. This is\n# useful for templates that use multiple delimiter tokens.\neot_tokens: list[str] | None\n# Changes the default system message. Currently only supports chatml.\ndefault_system_message: str | None\n\n# Token index or indices to adjust embedding weights to the mean of the other tokens.\n# This is useful when the model has untrained embeddings.\nfix_untrained_tokens: int | list[int] | None\n\nis_preprocess: bool | None\npreprocess_iterable: bool | None\n\n# Total number of tokens - internal use\ntotal_num_tokens: int | None\ntotal_supervised_tokens: int | None\n# You can set these packing optimizations AFTER starting a training at least once. The\n# trainer will provide recommended values for these values.\nsample_packing_eff_est: float | None\naxolotl_config_path: str | None\n\n# Internal use only - Used to identify which the model is based on\nis_falcon_derived_model: bool | None\n# Internal use only - Used to identify which the model is based on\nis_llama_derived_model: bool | None\n# Internal use only - Used to identify which the model is based on. Please note that if\n# you set this to true, `padding_side` will be set to 'left' by default\nis_mistral_derived_model: bool | None\n# Internal use only - Used to identify which the model is based on\nis_qwen_derived_model: bool | None\n\n# Add plugins to extend the pipeline. See `src/axolotl/integrations` for the available\n# plugins or doc below for more details.\n# https://docs.axolotl.ai/docs/custom_integrations.html\nplugins: list[str] | None\n# Enable sample generation during training for monitoring\ngenerate_samples: bool | None = False\n# Number of samples to generate at each interval\nnum_generation_samples: int | None = 3\n# Maximum new tokens to generate per sample\ngeneration_max_new_tokens: int | None = 50\n# Temperature for sample generation (0.0 = greedy)\ngeneration_temperature: float | None = 0.7\n# Nucleus sampling parameter for generation\ngeneration_top_p: float | None\n# Top-k sampling parameter for generation\ngeneration_top_k: int | None\n# Ratio of input to use as prompt (0.0-1.0)\ngeneration_prompt_ratio: float | None = 0.5\n# Whether to use sampling (vs greedy decoding)\ngeneration_do_sample: bool | None = True\n\n# This is the huggingface model that contains *.pt, *.safetensors, or *.bin files. This\n# can also be a relative path to a model on disk\nbase_model: str (required)\n# If the base_model repo on hf hub doesn't include configuration .json files, You can\n# set that here, or leave this empty to default to base_model\nbase_model_config: str | None\n# transformers config class (e.g., 'LlamaConfig', 'MistralConfig'). Defaults to\n# AutoConfig.\ncls_model_config: str | None\n# Optional tokenizer configuration path in case you want to use a different tokenizer\n# than the one defined in the base model\ntokenizer_config: str | None\n# use_fast option for tokenizer loading from_pretrained, default to True\ntokenizer_use_fast: bool | None\n# Whether to use the legacy tokenizer setting, defaults to True\ntokenizer_legacy: bool | None\n# Whether to use mistral-common tokenizer. If set to True, it will use the mistral-\n# common tokenizer.\ntokenizer_use_mistral_common: bool | None\n# Corresponding tokenizer for the model AutoTokenizer is a good choice\ntokenizer_type: str | None\n# transformers processor class\nprocessor_type: str | None\n# Whether to save jinja files for tokenizer, transformers default is True\ntokenizer_save_jinja_files: bool | None = True\n# Trust remote code for untrusted source\ntrust_remote_code: bool | None\n\n# Don't move the model to the device before sharding. Set to `false` to revert to legacy\n# behavior.\nexperimental_skip_move_to_device: bool | None = True\n\n# Use custom kernels, e.g. MegaBlocks.\nuse_kernels: bool | None\n\n# Model loading quantization config\nmodel_quantization_config: Literal['Mxfp4Config'] | None\n# kwargs for model quantization config\nmodel_quantization_config_kwargs: dict[str, Any] | None\n\n# Where to save the full-finetuned model to\noutput_dir: str = ./model-out\n# push checkpoints to hub\nhub_model_id: str | None\n# how to push checkpoints to hub\nhub_strategy: str | None\n# branch/revision to push to on hub (default: main)\nhub_revision: str | None\n# Whether to save the model using safetensors format. Defaults to True.\nsave_safetensors: bool | None = True\n\n# This will attempt to quantize the model down to 8 bits and use adam 8 bit optimizer\nload_in_8bit: bool | None = False\n# Use bitsandbytes 4 bit\nload_in_4bit: bool | None = False\n\n# If you want to use 'lora', 'qlora', or 'llama-adapter', or leave blank to train all\n# parameters in original model\nadapter: Literal['lora', 'qlora', 'llama-adapter'] | None\n# If you already have a lora model trained that you want to load, put that here. This\n# means after training, if you want to test the model, you should set this to the value\n# of `output_dir`. Note that if you merge an adapter to the base model, a new\n# subdirectory `merged` will be created under the `output_dir`.\nlora_model_dir: str | None\nlora_r: int | None\nlora_alpha: int | None\nlora_fan_in_fan_out: bool | None\nlora_target_modules: str | list[str] | None\nlora_target_parameters: str | list[str] | None\n# If true, will target all linear modules\nlora_target_linear: bool | None\n# If you added new tokens to the tokenizer, you may need to save some LoRA modules\n# because they need to know the new tokens. For LLaMA and Mistral, you need to save\n# `embed_tokens` and `lm_head`. It may vary for other models. `embed_tokens` converts\n# tokens to embeddings, and `lm_head` converts embeddings to token probabilities.\nlora_modules_to_save: list[str] | None\nlora_dropout: float | None = 0.0\n# The layer indices to transform, otherwise, apply to all layers\npeft_layers_to_transform: list[int] | None\npeft_layers_pattern: list[str] | None\n\npeft: PeftConfig | None\n  # For PeftConfig:\n  # Configuration options for loftq initialization for LoRA\n  loftq_config: LoftQConfig | None\n    # For LoftQConfig:\n    # typically 4 bits\n    loftq_bits: int = 4\n\n# Whether to use DoRA.\npeft_use_dora: bool | None\n# Whether to use RSLoRA.\npeft_use_rslora: bool | None\n# List of layer indices to replicate.\npeft_layer_replication: list[tuple[int, int]] | None\n# How to initialize LoRA weights. Default to True which is MS original implementation.\npeft_init_lora_weights: bool | str | None\n# A list of token indices to fine-tune on the `embed_tokens` layer. Otherwise, a dict\n# mapping an embedding layer name to its trainable token indices. See\n# https://huggingface.co/docs/peft/v0.17.0/en/developer_guides/lora#efficiently-train-\n# tokens-alongside-lora\npeft_trainable_token_indices: list[int] | dict[str, list[int]] | None\n# Whether to tie adapter weights for tied model weights. See\n# https://github.com/huggingface/peft/issues/2864\npeft_ensure_weight_tying: bool | None\n# Whether to upcast the LoRA adapter to fp32. This is enabled by default in PEFT.\npeft_autocast_adapter_dtype: bool | None\n\n# load qlora model in sharded format for FSDP using answer.ai technique.\nqlora_sharded_model_loading: bool | None = False\n# Do the LoRA/PEFT loading on CPU -- this is required if the base model is so large it\n# takes up most or all of the available GPU VRAM, e.g. during a model and LoRA merge\nlora_on_cpu: bool | None\n# Whether you are training a 4-bit GPTQ quantized model\ngptq: bool | None\n# optional overrides to the bnb 4bit quantization configuration\nbnb_config_kwargs: dict[str, Any] | None\n\n# loraplus learning rate ratio lr_B / lr_A. Recommended value is 2^4.\nloraplus_lr_ratio: float | None\n# loraplus learning rate for lora embedding layers. Default value is 1e-6.\nloraplus_lr_embedding: float | None = 1e-06\n\nmerge_lora: bool | None\n\n# Whether to use ReLoRA. Use with jagged_restart_*steps options.\nrelora: bool | None\n# threshold for optimizer magnitude when pruning\nrelora_prune_ratio: float | None\n# True to perform lora weight merges on cpu during restarts, for modest gpu memory\n# savings\nrelora_cpu_offload: bool | None\n\n# how often to reset for jagged restarts\njagged_restart_steps: int | None\n# how many warmup steps to take after reset for jagged restarts\njagged_restart_warmup_steps: int | None\n# how many anneal steps to take before reset for jagged restarts\njagged_restart_anneal_steps: int | None\n\n# If greater than 1, backpropagation will be skipped and the gradients will be\n# accumulated for the given number of steps.\ngradient_accumulation_steps: int | None = 1\n# The number of samples to include in each batch. This is the number of samples sent to\n# each GPU. Batch size per gpu = micro_batch_size * gradient_accumulation_steps\nmicro_batch_size: int | None = 1\n# Total batch size, we do not recommended setting this manually\nbatch_size: int | None\n# per gpu micro batch size for evals, defaults to value of micro_batch_size\neval_batch_size: int | None\n\n# whether to find batch size that fits in memory. Passed to underlying transformers\n# Trainer\nauto_find_batch_size: bool | None\n\n# Whether to mask out or include the human's prompt from the training labels\ntrain_on_inputs: bool | None = False\n# Group similarly sized data to minimize padding. May be slower to start, as it must\n# download and sort the entire dataset. Note that training loss may have an oscillating\n# pattern with this enabled.\ngroup_by_length: bool | None\n\nlearning_rate: str | float (required)\nembedding_lr: float | None\nembedding_lr_scale: float | None\n# Specify weight decay\nweight_decay: float | None = 0.0\n# Specify optimizer\noptimizer: OptimizerNames | CustomSupportedOptimizers | None = OptimizerNames.ADAMW_TORCH_FUSED\n# Dictionary of arguments to pass to the optimizer\noptim_args: str | dict[str, Any] | None\n# The target modules to optimize, i.e. the module names that you would like to train,\n# right now this is used only for GaLore algorithm\noptim_target_modules: list[str] | Literal['all_linear'] | None\n# Path to torch distx for optim 'adamw_anyprecision'\ntorchdistx_path: str | None\nlr_scheduler: SchedulerType | Literal['one_cycle'] | Literal['rex'] | None = SchedulerType.COSINE\n# Specify a scheduler and kwargs to use with the optimizer\nlr_scheduler_kwargs: dict[str, Any] | None\nlr_quadratic_warmup: bool | None\n# decay lr to some percentage of the peak lr, e.g. cosine_min_lr_ratio=0.1 for 10% of\n# peak lr\ncosine_min_lr_ratio: float | None\n# freeze lr at some percentage of the step, e.g. cosine_constant_lr_ratio=0.8 means\n# start cosine_min_lr at 80% of training step\ncosine_constant_lr_ratio: float | None\n# Learning rate div factor\nlr_div_factor: float | None\n\nlr_groups: list[LrGroup] | None\n  # For LrGroup:\n  name: str (required)\n  modules: list[str] (required)\n  lr: float (required)\n\n# adamw hyperparams\nadam_epsilon: float | None\n# only used for CAME Optimizer\nadam_epsilon2: float | None\n# adamw hyperparams\nadam_beta1: float | None\n# adamw hyperparams\nadam_beta2: float | None\n# only used for CAME Optimizer\nadam_beta3: float | None\n\n# Dion Optimizer learning rate\ndion_lr: float | None\n# Dion Optimizer momentum\ndion_momentum: float | None\n# Dion Optimizer: r/d fraction for low-rank approximation. Used to compute the low-rank\n# dimension.\ndion_rank_fraction: float | None = 1.0\n# Dion Optimizer: Round up the low-rank dimension to a multiple of this number. This may\n# be useful to ensure even sharding.\ndion_rank_multiple_of: int | None = 1\n\n# Gradient clipping max norm\nmax_grad_norm: float | None\nnum_epochs: float = 1.0\n\nuse_wandb: bool | None\n# Set the name of your wandb run\nwandb_name: str | None\n# Set the ID of your wandb run\nwandb_run_id: str | None\n# \"offline\" to save run metadata locally and not sync to the server, \"disabled\" to turn\n# off wandb\nwandb_mode: str | None\n# Your wandb project name\nwandb_project: str | None\n# A wandb Team name if using a Team\nwandb_entity: str | None\nwandb_watch: str | None\n# \"checkpoint\" to log model to wandb Artifacts every `save_steps` or \"end\" to log only\n# at the end of training\nwandb_log_model: str | None\n\nuse_mlflow: bool | None\n# URI to mlflow\nmlflow_tracking_uri: str | None\n# Your experiment name\nmlflow_experiment_name: str | None\n# Your run name\nmlflow_run_name: str | None\n# set to true to copy each saved checkpoint on each save to mlflow artifact registry\nhf_mlflow_log_artifacts: bool | None\n\n# Enable or disable Comet integration.\nuse_comet: bool | None\n# API key for Comet. Recommended to set via `comet login`.\ncomet_api_key: str | None\n# Workspace name in Comet. Defaults to the user's default workspace.\ncomet_workspace: str | None\n# Project name in Comet. Defaults to Uncategorized.\ncomet_project_name: str | None\n# Identifier for the experiment. Used to append data to an existing experiment or\n# control the key of new experiments. Default to a random key.\ncomet_experiment_key: str | None\n# Create a new experiment (\"create\") or log to an existing one (\"get\"). Default\n# (\"get_or_create\") auto-selects based on configuration.\ncomet_mode: str | None\n# Set to True to log data to Comet server, or False for offline storage. Default is\n# True.\ncomet_online: bool | None\n# Dictionary for additional configuration settings, see the doc for more details.\ncomet_experiment_config: dict[str, Any] | None\n\nuse_trackio: bool | None\n# Your trackio project name\ntrackio_project_name: str | None\n# Set the name of your trackio run\ntrackio_run_name: str | None\n# Hugging Face Space ID to sync dashboard to (optional, runs locally if not provided)\ntrackio_space_id: str | None\n\n# Enable OpenTelemetry metrics collection and Prometheus export\nuse_otel_metrics: bool | None = False\n# Host to bind the OpenTelemetry metrics server to\notel_metrics_host: str | None = localhost\n# Port for the Prometheus metrics HTTP server\notel_metrics_port: int | None = 8000\n\n# the number of activate layers in LISA\nlisa_n_layers: int | None\n# how often to switch layers in LISA\nlisa_step_interval: int | None\n# path under the model to access the layers\nlisa_layers_attribute: str | None = model.layers\n\ngradio_title: str | None\ngradio_share: bool | None\ngradio_server_name: str | None\ngradio_server_port: int | None\ngradio_max_new_tokens: int | None\ngradio_temperature: float | None\n\nuse_ray: bool = False\nray_run_name: str | None\nray_num_workers: int = 1\nresources_per_worker: dict\n\n# The size of the image to resize to. It can be an integer (resized into padded-square\n# image) or a tuple (width, height).If not provided, we will attempt to load from\n# preprocessor.size, otherwise, images won't be resized.\nimage_size: int | tuple[int, int] | None\n# The resampling algorithm to use for image resizing. Default is bilinear. Please refer\n# to PIL.Image.Resampling for more details.\nimage_resize_algorithm: Literal['bilinear', 'bicubic', 'lanczos'] | Resampling | None\n\n# optional overrides to the base model configuration\noverrides_of_model_config: dict[str, Any] | None\n# optional overrides the base model loading from_pretrained\noverrides_of_model_kwargs: dict[str, Any] | None\n# If you want to specify the type of model to load, AutoModelForCausalLM is a good\n# choice too\ntype_of_model: str | None\n# You can specify to choose a specific model revision from huggingface hub\nrevision_of_model: str | None\n\nmax_packed_sequence_len: int | None\nrope_scaling: Any | None\nnoisy_embedding_alpha: float | None\ndpo_beta: float | None\nevaluation_strategy: str | None\neval_table_size: int | None\neval_max_new_tokens: int | None\ndpo_use_logits_to_keep: bool | None\ndpo_generate_during_eval: bool | None",
+    "text": "# Allow overwrite yml config using from cli\nstrict: bool | None = False\n# Resume from a specific checkpoint dir\nresume_from_checkpoint: str | None\n# If resume_from_checkpoint isn't set and you simply want it to start where it left off.\n# Be careful with this being turned on between different models.\nauto_resume_from_checkpoints: bool | None\n# Resize the model embeddings when new tokens are added to multiples of 32. This is\n# reported to improve training speed on some models\nresize_token_embeddings_to_32x: bool | None\nmean_resizing_embeddings: bool | None = False\n\n# Whether to shrink the embeddings to len(tokenizer). By default, we won't shrink.\nshrink_embeddings: bool | None\n# Don't upcast the embeddings to float32 when using PEFT. Useful for low-VRAM GPUs\nembeddings_skip_upcast: bool | None\n# Reinitialize model weights randomly instead of loading pretrained weights\nreinit_weights: bool | None\n\n# module to custom trainer class to use for training\ntrainer_cls: str | None\n\n# Use RL training: 'dpo', 'ipo', 'kto', 'simpo', 'orpo', 'grpo'\nrl: RLType | None\n\ntrl: TRLConfig | None\n  # For TRLConfig:\n  # Beta parameter for the RL training. Same as `rl_beta`. Use\n  beta: float | None\n  # Maximum length of the completion for RL training.\n  max_completion_length: int | None\n\n  # Whether to use VLLM for RL training.\n  use_vllm: bool = False\n  # VLLM mode to use, one of 'server' or 'colocate'\n  vllm_mode: Literal['server', 'colocate'] | None\n  # Host of the vLLM server to connect to.\n  vllm_server_host: str | None = 0.0.0.0\n  # Port of the vLLM server to connect to.\n  vllm_server_port: int | None = 8000\n  # Total timeout (in seconds) to wait for the vLLM server to respond.\n  vllm_server_timeout: int | None\n  # Regex for vLLM guided decoding.\n  vllm_guided_decoding_regex: str | None\n\n  # List of reward functions to load. Paths must be importable from current dir.\n  reward_funcs: list[str] | None\n  # List of reward weights for the reward functions.\n  reward_weights: list[float] | None\n  # Number of generations to sample.\n  num_generations: int | None\n  # Whether to log completions.\n  log_completions: bool | None = False\n  # Number of completions to print when log_completions is True.\n  num_completions_to_print: int | None\n  # Controls whether importance sampling ratios are computed at the `'token'` or\n  # `'sequence'` level. For GSPO, use `sequence`, default is None which corresponds to\n  # the original GRPO paper.\n  importance_sampling_level: Literal['sequence', 'token'] | None\n\n  # Whether to sync the reference model.\n  sync_ref_model: bool | None = False\n  # Mixup alpha for the reference model.\n  ref_model_mixup_alpha: float | None = 0.9\n  # Sync steps for the reference model.\n  ref_model_sync_steps: int | None = 64\n  # Whether to scale rewards by their standard deviation.\n  scale_rewards: bool = True\n\n  # Sampling temperature for the GRPO policy.\n  temperature: float | None\n  # Top-p sampling probability for the generation policy.\n  top_p: float | None\n  # Top-k sampling for the generation policy.\n  top_k: int | None\n  # Minimum probability for the generation policy.\n  min_p: float | None\n  # Penalty for tokens that appear in prompt and generated text.\n  repetition_penalty: float | None\n  # Number of iterations per batch (μ) for GRPO.\n  num_iterations: int | None\n  # Epsilon value for clipping in the GRPO algorithm.\n  epsilon: float | None\n  # Upper-bound epsilon value for clipping in the GRPO algorithm.\n  epsilon_high: float | None\n  # Whether to use Liger loss for GRPO.\n  use_liger_loss: bool | None\n  # Loss formulation to use. Supported values: grpo, bnpo, dr_grpo.\n  loss_type: str | None\n  # Whether to exclude truncated completions from loss calculation.\n  mask_truncated_completions: bool = False\n  # Enable sleep mode for vLLM to offload VRAM when idle\n  vllm_enable_sleep_mode: bool | None\n  # Path to custom rollout function. Must be importable from current dir.\n  rollout_func: str | None\n  # Multi-objective reward aggregation strategy. 'sum_then_normalize' (GRPO default):\n  # weights and sums rewards first, then normalizes. 'normalize_then_sum' (GDPO):\n  # normalizes each reward independently, then sums.\n  multi_objective_aggregation: Literal['sum_then_normalize', 'normalize_then_sum'] | None\n\nvllm: VllmConfig | None\n  # For VllmConfig:\n  # Device to use for VLLM\n  device: str | None = auto\n  # Tensor parallel size for VLLM\n  tensor_parallel_size: int | None\n  # Data parallel size for VLLM\n  data_parallel_size: int | None\n  # GPU memory utilization for VLLM\n  gpu_memory_utilization: float | None = 0.9\n  # Data type for VLLM\n  dtype: str | None = auto\n  # Maximum length of the model context for VLLM\n  max_model_len: int | None\n  # Enable prefix caching for VLLM\n  enable_prefix_caching: bool | None\n  # Host for the vLLM server to start on\n  host: str | None = 0.0.0.0\n  # Port of the vLLM server to start on\n  port: int | None = 8000\n\n  # Enable reasoning for VLLM\n  enable_reasoning: bool | None\n  # Reasoning parser for VLLM\n  reasoning_parser: str | None\n\nqat: QATConfig | None\n  # For QATConfig:\n  # Fake quantization layout to use for activation quantization.\n  activation_dtype: TorchAOQuantDType | None\n  # Fake quantization layout to use for weight quantization.\n  weight_dtype: TorchAOQuantDType = TorchAOQuantDType.int8\n  # Quantize embedding\n  quantize_embedding: bool | None = False\n  # The number of elements in each group for per-group fake quantization\n  group_size: int | None = 32\n  # The number of steps to apply fake quantization after\n  fake_quant_after_n_steps: int | None\n\nquantization: PTQConfig | None\n  # For PTQConfig:\n  # Fake quantization layout to use for weight quantization.\n  weight_dtype: TorchAOQuantDType = TorchAOQuantDType.int8\n  # Fake quantization layout to use for activation quantization.\n  activation_dtype: TorchAOQuantDType | None\n  # Whether to quantize the embedding layer.\n  quantize_embedding: bool | None\n  # The number of elements in each group for per-group fake quantization\n  group_size: int | None = 32\n\n# Reward modelling: `True` or `False`\nreward_model: bool | None\n\n# Configuration for dynamic checkpointing (trigger by file or signal). Set 'enabled:\n# true' to activate this feature.\ndynamic_checkpoint: DynamicCheckpointConfig | None\n  # For DynamicCheckpointConfig:\n  # Enable dynamic checkpoint triggering during training. Create a file\n  # 'axolotl_checkpoint.save' in the configured `output_dir` to trigger.\n  enabled: bool = False\n  # Check for trigger file every N steps (reduces I/O overhead). Default: 100\n  check_interval: int = 10\n  # Custom trigger filename (optional). If not specified, defaults to\n  # 'axolotl_checkpoint.save'. Specify a filename (not a full path) to override the\n  # default.\n  trigger_file_path: str = \n\n# Process reward modelling: `True` or `False`\nprocess_reward_model: bool | None\n# Coefficient to incentivize the reward model to output mean-zero rewards (proposed by\n# https://huggingface.co/papers/2312.09244, Eq. 2). Recommended value: `0.01`.\ncenter_rewards_coefficient: float | None\nnum_labels: int | None\n\n# Whether to perform weighting in DPO trainer\ndpo_use_weighting: bool | None\ndpo_label_smoothing: float | None\ndpo_norm_loss: bool | None\n\n# Whether to use Liger kernel for DPO loss.\ndpo_use_liger_kernel: bool | None\n\ndpo_padding_free: bool | None\n\n# A list of one or more datasets to finetune the model with\ndatasets: Annotated[list[SFTDataset | DPODataset | KTODataset | StepwiseSupervisedDataset], MinLen(1)] | None\n  # For SFTDataset:\n  # HuggingFace dataset repo | s3:// | gs:// | path to local file or directory\n  path: str | None\n  # name of dataset split to load from\n  split: str | None\n  # The type of prompt to use for training. [alpaca, gpteacher, oasst, reflection]\n  type: str | UserDefinedPrompterType | None\n    # For UserDefinedPrompterType:\n    # Custom user instruction prompt\n    system_prompt: str | None\n    # Use {system} as key to be replaced\n    system_format: str | None\n    field_system: str | None\n    field_instruction: str | None\n    field_input: str | None\n    field_output: str | None\n\n    # Customizable to be single line or multi-line. Use {instruction}/{input} as key to\n    # be replaced. 'format' can include {input}\n    format: str | None\n    # 'no_input_format' cannot include {input}\n    no_input_format: str | None\n  input_transform: str | None\n  # split dataset into N pieces (use with shards_idx)\n  shards: int | None\n  # the index of sharded dataset to use\n  shards_idx: int | None\n  # process dataset in N sequential chunks for memory efficiency (exclusive with\n  # `shards`)\n  preprocess_shards: int | None\n  conversation: str | None\n\n  # The name of the chat template to use for training, following values are supported:\n  # tokenizer_default: Uses the chat template that is available in the\n  # tokenizer_config.json. If the chat template is not available in the tokenizer, it\n  # will raise an error. This is the default.\n  # alpaca/inst/chatml/gemma/cohere/llama3/phi_3/deepseek_v2/jamba: These chat templates\n  # are available in the axolotl codebase at src/axolotl/utils/chat_templates.py.\n  # tokenizer_default_fallback_*: where * is the name of the chat template to fallback\n  # to if the tokenizer does not have a chat template else default to tokenizer. E.g.\n  # tokenizer_default_fallback_chatml. jinja: Uses a custom jinja template for the chat\n  # template. The custom jinja template should be provided in the chat_template_jinja\n  # field.\n  chat_template: ChatTemplate | str | None\n  # Custom jinja chat template or path to jinja file. Used only if `chat_template:\n  # jinja` or empty.\n  chat_template_jinja: str | None\n  # path to source data files\n  data_files: str | list[str] | None\n  input_format: str | None\n  # name of dataset configuration to load\n  name: str | None\n  # defines the datatype when path is a file\n  ds_type: str | None\n  # For `completion` datasets only, uses the provided field instead of `text` column\n  field: str | None\n  field_human: str | None\n  field_model: str | None\n  # Key containing the messages (default: \"messages\")\n  field_messages: str | None\n  # Key containing the tools (default: \"tools\"). Must be a list[dict] and follow [JSON\n  # schema](https://json-schema.org/learn/getting-started-step-by-step).\n  field_tools: str | None\n  # Key containing the reasoning trace (default: \"reasoning_content\").\n  field_thinking: str | None\n  # The key the chat template expects that indicates the reasoning trace.\n  template_thinking_key: str | None\n\n  message_field_role: str | None\n\n  message_field_content: str | None\n  # Mapping of properties from the input dataset to the chat template. (default:\n  # message_property_mappings={'role':'role', 'content':'content'}) If a property exists\n  # in the template but not in this mapping, the system will attempt to load it directly\n  # from the message using the property name as the key. Example: In the mapping below,\n  # 'from' is loaded from input dataset and used as 'role', while 'value' is loaded and\n  # used as 'content' in the chat template.\n  message_property_mappings: dict[str, str] | None\n  # The key in the message turn that indicates via boolean whether tokens of a turn\n  # should be considered for training. Useful to selectively train on certain turns\n  # besides the `roles_to_train`.\n  message_field_training: str | None\n  # The key in the message turn that contains the training details. Useful to\n  # selectively train on certain tokens in a turn. The value of the key is a List[Dict]\n  # containing `begin_offset` (start character index in content), `end_offset` (end\n  # character index in content), and `train` (boolean whether to train).\n  message_field_training_detail: str | None\n  # (for Qwen3 template only) Whether to split the assistant content based on a\n  # reasoning trace inside delimited tags\n  split_thinking: bool | None\n  logprobs_field: str | None\n  temperature: float | None\n  # Roles to train on. The tokens from these roles will be considered for the loss.\n  roles_to_train: list[str] | None\n  # Which EOS tokens to train on in the conversation. Possible values are: all: train on\n  # all EOS tokens, turn (default): train on the EOS token at the end of each trainable\n  # turn, last: train on the last EOS token in the conversation\n  train_on_eos: Literal['all', 'turn', 'last'] | None\n  # Roles mapping in the messages. The format is {target_role: [source_roles]}. All\n  # source roles will be mapped to the target role. The default is: user: [\"human\",\n  # \"user\"], assistant: [\"gpt\", \"assistant\"], system: [\"system\"], tool: [\"tool\"]\n  roles: dict[str, list[str]] | None\n  # Whether to drop the system turn from the dataset. Only works with chat_template.\n  # This does not drop the default system message from chat_template if it exists. If\n  # you wish to, we recommend using a custom jinja template with the default system\n  # message removed or adding a system turn with empty content.\n  drop_system_message: bool | None\n  # Trust remote code for untrusted source\n  trust_remote_code: bool | None = False\n  # The specific revision of the dataset to use when loading from the Hugging Face Hub.\n  # This can be a commit hash, tag, or branch name. If not specified, the latest version\n  # will be used. This parameter is ignored for local datasets.\n  revision: str | None\n\n  # For DPODataset:\n  path: str | None\n  split: str | None\n  type: UserDefinedDPOType | str | None\n    # For UserDefinedDPOType:\n    field_system: str | None\n    field_prompt: str | None\n    field_chosen: str | None\n    field_rejected: str | None\n    prompt_format: str | None\n    chosen_format: str | None\n    rejected_format: str | None\n  data_files: list[str] | None\n  revision: str | None\n  field_messages: str | None\n\n  # For KTODataset:\n  path: str | None\n  split: str | None\n  type: UserDefinedKTOType | str | None\n    # For UserDefinedKTOType:\n    field_system: str | None\n    field_prompt: str | None\n    field_completion: str | None\n    field_label: bool | None\n    prompt_format: str | None\n    completion_format: str | None\n  data_files: list[str] | None\n  trust_remote_code: bool | None = False\n  revision: str | None\n\n  # For StepwiseSupervisedDataset:\n  path: str | None\n  split: str | None\n  data_files: list[str] | None\n  revision: str | None\n  step_separator: str | None\n  max_completion_length: int | None\n  train_on_last_step_only: bool | None\n\n# A list of one or more datasets to eval the model with. You can use either\n# test_datasets, or val_set_size, but not both.\ntest_datasets: Annotated[list[SFTDataset | DPODataset | KTODataset | StepwiseSupervisedDataset], MinLen(1)] | None\n  # For SFTDataset:\n  # HuggingFace dataset repo | s3:// | gs:// | path to local file or directory\n  path: str | None\n  # name of dataset split to load from\n  split: str | None\n  # The type of prompt to use for training. [alpaca, gpteacher, oasst, reflection]\n  type: str | UserDefinedPrompterType | None\n    # For UserDefinedPrompterType:\n    # Custom user instruction prompt\n    system_prompt: str | None\n    # Use {system} as key to be replaced\n    system_format: str | None\n    field_system: str | None\n    field_instruction: str | None\n    field_input: str | None\n    field_output: str | None\n\n    # Customizable to be single line or multi-line. Use {instruction}/{input} as key to\n    # be replaced. 'format' can include {input}\n    format: str | None\n    # 'no_input_format' cannot include {input}\n    no_input_format: str | None\n  input_transform: str | None\n  # split dataset into N pieces (use with shards_idx)\n  shards: int | None\n  # the index of sharded dataset to use\n  shards_idx: int | None\n  # process dataset in N sequential chunks for memory efficiency (exclusive with\n  # `shards`)\n  preprocess_shards: int | None\n  conversation: str | None\n\n  # The name of the chat template to use for training, following values are supported:\n  # tokenizer_default: Uses the chat template that is available in the\n  # tokenizer_config.json. If the chat template is not available in the tokenizer, it\n  # will raise an error. This is the default.\n  # alpaca/inst/chatml/gemma/cohere/llama3/phi_3/deepseek_v2/jamba: These chat templates\n  # are available in the axolotl codebase at src/axolotl/utils/chat_templates.py.\n  # tokenizer_default_fallback_*: where * is the name of the chat template to fallback\n  # to if the tokenizer does not have a chat template else default to tokenizer. E.g.\n  # tokenizer_default_fallback_chatml. jinja: Uses a custom jinja template for the chat\n  # template. The custom jinja template should be provided in the chat_template_jinja\n  # field.\n  chat_template: ChatTemplate | str | None\n  # Custom jinja chat template or path to jinja file. Used only if `chat_template:\n  # jinja` or empty.\n  chat_template_jinja: str | None\n  # path to source data files\n  data_files: str | list[str] | None\n  input_format: str | None\n  # name of dataset configuration to load\n  name: str | None\n  # defines the datatype when path is a file\n  ds_type: str | None\n  # For `completion` datasets only, uses the provided field instead of `text` column\n  field: str | None\n  field_human: str | None\n  field_model: str | None\n  # Key containing the messages (default: \"messages\")\n  field_messages: str | None\n  # Key containing the tools (default: \"tools\"). Must be a list[dict] and follow [JSON\n  # schema](https://json-schema.org/learn/getting-started-step-by-step).\n  field_tools: str | None\n  # Key containing the reasoning trace (default: \"reasoning_content\").\n  field_thinking: str | None\n  # The key the chat template expects that indicates the reasoning trace.\n  template_thinking_key: str | None\n\n  message_field_role: str | None\n\n  message_field_content: str | None\n  # Mapping of properties from the input dataset to the chat template. (default:\n  # message_property_mappings={'role':'role', 'content':'content'}) If a property exists\n  # in the template but not in this mapping, the system will attempt to load it directly\n  # from the message using the property name as the key. Example: In the mapping below,\n  # 'from' is loaded from input dataset and used as 'role', while 'value' is loaded and\n  # used as 'content' in the chat template.\n  message_property_mappings: dict[str, str] | None\n  # The key in the message turn that indicates via boolean whether tokens of a turn\n  # should be considered for training. Useful to selectively train on certain turns\n  # besides the `roles_to_train`.\n  message_field_training: str | None\n  # The key in the message turn that contains the training details. Useful to\n  # selectively train on certain tokens in a turn. The value of the key is a List[Dict]\n  # containing `begin_offset` (start character index in content), `end_offset` (end\n  # character index in content), and `train` (boolean whether to train).\n  message_field_training_detail: str | None\n  # (for Qwen3 template only) Whether to split the assistant content based on a\n  # reasoning trace inside delimited tags\n  split_thinking: bool | None\n  logprobs_field: str | None\n  temperature: float | None\n  # Roles to train on. The tokens from these roles will be considered for the loss.\n  roles_to_train: list[str] | None\n  # Which EOS tokens to train on in the conversation. Possible values are: all: train on\n  # all EOS tokens, turn (default): train on the EOS token at the end of each trainable\n  # turn, last: train on the last EOS token in the conversation\n  train_on_eos: Literal['all', 'turn', 'last'] | None\n  # Roles mapping in the messages. The format is {target_role: [source_roles]}. All\n  # source roles will be mapped to the target role. The default is: user: [\"human\",\n  # \"user\"], assistant: [\"gpt\", \"assistant\"], system: [\"system\"], tool: [\"tool\"]\n  roles: dict[str, list[str]] | None\n  # Whether to drop the system turn from the dataset. Only works with chat_template.\n  # This does not drop the default system message from chat_template if it exists. If\n  # you wish to, we recommend using a custom jinja template with the default system\n  # message removed or adding a system turn with empty content.\n  drop_system_message: bool | None\n  # Trust remote code for untrusted source\n  trust_remote_code: bool | None = False\n  # The specific revision of the dataset to use when loading from the Hugging Face Hub.\n  # This can be a commit hash, tag, or branch name. If not specified, the latest version\n  # will be used. This parameter is ignored for local datasets.\n  revision: str | None\n\n  # For DPODataset:\n  path: str | None\n  split: str | None\n  type: UserDefinedDPOType | str | None\n    # For UserDefinedDPOType:\n    field_system: str | None\n    field_prompt: str | None\n    field_chosen: str | None\n    field_rejected: str | None\n    prompt_format: str | None\n    chosen_format: str | None\n    rejected_format: str | None\n  data_files: list[str] | None\n  revision: str | None\n  field_messages: str | None\n\n  # For KTODataset:\n  path: str | None\n  split: str | None\n  type: UserDefinedKTOType | str | None\n    # For UserDefinedKTOType:\n    field_system: str | None\n    field_prompt: str | None\n    field_completion: str | None\n    field_label: bool | None\n    prompt_format: str | None\n    completion_format: str | None\n  data_files: list[str] | None\n  trust_remote_code: bool | None = False\n  revision: str | None\n\n  # For StepwiseSupervisedDataset:\n  path: str | None\n  split: str | None\n  data_files: list[str] | None\n  revision: str | None\n  step_separator: str | None\n  max_completion_length: int | None\n  train_on_last_step_only: bool | None\n\n# If false, the datasets will not be shuffled and will keep their original order in\n# `datasets`. The same applies to the `test_datasets` option and the\n# `pretraining_dataset` option. Default is true.\nshuffle_merged_datasets: bool | None = True\n# If true, each dataset in `datasets` will be shuffled before merging. This allows\n# curriculum learning strategies to be applied at the dataset level. Default is false.\nshuffle_before_merging_datasets: bool | None = False\n# Axolotl attempts to save the dataset as an arrow after packing the data together so\n# subsequent training attempts load faster, relative path\ndataset_prepared_path: str | None\n# Num shards for whole dataset\ndataset_shard_num: int | None\n# Index of shard to use for whole dataset\ndataset_shard_idx: int | None\nskip_prepare_dataset: bool | None = False\n# Number of shards to save the prepared dataset\nnum_dataset_shards_to_save: int | None\n\n# Set to HF dataset for type: 'completion' for streaming instead of pre-tokenize\npretraining_dataset: Annotated[list[PretrainingDataset | SFTDataset], MinLen(1)] | None\n  # For PretrainingDataset:\n  name: str | None\n  path: str | None\n  split: str | None = train\n  text_column: str | None = text\n  type: str | None = pretrain\n  trust_remote_code: bool | None = False\n  data_files: str | None\n  skip: int | None\n\n  # For SFTDataset:\n  # HuggingFace dataset repo | s3:// | gs:// | path to local file or directory\n  path: str | None\n  # name of dataset split to load from\n  split: str | None\n  # The type of prompt to use for training. [alpaca, gpteacher, oasst, reflection]\n  type: str | UserDefinedPrompterType | None\n    # For UserDefinedPrompterType:\n    # Custom user instruction prompt\n    system_prompt: str | None\n    # Use {system} as key to be replaced\n    system_format: str | None\n    field_system: str | None\n    field_instruction: str | None\n    field_input: str | None\n    field_output: str | None\n\n    # Customizable to be single line or multi-line. Use {instruction}/{input} as key to\n    # be replaced. 'format' can include {input}\n    format: str | None\n    # 'no_input_format' cannot include {input}\n    no_input_format: str | None\n  input_transform: str | None\n  # split dataset into N pieces (use with shards_idx)\n  shards: int | None\n  # the index of sharded dataset to use\n  shards_idx: int | None\n  # process dataset in N sequential chunks for memory efficiency (exclusive with\n  # `shards`)\n  preprocess_shards: int | None\n  conversation: str | None\n\n  # The name of the chat template to use for training, following values are supported:\n  # tokenizer_default: Uses the chat template that is available in the\n  # tokenizer_config.json. If the chat template is not available in the tokenizer, it\n  # will raise an error. This is the default.\n  # alpaca/inst/chatml/gemma/cohere/llama3/phi_3/deepseek_v2/jamba: These chat templates\n  # are available in the axolotl codebase at src/axolotl/utils/chat_templates.py.\n  # tokenizer_default_fallback_*: where * is the name of the chat template to fallback\n  # to if the tokenizer does not have a chat template else default to tokenizer. E.g.\n  # tokenizer_default_fallback_chatml. jinja: Uses a custom jinja template for the chat\n  # template. The custom jinja template should be provided in the chat_template_jinja\n  # field.\n  chat_template: ChatTemplate | str | None\n  # Custom jinja chat template or path to jinja file. Used only if `chat_template:\n  # jinja` or empty.\n  chat_template_jinja: str | None\n  # path to source data files\n  data_files: str | list[str] | None\n  input_format: str | None\n  # name of dataset configuration to load\n  name: str | None\n  # defines the datatype when path is a file\n  ds_type: str | None\n  # For `completion` datasets only, uses the provided field instead of `text` column\n  field: str | None\n  field_human: str | None\n  field_model: str | None\n  # Key containing the messages (default: \"messages\")\n  field_messages: str | None\n  # Key containing the tools (default: \"tools\"). Must be a list[dict] and follow [JSON\n  # schema](https://json-schema.org/learn/getting-started-step-by-step).\n  field_tools: str | None\n  # Key containing the reasoning trace (default: \"reasoning_content\").\n  field_thinking: str | None\n  # The key the chat template expects that indicates the reasoning trace.\n  template_thinking_key: str | None\n\n  message_field_role: str | None\n\n  message_field_content: str | None\n  # Mapping of properties from the input dataset to the chat template. (default:\n  # message_property_mappings={'role':'role', 'content':'content'}) If a property exists\n  # in the template but not in this mapping, the system will attempt to load it directly\n  # from the message using the property name as the key. Example: In the mapping below,\n  # 'from' is loaded from input dataset and used as 'role', while 'value' is loaded and\n  # used as 'content' in the chat template.\n  message_property_mappings: dict[str, str] | None\n  # The key in the message turn that indicates via boolean whether tokens of a turn\n  # should be considered for training. Useful to selectively train on certain turns\n  # besides the `roles_to_train`.\n  message_field_training: str | None\n  # The key in the message turn that contains the training details. Useful to\n  # selectively train on certain tokens in a turn. The value of the key is a List[Dict]\n  # containing `begin_offset` (start character index in content), `end_offset` (end\n  # character index in content), and `train` (boolean whether to train).\n  message_field_training_detail: str | None\n  # (for Qwen3 template only) Whether to split the assistant content based on a\n  # reasoning trace inside delimited tags\n  split_thinking: bool | None\n  logprobs_field: str | None\n  temperature: float | None\n  # Roles to train on. The tokens from these roles will be considered for the loss.\n  roles_to_train: list[str] | None\n  # Which EOS tokens to train on in the conversation. Possible values are: all: train on\n  # all EOS tokens, turn (default): train on the EOS token at the end of each trainable\n  # turn, last: train on the last EOS token in the conversation\n  train_on_eos: Literal['all', 'turn', 'last'] | None\n  # Roles mapping in the messages. The format is {target_role: [source_roles]}. All\n  # source roles will be mapped to the target role. The default is: user: [\"human\",\n  # \"user\"], assistant: [\"gpt\", \"assistant\"], system: [\"system\"], tool: [\"tool\"]\n  roles: dict[str, list[str]] | None\n  # Whether to drop the system turn from the dataset. Only works with chat_template.\n  # This does not drop the default system message from chat_template if it exists. If\n  # you wish to, we recommend using a custom jinja template with the default system\n  # message removed or adding a system turn with empty content.\n  drop_system_message: bool | None\n  # Trust remote code for untrusted source\n  trust_remote_code: bool | None = False\n  # The specific revision of the dataset to use when loading from the Hugging Face Hub.\n  # This can be a commit hash, tag, or branch name. If not specified, the latest version\n  # will be used. This parameter is ignored for local datasets.\n  revision: str | None\n\n# The maximum number of processes to use while preprocessing your input dataset. This\n# defaults to `os.cpu_count()` if not set. For Runpod VMs, it will default to number of\n# vCPUs via RUNPOD_CPU_COUNT.\ndataset_processes: int | None\n# The maximum number of processes to use while preprocessing your input dataset. This\n# defaults to `os.cpu_count()` if not set. For Runpod VMs, it will default to number of\n# vCPUs via RUNPOD_CPU_COUNT.\ndataset_num_proc: int | None\n\n# Deduplicates datasets and test_datasets with identical entries\ndataset_exact_deduplication: bool | None\n# Keep dataset in memory while preprocessing. Only needed if cached dataset is taking\n# too much storage\ndataset_keep_in_memory: bool | None\ndataloader_pin_memory: bool | None\ndataloader_num_workers: int | None\ndataloader_prefetch_factor: int | None\ndataloader_drop_last: bool | None\n\naccelerator_config: dict[str, Any] | None\n\nremove_unused_columns: bool | None\n\n# Push prepared dataset to hub - repo_org/repo_name\npush_dataset_to_hub: str | None\n# Whether to use hf `use_auth_token` for loading datasets. Useful for fetching private\n# datasets. Required to be true when used in combination with `push_dataset_to_hub`\nhf_use_auth_token: bool | None\n\ndevice: Any | None\n# Passed through to transformers when loading the model when launched without\n# accelerate. Use `sequential` when training w/ model parallelism to limit memory\ndevice_map: Any | None\nworld_size: int | None\n# Don't mess with this, it's here for accelerate and torchrun\nlocal_rank: int | None\nddp: bool | None\n\n# Seed for reproducibility\nseed: int | None\n# Advanced DDP Arguments - timeout\nddp_timeout: int | None\n# Advanced DDP Arguments - bucket cap in MB\nddp_bucket_cap_mb: int | None\n# Advanced DDP Arguments - broadcast buffers\nddp_broadcast_buffers: bool | None\nddp_find_unused_parameters: bool | None\n\n# Whether to run causal language model evaluation for metrics in\n# `eval_causal_lm_metrics`\ndo_causal_lm_eval: bool | None\n# HF evaluate metrics used during evaluation. Default is ['sacrebleu', 'comet', 'ter',\n# 'chrf', 'perplexity']\neval_causal_lm_metrics: list[str] | None\ndo_bench_eval: bool | None\nbench_dataset: str | None\nbench_split: str | None\nmetric_for_best_model: str | None\ngreater_is_better: bool | None\n\n# High loss value, indicating the learning has broken down (a good estimate is ~2 times\n# the loss at the start of training)\nloss_watchdog_threshold: float | None\n# Number of high-loss steps in a row before the trainer aborts (default: 3)\nloss_watchdog_patience: int | None\n\n# Run garbage collection every `gc_steps` steps. -1 will run on epoch end and before\n# evaluations. Default is 0 (disabled).\ngc_steps: int | None\n\n# Use CUDA bf16. bool or 'full' for `bf16_full_eval`, or 'auto' for automatic detection.\n# require &gt;=ampere\nbf16: Literal['auto'] | bool | None = auto\n# Use CUDA fp16\nfp16: bool | None\n# Enable FP8 mixed precision training using TorchAO. Best used in combination with\n# torch.compile.\nfp8: bool | None\n# Enable FSDP float8 all-gather optimization for FP8 training. Can improve training\n# speed by 10-15% when FSDP is enabled.\nfp8_enable_fsdp_float8_all_gather: bool | None\n# No AMP (automatic mixed precision) - require &gt;=ampere\nbfloat16: bool | None\n# No AMP (automatic mixed precision)\nfloat16: bool | None\n# bool to use CUDA tf32 or 'auto' for automatic detection - require &gt;=ampere\ntf32: Literal['auto'] | bool | None = auto\nfloat32: bool | None\n\n# Whether to use gradient checkpointing. Available options are: true, false, 'offload',\n# 'offload_disk'.\n# https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing\ngradient_checkpointing: Literal['offload', 'offload_disk'] | bool | None = False\n# Additional kwargs to pass to the trainer for gradient checkpointing\ngradient_checkpointing_kwargs: dict[str, Any] | None\n# Whether to offload activations. Available options are: true, false, 'legacy', 'disk'.\nactivation_offloading: Literal['legacy', 'disk'] | bool | None = False\n\n# List of regex patterns for parameter names to keep unfrozen. All other parameters will\n# be frozen via requires_grad=False. Note: range-based patterns (e.g.\n# embed_tokens.weight$[:32000]) use gradient zeroing rather than a true freeze, so\n# weight decay will still apply to the frozen portion and optimizer states are allocated\n# for the full parameter.\nunfrozen_parameters: list[str] | None\n\n# The maximum length of an input to train with, this should typically be less than 2048\n# as most models have a token/context limit of 2048\nsequence_len: int = 512\n# What to do when a tokenized row exceeds sequence_len. 'drop' removes the row;\n# 'truncate' slices tensors to sequence_len; 'raise' raises a ValueError. Defaults to\n# 'drop' for backward compatibility.\nexcess_length_strategy: Literal['drop', 'truncate', 'raise'] | None\n# The maximum length of an input for evaluation. If not specified, defaults to\n# sequence_len\neval_sequence_len: int | None\nmin_sample_len: int | None\n# maximum prompt length for RL training\nmax_prompt_len: int | None\n# Use efficient multi-packing with block diagonal attention and per sequence\n# position_ids. Recommend set to 'true'\nsample_packing: bool | None\n# The number of samples packed at a time. Increasing the following values helps with\n# packing, but usually only slightly (&lt;%1.)\nsample_packing_group_size: int | None = 100000\n# The number of samples which can be packed into one sequence. Increase if using a large\n# sequence_len with many short samples.\nsample_packing_bin_size: int | None = 200\n# Whether to pack samples sequentially\nsample_packing_sequentially: bool | None\n# The multiprocessing start method to use for packing. Should be 'fork', 'spawn' or\n# 'forkserver'\nsample_packing_mp_start_method: str | None\n# Set to 'false' if getting errors during eval with sample_packing on\neval_sample_packing: bool | None\n# Pad inputs so each step uses constant sized buffers. This will reduce memory\n# fragmentation and may prevent OOMs, by re-using memory more efficiently. Defaults to\n# True if `sample_packing` enabled\npad_to_sequence_len: bool | None\n# Whether to use sequential sampling for curriculum learning\ncurriculum_sampling: bool | None\nmultipack_real_batches: bool | None\n\n# Use batch flattening for speedups when not using sample_packing\nbatch_flattening: Literal['auto'] | bool | None\n\nuse_pose: bool | None\npose_split_on_token_ids: list[int] | None\npose_max_context_len: int | None\npose_num_chunks: int | None\n\npretrain_multipack_buffer_size: int | None\n# whether to prevent cross attention for packed sequences during pretraining\npretrain_multipack_attn: bool | None = True\n# whether to concatenate samples during pretraining\npretraining_sample_concatenation: bool | None\n\n# Use streaming mode for loading datasets\nstreaming: bool | None\n# Buffer size for multipack streaming datasets\nstreaming_multipack_buffer_size: int | None = 10000\n\n# Whether to use xformers attention patch https://github.com/facebookresearch/xformers\nxformers_attention: bool | None\n# Whether to use scaled-dot-product attention https://pytorch.org/docs/stable/generated/\n# torch.nn.functional.scaled_dot_product_attention.html\nsdp_attention: bool | None\n# Shifted-sparse attention (only llama) - https://arxiv.org/pdf/2309.12307.pdf\ns2_attention: bool | None\nflex_attention: bool | None\nflex_attn_compile_kwargs: dict[str, Any] | None\n# Whether to use flash attention patch https://github.com/Dao-AILab/flash-attention\nflash_attention: bool | None\n# Whether to use flash-attention cross entropy implementation - advanced use only\nflash_attn_cross_entropy: bool | None\n# Whether to use flash-attention rms norm implementation - advanced use only\nflash_attn_rms_norm: bool | None\n# Whether to fuse part of the MLP into a single operation\nflash_attn_fuse_mlp: bool | None\n# Whether to use bettertransformers\nflash_optimum: bool | None\n# Whether to use SageAttention https://github.com/thu-ml/SageAttention\nsage_attention: bool | None\n\neager_attention: bool | None\n\n# Specify a custom attention implementation, used mostly for kernels.\nattn_implementation: str | None\n\n# Which experts implementation to use for MoE models,\nexperts_implementation: str | None\n\n# Quantize MoE expert weights on load to reduce VRAM. Requires adapter (lora/qlora) with\n# load_in_4bit or load_in_8bit. Requires CUDA (not compatible with ROCm or other\n# backends). Note: total parameter count may be reported incorrectly when enabled\n# (trainable param count is correct).\nquantize_moe_experts: bool = False\n\n# Whether to use Scaled Softmax (SSMax) attention. Ref: https://arxiv.org/abs/2501.19399\nscaling_softmax: bool | None\n# Scaling factor for SSMax attention. Default is 0.43\nscaling_softmax_factor: float | None\n# Bias for SSMax attention. Default is 0.0. Note: The paper recommends bias=0 for better\n# length generalization.\nscaling_softmax_bias: float | None\n\nunsloth_cross_entropy_loss: bool | None\nunsloth_lora_mlp: bool | None\nunsloth_lora_qkv: bool | None\nunsloth_lora_o: bool | None\nunsloth_rms_norm: bool | None\nunsloth_rope: bool | None\n\n# Apply custom LoRA autograd functions and activation function Triton kernels for speed\n# and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html\nlora_mlp_kernel: bool | None\n# Apply custom LoRA autograd functions and activation function Triton kernels for speed\n# and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html\nlora_qkv_kernel: bool | None\n# Apply custom LoRA autograd functions and activation function Triton kernels for speed\n# and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html\nlora_o_kernel: bool | None\n\n# Whether to use chunked cross entropy loss for memory efficiency\nchunked_cross_entropy: bool | None\n# Number of chunks to use for chunked cross entropy loss\nchunked_cross_entropy_num_chunks: int | None\n# Enable Entropy-Aware Focal Training loss (EAFT)\nuse_eaft: bool | None\n# Exponent for entropy weighting in EAFT (default: 1.0)\neaft_alpha: float | None = 1.0\n# Number of top logits for entropy approximation (default: 20)\neaft_k: int | None = 20\n\n# Whether to use ALST tiled mlp for memory efficient long context\ntiled_mlp: bool | None\n\n# Number of shards to use for ALST tiled mlp. If unset, it will be set based on\n# seqlen/hidden_size\ntiled_mlp_num_shards: int | None\n\n# Whether to use original mlp for ALST tiled mlp. Otherwise uses a generic MLP based on\n# llama.\ntiled_mlp_use_original_mlp: bool | None = True\n\nllama4_linearized_experts: bool | None\n\n# Deepspeed config path. e.g., deepspeed_configs/zero3.json\ndeepspeed: str | dict[str, Any] | None\n# Whether to use deepcompile for faster training with deepspeed\ndeepcompile: bool | None\n# FSDP configuration\nfsdp: list[str] | None\n\n# FSDP configuration options\nfsdp_config: FSDPConfig | None\n  # For FSDPConfig:\n  # FSDP version\n  fsdp_version: int | None\n  # Enable activation checkpointing to reduce memory usage during forward passes\n  activation_checkpointing: bool | None\n  # Offload parameters to CPU to reduce GPU memory usage\n  offload_params: bool | None\n  # Synchronize module states across all processes\n  sync_module_states: bool | None\n  # Enable CPU RAM efficient loading to reduce memory usage during model loading\n  cpu_ram_efficient_loading: bool | None\n  # Disabling this enables swap memory usage for resource-constrained setups when\n  # offload_params is enabled.\n  cpu_offload_pin_memory: bool | None\n  # Use original parameters instead of flattened parameters\n  use_orig_params: bool | None\n\n  # Type of state dict to use for saving/loading checkpoints\n  state_dict_type: Literal['FULL_STATE_DICT', 'LOCAL_STATE_DICT', 'SHARDED_STATE_DICT'] | None\n  # Final state dict type to use after training completion\n  final_state_dict_type: Literal['FULL_STATE_DICT', 'LOCAL_STATE_DICT', 'SHARDED_STATE_DICT'] | None\n\n  # Policy for automatically wrapping modules with FSDP\n  auto_wrap_policy: Literal['TRANSFORMER_BASED_WRAP', 'SIZE_BASED_WRAP'] | None\n  # Class name of transformer layers to wrap (e.g., 'LlamaDecoderLayer')\n  transformer_layer_cls_to_wrap: str | None\n\n  # Reshard parameters after forward pass to save memory\n  reshard_after_forward: bool | None\n  # Mixed precision policy for FSDP (e.g., 'fp16', 'bf16')\n  mixed_precision_policy: str | None\n\n# FSDP version\nfsdp_version: int | None\nfsdp_final_state_dict_type: Literal['FULL_STATE_DICT', 'LOCAL_STATE_DICT', 'SHARDED_STATE_DICT'] | None\n\n# How much of the dataset to set aside as evaluation. 1 = 100%, 0.50 = 50%, etc. 0 for\n# no eval.\nval_set_size: float | None = 0.0\n\n# Number of devices to shard across. If not set, will use all available devices.\ndp_shard_size: int | None\n# Number of devices to replicate across.\ndp_replicate_size: int | None\n# Deprecated: use `context_parallel_size` instead\nsequence_parallel_degree: int | None\n# Set to a divisor of the number of GPUs available to split sequences into chunks of\n# equal size. Use in long context training to prevent OOM when sequences cannot fit into\n# a single GPU's VRAM. E.g., if 4 GPUs are available, set this value to 2 to split each\n# sequence into two equal-sized subsequences, or set to 4 to split into four equal-sized\n# subsequences. See https://docs.axolotl.ai/docs/sequence_parallelism.html for more\n# details.\ncontext_parallel_size: int | None\n# Optional; strides across the key dimension. Larger values use more memory but should\n# make training faster. Must evenly divide the number of KV heads in your model.\nheads_k_stride: int | None\n# One of 'varlen_llama3', 'batch_ring', 'batch_zigzag', 'batch_stripe'. Defaults to\n# 'varlen_llama3' in the sample packing case, and 'batch_ring' in the non-sample packing\n# case.\nring_attn_func: RingAttnFunc | None\n# Number of tensor parallel processes in TP group. Only supported with DeepSpeed AutoTP.\ntensor_parallel_size: int | None\n\n# Add or change special tokens. If you add tokens here, you don't need to add them to\n# the `tokens` list.\nspecial_tokens: SpecialTokensConfig | None\n  # For SpecialTokensConfig:\n  bos_token: str | None\n  eos_token: str | None\n  pad_token: str | None\n  unk_token: str | None\n  additional_special_tokens: list[str] | None\n\n# Add extra tokens to the tokenizer\ntokens: list[str] | None\n# Mapping token_id to new_token_string to override reserved added_tokens in the\n# tokenizer. Only works for tokens that are not part of the base vocab (aka are\n# added_tokens). Can be checked if they exist in tokenizer.json added_tokens.\nadded_tokens_overrides: dict[int, str] | None\n\n# Whether to use torch.compile and which backend to use. setting to `auto` will enable\n# torch compile when torch&gt;=2.6.0\ntorch_compile: Literal['auto'] | bool | None\n# Backend to use for torch.compile\ntorch_compile_backend: str | None\ntorch_compile_mode: Literal['default', 'reduce-overhead', 'max-autotune'] | None\n\n# Maximum number of iterations to train for. It precedes num_epochs which means that if\n# both are set, num_epochs will not be guaranteed. e.g., when 1 epoch is 1000 steps =&gt;\n# `num_epochs: 2` and `max_steps: 100` will train for 100 steps\nmax_steps: int | None\n# Number of warmup steps. Cannot use with warmup_ratio\nwarmup_steps: int | None\n# Warmup ratio. Cannot use with warmup_steps\nwarmup_ratio: float | None\n# Leave empty to eval at each epoch, integer for every N steps. float for fraction of\n# total steps\neval_steps: int | float | None\n# Number of times per epoch to run evals, mutually exclusive with eval_steps\nevals_per_epoch: int | None\n# Set to `no` to skip evaluation, `epoch` at end of each epoch, leave empty to infer\n# from `eval_steps`\neval_strategy: str | None\n\n# Leave empty to save at each epoch, integer for every N steps. float for fraction of\n# total steps\nsave_steps: int | float | None\n# Number of times per epoch to save a checkpoint, mutually exclusive with save_steps\nsaves_per_epoch: int | None\n# Set to `no` to skip checkpoint saves, `epoch` at end of each epoch, `best` when better\n# result is achieved, leave empty to infer from `save_steps`\nsave_strategy: str | None\n# Checkpoints saved at a time\nsave_total_limit: int | None\n# Whether to checkpoint a model after the first step of training. Defaults to False.\nsave_first_step: bool | None\n\n# Logging frequency\nlogging_steps: int | None\n# Stop training after this many evaluation losses have increased in a row. https://huggi\n# ngface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppin\n# gCallback\nearly_stopping_patience: int | None\nload_best_model_at_end: bool | None = False\n# Save only the model weights, skipping the optimizer. Using this means you can't resume\n# from checkpoints.\nsave_only_model: bool | None = False\n# Use tensorboard for logging\nuse_tensorboard: bool | None\n# Enable the pytorch profiler to capture the first N steps of training to the\n# output_dir. see https://pytorch.org/blog/understanding-gpu-memory-1/ for more\n# information. Snapshots can be visualized @ https://pytorch.org/memory_viz\nprofiler_steps: int | None\n# Which step to start the profiler at. Useful for only capturing a few steps mid-run.\nprofiler_steps_start: int | None = 0\n# bool of whether to report tokens per second at the end of training. This is not\n# supported with pre-training datasets.\ninclude_tokens_per_second: bool | None\n# bool of whether to report tokens per second per-gpu during training by measuring\n# throughput of non-padding tokens.\ninclude_tkps: bool | None = True\n# NEFT https://arxiv.org/abs/2310.05914, set this to a number (paper default is 5) to\n# add noise to embeddings. Currently only supported on Llama and Mistral\nneftune_noise_alpha: float | None\n\n# Parameter controlling the relative ratio loss weight in the ORPO loss. Passed to\n# `beta` in `ORPOConfig` due to trl mapping.\norpo_alpha: float | None\n# Weighting of NLL term in loss from RPO paper\nrpo_alpha: float | None\n# Target reward margin for the SimPO loss\nsimpo_gamma: float | None\n# Weight of the BC regularizer\ncpo_alpha: float | None\n\n# Factor for desirable loss term in KTO loss\nkto_desirable_weight: float | None\n# Factor for undesirable loss term in KTO loss\nkto_undesirable_weight: float | None\n# The beta parameter for the RL training\nrl_beta: float | None\n\n# Defines the max memory usage per gpu on the system. Passed through to transformers\n# when loading the model.\nmax_memory: dict[int | Literal['cpu', 'disk'], int | str] | None\n# Limit the memory for all available GPUs to this amount (if an integer, expressed in\n# gigabytes); default: unset\ngpu_memory_limit: int | str | None\n# Whether to use low_cpu_mem_usage\nlow_cpu_mem_usage: bool | None\n\n# The name of the chat template to use for training, following values are supported:\n# tokenizer_default: Uses the chat template that is available in the\n# tokenizer_config.json. If the chat template is not available in the tokenizer, it will\n# raise an error. This is the default value.\n# alpaca/inst/chatml/gemma/cohere/llama3/phi_3/deepseek_v2/jamba: These chat templates\n# are available in the axolotl codebase at src/axolotl/utils/chat_templates.py.\n# tokenizer_default_fallback_*: where * is the name of the chat template to fallback to.\n# E.g. tokenizer_default_fallback_chatml. This is useful when the chat template is not\n# available in the tokenizer. jinja: Uses a custom jinja template for the chat template.\n# The custom jinja template should be provided in the chat_template_jinja field. The\n# selected chat template will be saved to the tokenizer_config.json for easier\n# inferencing\nchat_template: ChatTemplate | Annotated[str, StringConstraints(pattern='^tokenizer_default_fallback_')] | None\n# Custom jinja template or path to jinja file for chat template. This will be only used\n# if chat_template is set to `jinja` or `null` (in which case chat_template is\n# automatically set to `jinja`). Default is null.\nchat_template_jinja: str | None\n# Additional kwargs to pass to the chat template. This is useful for customizing the\n# chat template. For example, you can pass `thinking=False` to add a generation prompt\n# to the chat template.\nchat_template_kwargs: dict[str, Any] | None\n# Custom EOT (End-of-Turn) tokens to mask/unmask during training. These tokens mark the\n# boundaries between conversation turns. For example: ['/INST', '&lt;/s&gt;',\n# '[/SYSTEM_PROMPT]']. If not specified, defaults to just the model's eos_token. This is\n# useful for templates that use multiple delimiter tokens.\neot_tokens: list[str] | None\n# Changes the default system message. Currently only supports chatml.\ndefault_system_message: str | None\n\n# Token index or indices to adjust embedding weights to the mean of the other tokens.\n# This is useful when the model has untrained embeddings.\nfix_untrained_tokens: int | list[int] | None\n\nis_preprocess: bool | None\npreprocess_iterable: bool | None\n\n# Total number of tokens - internal use\ntotal_num_tokens: int | None\ntotal_supervised_tokens: int | None\n# You can set these packing optimizations AFTER starting a training at least once. The\n# trainer will provide recommended values for these values.\nsample_packing_eff_est: float | None\naxolotl_config_path: str | None\n\n# Internal use only - Used to identify which the model is based on\nis_falcon_derived_model: bool | None\n# Internal use only - Used to identify which the model is based on\nis_llama_derived_model: bool | None\n# Internal use only - Used to identify which the model is based on. Please note that if\n# you set this to true, `padding_side` will be set to 'left' by default\nis_mistral_derived_model: bool | None\n# Internal use only - Used to identify which the model is based on\nis_qwen_derived_model: bool | None\n\n# Add plugins to extend the pipeline. See `src/axolotl/integrations` for the available\n# plugins or doc below for more details.\n# https://docs.axolotl.ai/docs/custom_integrations.html\nplugins: list[str] | None\n# Enable sample generation during training for monitoring\ngenerate_samples: bool | None = False\n# Number of samples to generate at each interval\nnum_generation_samples: int | None = 3\n# Maximum new tokens to generate per sample\ngeneration_max_new_tokens: int | None = 50\n# Temperature for sample generation (0.0 = greedy)\ngeneration_temperature: float | None = 0.7\n# Nucleus sampling parameter for generation\ngeneration_top_p: float | None\n# Top-k sampling parameter for generation\ngeneration_top_k: int | None\n# Ratio of input to use as prompt (0.0-1.0)\ngeneration_prompt_ratio: float | None = 0.5\n# Whether to use sampling (vs greedy decoding)\ngeneration_do_sample: bool | None = True\n\n# This is the huggingface model that contains *.pt, *.safetensors, or *.bin files. This\n# can also be a relative path to a model on disk\nbase_model: str (required)\n# If the base_model repo on hf hub doesn't include configuration .json files, You can\n# set that here, or leave this empty to default to base_model\nbase_model_config: str | None\n# transformers config class (e.g., 'LlamaConfig', 'MistralConfig'). Defaults to\n# AutoConfig.\ncls_model_config: str | None\n# Optional tokenizer configuration path in case you want to use a different tokenizer\n# than the one defined in the base model\ntokenizer_config: str | None\n# use_fast option for tokenizer loading from_pretrained, default to True\ntokenizer_use_fast: bool | None\n# Whether to use the legacy tokenizer setting, defaults to True\ntokenizer_legacy: bool | None\n# Whether to use mistral-common tokenizer. If set to True, it will use the mistral-\n# common tokenizer.\ntokenizer_use_mistral_common: bool | None\n# Corresponding tokenizer for the model AutoTokenizer is a good choice\ntokenizer_type: str | None\n# transformers processor class\nprocessor_type: str | None\n# Whether to save jinja files for tokenizer, transformers default is True\ntokenizer_save_jinja_files: bool | None = True\n# Trust remote code for untrusted source\ntrust_remote_code: bool | None\n\n# Don't move the model to the device before sharding. Set to `false` to revert to legacy\n# behavior.\nexperimental_skip_move_to_device: bool | None = True\n\n# Use custom kernels, e.g. MegaBlocks.\nuse_kernels: bool | None\n\n# Model loading quantization config\nmodel_quantization_config: Literal['Mxfp4Config'] | None\n# kwargs for model quantization config\nmodel_quantization_config_kwargs: dict[str, Any] | None\n\n# Where to save the full-finetuned model to\noutput_dir: str = ./model-out\n# push checkpoints to hub\nhub_model_id: str | None\n# how to push checkpoints to hub\nhub_strategy: str | None\n# branch/revision to push to on hub (default: main)\nhub_revision: str | None\n# Whether to save the model using safetensors format. Defaults to True.\nsave_safetensors: bool | None = True\n\n# This will attempt to quantize the model down to 8 bits and use adam 8 bit optimizer\nload_in_8bit: bool | None = False\n# Use bitsandbytes 4 bit\nload_in_4bit: bool | None = False\n\n# If you want to use 'lora', 'qlora', or 'llama-adapter', or leave blank to train all\n# parameters in original model\nadapter: Literal['lora', 'qlora', 'llama-adapter'] | None\n# If you already have a lora model trained that you want to load, put that here. This\n# means after training, if you want to test the model, you should set this to the value\n# of `output_dir`. Note that if you merge an adapter to the base model, a new\n# subdirectory `merged` will be created under the `output_dir`.\nlora_model_dir: str | None\nlora_r: int | None\nlora_alpha: int | None\nlora_fan_in_fan_out: bool | None\nlora_target_modules: str | list[str] | None\nlora_target_parameters: str | list[str] | None\n# If true, will target all linear modules\nlora_target_linear: bool | None\n# If you added new tokens to the tokenizer, you may need to save some LoRA modules\n# because they need to know the new tokens. For LLaMA and Mistral, you need to save\n# `embed_tokens` and `lm_head`. It may vary for other models. `embed_tokens` converts\n# tokens to embeddings, and `lm_head` converts embeddings to token probabilities.\nlora_modules_to_save: list[str] | None\nlora_dropout: float | None = 0.0\n# The layer indices to transform, otherwise, apply to all layers\npeft_layers_to_transform: list[int] | None\npeft_layers_pattern: list[str] | None\n\npeft: PeftConfig | None\n  # For PeftConfig:\n  # Configuration options for loftq initialization for LoRA\n  loftq_config: LoftQConfig | None\n    # For LoftQConfig:\n    # typically 4 bits\n    loftq_bits: int = 4\n\n# Whether to use DoRA.\npeft_use_dora: bool | None\n# Whether to use RSLoRA.\npeft_use_rslora: bool | None\n# List of layer indices to replicate.\npeft_layer_replication: list[tuple[int, int]] | None\n# How to initialize LoRA weights. Default to True which is MS original implementation.\npeft_init_lora_weights: bool | str | None\n# A list of token indices to fine-tune on the `embed_tokens` layer. Otherwise, a dict\n# mapping an embedding layer name to its trainable token indices. See\n# https://huggingface.co/docs/peft/v0.17.0/en/developer_guides/lora#efficiently-train-\n# tokens-alongside-lora\npeft_trainable_token_indices: list[int] | dict[str, list[int]] | None\n# Whether to tie adapter weights for tied model weights. See\n# https://github.com/huggingface/peft/issues/2864\npeft_ensure_weight_tying: bool | None\n# Whether to upcast the LoRA adapter to fp32. This is enabled by default in PEFT.\npeft_autocast_adapter_dtype: bool | None\n\n# load qlora model in sharded format for FSDP using answer.ai technique.\nqlora_sharded_model_loading: bool | None = False\n# Do the LoRA/PEFT loading on CPU -- this is required if the base model is so large it\n# takes up most or all of the available GPU VRAM, e.g. during a model and LoRA merge\nlora_on_cpu: bool | None\n# Whether you are training a 4-bit GPTQ quantized model\ngptq: bool | None\n# optional overrides to the bnb 4bit quantization configuration\nbnb_config_kwargs: dict[str, Any] | None\n\n# loraplus learning rate ratio lr_B / lr_A. Recommended value is 2^4.\nloraplus_lr_ratio: float | None\n# loraplus learning rate for lora embedding layers. Default value is 1e-6.\nloraplus_lr_embedding: float | None = 1e-06\n\nmerge_lora: bool | None\n\n# Whether to use ReLoRA. Use with jagged_restart_*steps options.\nrelora: bool | None\n# threshold for optimizer magnitude when pruning\nrelora_prune_ratio: float | None\n# True to perform lora weight merges on cpu during restarts, for modest gpu memory\n# savings\nrelora_cpu_offload: bool | None\n\n# how often to reset for jagged restarts\njagged_restart_steps: int | None\n# how many warmup steps to take after reset for jagged restarts\njagged_restart_warmup_steps: int | None\n# how many anneal steps to take before reset for jagged restarts\njagged_restart_anneal_steps: int | None\n\n# If greater than 1, backpropagation will be skipped and the gradients will be\n# accumulated for the given number of steps.\ngradient_accumulation_steps: int | None = 1\n# The number of samples to include in each batch. This is the number of samples sent to\n# each GPU. Batch size per gpu = micro_batch_size * gradient_accumulation_steps\nmicro_batch_size: int | None = 1\n# Total batch size, we do not recommended setting this manually\nbatch_size: int | None\n# per gpu micro batch size for evals, defaults to value of micro_batch_size\neval_batch_size: int | None\n\n# whether to find batch size that fits in memory. Passed to underlying transformers\n# Trainer\nauto_find_batch_size: bool | None\n\n# Whether to mask out or include the human's prompt from the training labels\ntrain_on_inputs: bool | None = False\n# Group similarly sized data to minimize padding. May be slower to start, as it must\n# download and sort the entire dataset. Note that training loss may have an oscillating\n# pattern with this enabled.\ngroup_by_length: bool | None\n\nlearning_rate: str | float (required)\nembedding_lr: float | None\nembedding_lr_scale: float | None\n# Specify weight decay\nweight_decay: float | None = 0.0\n# Specify optimizer\noptimizer: OptimizerNames | CustomSupportedOptimizers | None = OptimizerNames.ADAMW_TORCH_FUSED\n# Dictionary of arguments to pass to the optimizer\noptim_args: str | dict[str, Any] | None\n# The target modules to optimize, i.e. the module names that you would like to train,\n# right now this is used only for GaLore algorithm\noptim_target_modules: list[str] | Literal['all_linear'] | None\n# Path to torch distx for optim 'adamw_anyprecision'\ntorchdistx_path: str | None\nlr_scheduler: SchedulerType | Literal['one_cycle'] | Literal['rex'] | None = SchedulerType.COSINE\n# Specify a scheduler and kwargs to use with the optimizer\nlr_scheduler_kwargs: dict[str, Any] | None\nlr_quadratic_warmup: bool | None\n# decay lr to some percentage of the peak lr, e.g. cosine_min_lr_ratio=0.1 for 10% of\n# peak lr\ncosine_min_lr_ratio: float | None\n# freeze lr at some percentage of the step, e.g. cosine_constant_lr_ratio=0.8 means\n# start cosine_min_lr at 80% of training step\ncosine_constant_lr_ratio: float | None\n# Learning rate div factor\nlr_div_factor: float | None\n\nlr_groups: list[LrGroup] | None\n  # For LrGroup:\n  name: str (required)\n  modules: list[str] (required)\n  lr: float (required)\n\n# adamw hyperparams\nadam_epsilon: float | None\n# only used for CAME Optimizer\nadam_epsilon2: float | None\n# adamw hyperparams\nadam_beta1: float | None\n# adamw hyperparams\nadam_beta2: float | None\n# only used for CAME Optimizer\nadam_beta3: float | None\n\n# Dion Optimizer learning rate\ndion_lr: float | None\n# Dion Optimizer momentum\ndion_momentum: float | None\n# Dion Optimizer: r/d fraction for low-rank approximation. Used to compute the low-rank\n# dimension.\ndion_rank_fraction: float | None = 1.0\n# Dion Optimizer: Round up the low-rank dimension to a multiple of this number. This may\n# be useful to ensure even sharding.\ndion_rank_multiple_of: int | None = 1\n\n# Gradient clipping max norm\nmax_grad_norm: float | None\nnum_epochs: float = 1.0\n\nuse_wandb: bool | None\n# Set the name of your wandb run\nwandb_name: str | None\n# Set the ID of your wandb run\nwandb_run_id: str | None\n# \"offline\" to save run metadata locally and not sync to the server, \"disabled\" to turn\n# off wandb\nwandb_mode: str | None\n# Your wandb project name\nwandb_project: str | None\n# A wandb Team name if using a Team\nwandb_entity: str | None\nwandb_watch: str | None\n# \"checkpoint\" to log model to wandb Artifacts every `save_steps` or \"end\" to log only\n# at the end of training\nwandb_log_model: str | None\n\nuse_mlflow: bool | None\n# URI to mlflow\nmlflow_tracking_uri: str | None\n# Your experiment name\nmlflow_experiment_name: str | None\n# Your run name\nmlflow_run_name: str | None\n# set to true to copy each saved checkpoint on each save to mlflow artifact registry\nhf_mlflow_log_artifacts: bool | None\n\n# Enable or disable Comet integration.\nuse_comet: bool | None\n# API key for Comet. Recommended to set via `comet login`.\ncomet_api_key: str | None\n# Workspace name in Comet. Defaults to the user's default workspace.\ncomet_workspace: str | None\n# Project name in Comet. Defaults to Uncategorized.\ncomet_project_name: str | None\n# Identifier for the experiment. Used to append data to an existing experiment or\n# control the key of new experiments. Default to a random key.\ncomet_experiment_key: str | None\n# Create a new experiment (\"create\") or log to an existing one (\"get\"). Default\n# (\"get_or_create\") auto-selects based on configuration.\ncomet_mode: str | None\n# Set to True to log data to Comet server, or False for offline storage. Default is\n# True.\ncomet_online: bool | None\n# Dictionary for additional configuration settings, see the doc for more details.\ncomet_experiment_config: dict[str, Any] | None\n\nuse_trackio: bool | None\n# Your trackio project name\ntrackio_project_name: str | None\n# Set the name of your trackio run\ntrackio_run_name: str | None\n# Hugging Face Space ID to sync dashboard to (optional, runs locally if not provided)\ntrackio_space_id: str | None\n\n# Enable OpenTelemetry metrics collection and Prometheus export\nuse_otel_metrics: bool | None = False\n# Host to bind the OpenTelemetry metrics server to\notel_metrics_host: str | None = localhost\n# Port for the Prometheus metrics HTTP server\notel_metrics_port: int | None = 8000\n\n# the number of activate layers in LISA\nlisa_n_layers: int | None\n# how often to switch layers in LISA\nlisa_step_interval: int | None\n# path under the model to access the layers\nlisa_layers_attribute: str | None = model.layers\n\ngradio_title: str | None\ngradio_share: bool | None\ngradio_server_name: str | None\ngradio_server_port: int | None\ngradio_max_new_tokens: int | None\ngradio_temperature: float | None\n\nuse_ray: bool = False\nray_run_name: str | None\nray_num_workers: int = 1\nresources_per_worker: dict\n\n# The size of the image to resize to. It can be an integer (resized into padded-square\n# image) or a tuple (width, height).If not provided, we will attempt to load from\n# preprocessor.size, otherwise, images won't be resized.\nimage_size: int | tuple[int, int] | None\n# The resampling algorithm to use for image resizing. Default is bilinear. Please refer\n# to PIL.Image.Resampling for more details.\nimage_resize_algorithm: Literal['bilinear', 'bicubic', 'lanczos'] | Resampling | None\n\n# optional overrides to the base model configuration\noverrides_of_model_config: dict[str, Any] | None\n# optional overrides the base model loading from_pretrained\noverrides_of_model_kwargs: dict[str, Any] | None\n# If you want to specify the type of model to load, AutoModelForCausalLM is a good\n# choice too\ntype_of_model: str | None\n# You can specify to choose a specific model revision from huggingface hub\nrevision_of_model: str | None\n\nmax_packed_sequence_len: int | None\nrope_scaling: Any | None\nnoisy_embedding_alpha: float | None\ndpo_beta: float | None\nevaluation_strategy: str | None\neval_table_size: int | None\neval_max_new_tokens: int | None\ndpo_use_logits_to_keep: bool | None\ndpo_generate_during_eval: bool | None",
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-    "text": "🎉 Latest Updates\n\n2026/03:\n\nNew model support has been added in Axolotl for [Qwen3.5, Qwen3.5 MoE, GLM-4.7-Flash, GLM-4.6V, and GLM-4.5-Air.\nMoE expert quantization support (via quantize_moe_experts: true) greatly reduces VRAM when training MoE models (FSDP2 compat).\n\n2026/02:\n\nScatterMoE LoRA support. LoRA fine-tuning directly on MoE expert weights using custom Triton kernels.\nAxolotl now has support for SageAttention and GDPO (Generalized DPO).\n\n2026/01:\n\nNew integration for EAFT (Entropy-Aware Focal Training), weights loss by entropy of the top-k logit distribution, and Scalable Softmax, improves long context in attention.\n\n2025/12:\n\nAxolotl now includes support for Kimi-Linear, Plano-Orchestrator, MiMo, InternVL 3.5, Olmo3, Trinity, and Ministral3.\nDistributed Muon Optimizer support has been added for FSDP2 pretraining.\n\n2025/10: New model support has been added in Axolotl for: Qwen3 Next, Qwen2.5-vl, Qwen3-vl, Qwen3, Qwen3MoE, Granite 4, HunYuan, Magistral 2509, Apertus, and Seed-OSS.\n\n\n\nExpand older updates\n\n\n2025/09: Axolotl now has text diffusion training. Read more here.\n2025/08: QAT has been updated to include NVFP4 support. See PR.\n2025/07:\n\nND Parallelism support has been added into Axolotl. Compose Context Parallelism (CP), Tensor Parallelism (TP), and Fully Sharded Data Parallelism (FSDP) within a single node and across multiple nodes. Check out the blog post for more info.\nAxolotl adds more models: GPT-OSS, Gemma 3n, Liquid Foundation Model 2 (LFM2), and Arcee Foundation Models (AFM).\nFP8 finetuning with fp8 gather op is now possible in Axolotl via torchao. Get started here!\nVoxtral, Magistral 1.1, and Devstral with mistral-common tokenizer support has been integrated in Axolotl!\nTiledMLP support for single-GPU to multi-GPU training with DDP, DeepSpeed and FSDP support has been added to support Arctic Long Sequence Training. (ALST). See examples for using ALST with Axolotl!\n\n2025/06: Magistral with mistral-common tokenizer support has been added to Axolotl. See docs to start training your own Magistral models with Axolotl!\n2025/05: Quantization Aware Training (QAT) support has been added to Axolotl. Explore the docs to learn more!\n2025/04: Llama 4 support has been added in Axolotl. See docs to start training your own Llama 4 models with Axolotl’s linearized version!\n2025/03: Axolotl has implemented Sequence Parallelism (SP) support. Read the blog and docs to learn how to scale your context length when fine-tuning.\n2025/03: (Beta) Fine-tuning Multimodal models is now supported in Axolotl. Check out the docs to fine-tune your own!\n2025/02: Axolotl has added LoRA optimizations to reduce memory usage and improve training speed for LoRA and QLoRA in single GPU and multi-GPU training (DDP and DeepSpeed). Jump into the docs to give it a try.\n2025/02: Axolotl has added GRPO support. Dive into our blog and GRPO example and have some fun!\n2025/01: Axolotl has added Reward Modelling / Process Reward Modelling fine-tuning support. See docs.",
+    "text": "🎉 Latest Updates\n\n2026/03:\n\nNew model support has been added in Axolotl for Mistral Small 4, Qwen3.5, Qwen3.5 MoE, GLM-4.7-Flash, GLM-4.6V, and GLM-4.5-Air.\nMoE expert quantization support (via quantize_moe_experts: true) greatly reduces VRAM when training MoE models (FSDP2 compat).\n\n2026/02:\n\nScatterMoE LoRA support. LoRA fine-tuning directly on MoE expert weights using custom Triton kernels.\nAxolotl now has support for SageAttention and GDPO (Generalized DPO).\n\n2026/01:\n\nNew integration for EAFT (Entropy-Aware Focal Training), weights loss by entropy of the top-k logit distribution, and Scalable Softmax, improves long context in attention.\n\n2025/12:\n\nAxolotl now includes support for Kimi-Linear, Plano-Orchestrator, MiMo, InternVL 3.5, Olmo3, Trinity, and Ministral3.\nDistributed Muon Optimizer support has been added for FSDP2 pretraining.\n\n2025/10: New model support has been added in Axolotl for: Qwen3 Next, Qwen2.5-vl, Qwen3-vl, Qwen3, Qwen3MoE, Granite 4, HunYuan, Magistral 2509, Apertus, and Seed-OSS.\n\n\n\nExpand older updates\n\n\n2025/09: Axolotl now has text diffusion training. Read more here.\n2025/08: QAT has been updated to include NVFP4 support. See PR.\n2025/07:\n\nND Parallelism support has been added into Axolotl. Compose Context Parallelism (CP), Tensor Parallelism (TP), and Fully Sharded Data Parallelism (FSDP) within a single node and across multiple nodes. Check out the blog post for more info.\nAxolotl adds more models: GPT-OSS, Gemma 3n, Liquid Foundation Model 2 (LFM2), and Arcee Foundation Models (AFM).\nFP8 finetuning with fp8 gather op is now possible in Axolotl via torchao. Get started here!\nVoxtral, Magistral 1.1, and Devstral with mistral-common tokenizer support has been integrated in Axolotl!\nTiledMLP support for single-GPU to multi-GPU training with DDP, DeepSpeed and FSDP support has been added to support Arctic Long Sequence Training. (ALST). See examples for using ALST with Axolotl!\n\n2025/06: Magistral with mistral-common tokenizer support has been added to Axolotl. See docs to start training your own Magistral models with Axolotl!\n2025/05: Quantization Aware Training (QAT) support has been added to Axolotl. Explore the docs to learn more!\n2025/04: Llama 4 support has been added in Axolotl. See docs to start training your own Llama 4 models with Axolotl’s linearized version!\n2025/03: Axolotl has implemented Sequence Parallelism (SP) support. Read the blog and docs to learn how to scale your context length when fine-tuning.\n2025/03: (Beta) Fine-tuning Multimodal models is now supported in Axolotl. Check out the docs to fine-tune your own!\n2025/02: Axolotl has added LoRA optimizations to reduce memory usage and improve training speed for LoRA and QLoRA in single GPU and multi-GPU training (DDP and DeepSpeed). Jump into the docs to give it a try.\n2025/02: Axolotl has added GRPO support. Dive into our blog and GRPO example and have some fun!\n2025/01: Axolotl has added Reward Modelling / Process Reward Modelling fine-tuning support. See docs.",
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