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.github/workflows/tests.yml vendored
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@@ -371,7 +371,6 @@ jobs:
directory: coverage-reports
fail_ci_if_error: false
verbose: true
carryforward: true
name: codecov-umbrella
override_commit: ${{ github.event.pull_request.head.sha || github.sha }}
override_pr: ${{ github.event.pull_request.number }}

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docs/config-reference.qmd
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---
title: Config Reference
description: A complete list of all configuration options.
---
```yaml
# Allow overwrite yml config using from cli
strict: bool | None = False
# Resume from a specific checkpoint dir
resume_from_checkpoint: str | None
# If resume_from_checkpoint isn't set and you simply want it to start where it left off.
# Be careful with this being turned on between different models.
auto_resume_from_checkpoints: bool | None
# Resize the model embeddings when new tokens are added to multiples of 32. This is
# reported to improve training speed on some models
resize_token_embeddings_to_32x: bool | None
mean_resizing_embeddings: bool | None = False
# Whether to shrink the embeddings to len(tokenizer). By default, we won't shrink.
shrink_embeddings: bool | None
# Don't upcast the embeddings to float32 when using PEFT. Useful for low-VRAM GPUs
embeddings_skip_upcast: bool | None
# Use RL training: 'dpo', 'ipo', 'kto', 'simpo', 'orpo', 'grpo'
rl: RLType | None
trl:
# Beta parameter for the RL training. Same as `rl_beta`. Use
beta: float | None
# Maximum length of the completion for RL training.
max_completion_length: int | None
# Whether to use VLLM for RL training.
use_vllm: bool = False
# Host of the vLLM server to connect to.
vllm_server_host: str | None = 0.0.0.0
# Port of the vLLM server to connect to.
vllm_server_port: int | None = 8000
# Total timeout (in seconds) to wait for the vLLM server to respond.
vllm_server_timeout: int | None
# Regex for vLLM guided decoding.
vllm_guided_decoding_regex: str | None
# List of reward functions to load. Paths must be importable from current dir.
reward_funcs: list[str] | None
# List of reward weights for the reward functions.
reward_weights: list[float] | None
# Number of generations to sample.
num_generations: int | None
# Whether to log completions.
log_completions: bool | None = False
# Number of completions to print when log_completions is True.
num_completions_to_print: int | None
# Whether to sync the reference model.
sync_ref_model: bool | None = False
# Mixup alpha for the reference model.
ref_model_mixup_alpha: float | None = 0.9
# Sync steps for the reference model.
ref_model_sync_steps: int | None = 64
# Whether to scale rewards by their standard deviation.
scale_rewards: bool = True
# Sampling temperature for the GRPO policy.
temperature: float | None
# Top-p sampling probability for the generation policy.
top_p: float | None
# Top-k sampling for the generation policy.
top_k: int | None
# Minimum probability for the generation policy.
min_p: float | None
# Penalty for tokens that appear in prompt and generated text.
repetition_penalty: float | None
# Number of iterations per batch (μ) for GRPO.
num_iterations: int | None
# Epsilon value for clipping in the GRPO algorithm.
epsilon: float | None
# Upper-bound epsilon value for clipping in the GRPO algorithm.
epsilon_high: float | None
# Whether to use Liger loss for GRPO.
use_liger_loss: bool | None
# Loss formulation to use. Supported values: grpo, bnpo, dr_grpo.
loss_type: str | None
# Whether to exclude truncated completions from loss calculation.
mask_truncated_completions: bool = False
vllm:
# Device to use for VLLM
device: str | None = auto
# Tensor parallel size for VLLM
tensor_parallel_size: int | None
# GPU memory utilization for VLLM
gpu_memory_utilization: float | None = 0.9
# Data type for VLLM
dtype: str | None = auto
# Maximum length of the model context for VLLM
max_model_len: int | None
# Enable prefix caching for VLLM
enable_prefix_caching: bool | None
# Host for the vLLM server to start on
host: str | None = 0.0.0.0
# Port of the vLLM server to start on
port: int | None = 8000
# Enable reasoning for VLLM
enable_reasoning: bool | None
# Reasoning parser for VLLM
reasoning_parser: str | None
qat:
# Fake quantization layout to use for activation quantization. Valid options are
# "int4" and "int8"
activation_dtype: TorchIntDType | None
# Fake quantization layout to use for weight quantization. Valid options are "int4"
# and "int8"
weight_dtype: TorchIntDType = TorchIntDType.int8
# Quantize embedding
quantize_embedding: bool | None = False
# The number of elements in each group for per-group fake quantization
group_size: int | None = 32
# The number of steps to apply fake quantization after
fake_quant_after_n_steps: int | None
quantization:
# Fake quantization layout to use for weight quantization. Valid options are uintX for
# X in [1, 2, 3, 4, 5, 6, 7], or int4, or int8
weight_dtype: TorchIntDType = TorchIntDType.int8
# Fake quantization layout to use for activation quantization. Valid options are
# "int4" and "int8"
activation_dtype: TorchIntDType | None
# Whether to quantize the embedding layer.
quantize_embedding: bool | None
# The number of elements in each group for per-group fake quantization
group_size: int | None = 32
# Reward modelling: `True` or `False`
reward_model: bool | None
# Process reward modelling: `True` or `False`
process_reward_model: bool | None
num_labels: int | None
# Whether to perform weighting in DPO trainer
dpo_use_weighting: bool | None
dpo_use_logits_to_keep: bool | None
dpo_label_smoothing: float | None
dpo_norm_loss: bool | None
dpo_padding_free: bool | None
# A list of one or more datasets to finetune the model with
datasets: Annotated[list[SFTDataset | DPODataset | KTODataset | StepwiseSupervisedDataset], MinLen(1)] | None
# A list of one or more datasets to eval the model with. You can use either
# test_datasets, or val_set_size, but not both.
test_datasets: Annotated[list[SFTDataset | DPODataset | KTODataset | StepwiseSupervisedDataset], MinLen(1)] | None
# If false, the datasets will not be shuffled and will keep their original order in
# `datasets`. The same applies to the `test_datasets` option and the
# `pretraining_dataset` option. Default is true.
shuffle_merged_datasets: bool | None = True
# Axolotl attempts to save the dataset as an arrow after packing the data together so
# subsequent training attempts load faster, relative path
dataset_prepared_path: str | None
# Num shards for whole dataset
dataset_shard_num: int | None
# Index of shard to use for whole dataset
dataset_shard_idx: int | None
skip_prepare_dataset: bool | None = False
# Set to HF dataset for type: 'completion' for streaming instead of pre-tokenize
pretraining_dataset: Annotated[list[PretrainingDataset | SFTDataset], MinLen(1)] | None
# The maximum number of processes to use while preprocessing your input dataset. This
# defaults to `os.cpu_count()` if not set.
dataset_processes: int | None = 14
# Deduplicates datasets and test_datasets with identical entries
dataset_exact_deduplication: bool | None
# Keep dataset in memory while preprocessing. Only needed if cached dataset is taking
# too much storage
dataset_keep_in_memory: bool | None
dataloader_pin_memory: bool | None
dataloader_num_workers: int | None
dataloader_prefetch_factor: int | None
dataloader_drop_last: bool | None
accelerator_config: dict[str, Any] | None
remove_unused_columns: bool | None
# Push prepared dataset to hub - repo_org/repo_name
push_dataset_to_hub: str | None
# Whether to use hf `use_auth_token` for loading datasets. Useful for fetching private
# datasets. Required to be true when used in combination with `push_dataset_to_hub`
hf_use_auth_token: bool | None
device: Any | None
# Passed through to transformers when loading the model when launched without
# accelerate. Use `sequential` when training w/ model parallelism to limit memory
device_map: Any | None
world_size: int | None
# Don't mess with this, it's here for accelerate and torchrun
local_rank: int | None
ddp: bool | None
# Seed for reproducibility
seed: int | None
# Advanced DDP Arguments - timeout
ddp_timeout: int | None
# Advanced DDP Arguments - bucket cap in MB
ddp_bucket_cap_mb: int | None
# Advanced DDP Arguments - broadcast buffers
ddp_broadcast_buffers: bool | None
ddp_find_unused_parameters: bool | None
# Approximate number of predictions sent to wandb depending on batch size. Enabled above
# 0. Default is 0
eval_table_size: int | None
# Total number of tokens generated for predictions sent to wandb. Default is 128
eval_max_new_tokens: int | None
# Whether to run causal language model evaluation for metrics in
# `eval_causal_lm_metrics`
do_causal_lm_eval: bool | None
# HF evaluate metrics used during evaluation. Default is ['sacrebleu', 'comet', 'ter',
# 'chrf', 'perplexity']
eval_causal_lm_metrics: list[str] | None
do_bench_eval: bool | None
bench_dataset: str | None
bench_split: str | None
metric_for_best_model: str | None
greater_is_better: bool | None
# High loss value, indicating the learning has broken down (a good estimate is ~2 times
# the loss at the start of training)
loss_watchdog_threshold: float | None
# Number of high-loss steps in a row before the trainer aborts (default: 3)
loss_watchdog_patience: int | None
gc_steps: int | None
# Use CUDA bf16. bool or 'full' for `bf16_full_eval`, or 'auto' for automatic detection.
# require >=ampere
bf16: Literal['auto'] | bool | None = auto
# Use CUDA fp16
fp16: bool | None
fp8: bool | None
# No AMP (automatic mixed precision) - require >=ampere
bfloat16: bool | None
# No AMP (automatic mixed precision)
float16: bool | None
# Use CUDA tf32 - require >=ampere
tf32: bool | None
float32: bool | None
# Whether to use gradient checkpointing. Available options are: true, false, 'offload',
# 'offload_disk'.
# https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing
gradient_checkpointing: Literal['offload', 'offload_disk'] | bool | None = False
# Additional kwargs to pass to the trainer for gradient checkpointing
gradient_checkpointing_kwargs: dict[str, Any] | None
unfrozen_parameters: list[str] | None
# The maximum length of an input to train with, this should typically be less than 2048
# as most models have a token/context limit of 2048
sequence_len: int = 512
min_sample_len: int | None
# maximum prompt length for RL training
max_prompt_len: int = 512
# Use efficient multi-packing with block diagonal attention and per sequence
# position_ids. Recommend set to 'true'
sample_packing: bool | None
# The number of samples packed at a time. Increasing the following values helps with
# packing, but usually only slightly (<%1.)
sample_packing_group_size: int | None = 100000
# The number of samples which can be packed into one sequence. Increase if using a large
# sequence_len with many short samples.
sample_packing_bin_size: int | None = 200
# Whether to pack samples sequentially
sample_packing_sequentially: bool | None
# Set to 'false' if getting errors during eval with sample_packing on
eval_sample_packing: bool | None
# Pad inputs so each step uses constant sized buffers. This will reduce memory
# fragmentation and may prevent OOMs, by re-using memory more efficiently
pad_to_sequence_len: bool | None
# Whether to use sequential sampling for curriculum learning
curriculum_sampling: bool | None
multipack_real_batches: bool | None
# whether to concatenate samples during pretraining
pretraining_sample_concatenation: bool | None
# Use batch flattening for speedups when not using sample_packing
batch_flattening: Literal['auto'] | bool | None
use_pose: bool | None
pose_split_on_token_ids: list[int] | None
pose_max_context_len: int | None
pose_num_chunks: int | None
pretrain_multipack_buffer_size: int | None = 10000
# whether to prevent cross attention for packed sequences during pretraining
pretrain_multipack_attn: bool | None = True
# Whether to use xformers attention patch https://github.com/facebookresearch/xformers
xformers_attention: bool | None
# Whether to use scaled-dot-product attention https://pytorch.org/docs/stable/generated/
# torch.nn.functional.scaled_dot_product_attention.html
sdp_attention: bool | None
# Shifted-sparse attention (only llama) - https://arxiv.org/pdf/2309.12307.pdf
s2_attention: bool | None
flex_attention: bool | None
flex_attn_compile_kwargs: dict[str, Any] | None
# Whether to use flash attention patch https://github.com/Dao-AILab/flash-attention
flash_attention: bool | None
# Whether to use flash-attention cross entropy implementation - advanced use only
flash_attn_cross_entropy: bool | None
# Whether to use flash-attention rms norm implementation - advanced use only
flash_attn_rms_norm: bool | None
# Whether to fuse QKV into a single operation
flash_attn_fuse_qkv: bool | None
# Whether to fuse part of the MLP into a single operation
flash_attn_fuse_mlp: bool | None
# Whether to use bettertransformers
flash_optimum: bool | None
eager_attention: bool | None
unsloth_cross_entropy_loss: bool | None
unsloth_lora_mlp: bool | None
unsloth_lora_qkv: bool | None
unsloth_lora_o: bool | None
unsloth_rms_norm: bool | None
unsloth_rope: bool | None
# Apply custom LoRA autograd functions and activation function Triton kernels for speed
# and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html
lora_mlp_kernel: bool | None
# Apply custom LoRA autograd functions and activation function Triton kernels for speed
# and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html
lora_qkv_kernel: bool | None
# Apply custom LoRA autograd functions and activation function Triton kernels for speed
# and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html
lora_o_kernel: bool | None
llama4_linearized_experts: bool | None
# Deepspeed config path. e.g., deepspeed_configs/zero3.json
deepspeed: str | dict[str, Any] | None
# FSDP configuration
fsdp: list[str] | None
# FSDP configuration options
fsdp_config: dict[str, Any] | None
fsdp_final_state_dict_type: Literal['FULL_STATE_DICT', 'LOCAL_STATE_DICT', 'SHARDED_STATE_DICT'] | None
# How much of the dataset to set aside as evaluation. 1 = 100%, 0.50 = 50%, etc. 0 for
# no eval.
val_set_size: float | None = 0.0
# Set to a divisor of the number of GPUs available to split sequences into chunks of
# equal size. Use in long context training to prevent OOM when sequences cannot fit into
# a single GPU's VRAM. E.g., if 4 GPUs are available, set this value to 2 to split each
# sequence into two equal-sized subsequences, or set to 4 to split into four equal-sized
# subsequences. See https://docs.axolotl.ai/docs/sequence_parallelism.html for more
# details.
sequence_parallel_degree: int | None
# Optional; strides across the key dimension. Larger values use more memory but should
# make training faster. Must evenly divide the number of KV heads in your model.
heads_k_stride: int | None
# One of 'varlen_llama3', 'batch_ring', 'batch_zigzag', 'batch_stripe'. Defaults to
# 'varlen_llama3' in the sample packing case, and 'batch_ring' in the non-sample packing
# case.
ring_attn_func: RingAttnFunc | None
# Add or change special tokens. If you add tokens here, you don't need to add them to
# the `tokens` list.
special_tokens:
bos_token: str | None
eos_token: str | None
pad_token: str | None
unk_token: str | None
additional_special_tokens: list[str] | None
# Add extra tokens to the tokenizer
tokens: list[str] | None
# Mapping token_id to new_token_string to override reserved added_tokens in the
# tokenizer. Only works for tokens that are not part of the base vocab (aka are
# added_tokens). Can be checked if they exist in tokenizer.json added_tokens.
added_tokens_overrides: dict[int, str] | None
# Whether to use torch.compile and which backend to use. setting to `auto` will enable
# torch compile when torch>=2.5.1
torch_compile: Literal['auto'] | bool | None
# Backend to use for torch.compile
torch_compile_backend: str | None
torch_compile_mode: Literal['default', 'reduce-overhead', 'max-autotune'] | None
# Maximum number of iterations to train for. It precedes num_epochs which means that if
# both are set, num_epochs will not be guaranteed. e.g., when 1 epoch is 1000 steps =>
# `num_epochs: 2` and `max_steps: 100` will train for 100 steps
max_steps: int | None
# Number of warmup steps. Cannot use with warmup_ratio
warmup_steps: int | None
# Warmup ratio. Cannot use with warmup_steps
warmup_ratio: float | None
# Leave empty to eval at each epoch, integer for every N steps. float for fraction of
# total steps
eval_steps: int | float | None
# Number of times per epoch to run evals, mutually exclusive with eval_steps
evals_per_epoch: int | None
# Set to `no` to skip evaluation, `epoch` at end of each epoch, leave empty to infer
# from `eval_steps`
eval_strategy: str | None
# Leave empty to save at each epoch, integer for every N steps. float for fraction of
# total steps
save_steps: int | float | None
# Number of times per epoch to save a checkpoint, mutually exclusive with save_steps
saves_per_epoch: int | None
# Set to `no` to skip checkpoint saves, `epoch` at end of each epoch, `best` when better
# result is achieved, leave empty to infer from `save_steps`
save_strategy: str | None
# Checkpoints saved at a time
save_total_limit: int | None
# Logging frequency
logging_steps: int | None
# Stop training after this many evaluation losses have increased in a row. https://huggi
# ngface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppin
# gCallback
early_stopping_patience: int | None
load_best_model_at_end: bool | None = False
# Save only the model weights, skipping the optimizer. Using this means you can't resume
# from checkpoints.
save_only_model: bool | None = False
# Use tensorboard for logging
use_tensorboard: bool | None
# Enable the pytorch profiler to capture the first N steps of training to the
# output_dir. see https://pytorch.org/blog/understanding-gpu-memory-1/ for more
# information. Snapshots can be visualized @ https://pytorch.org/memory_viz
profiler_steps: int | None
# bool of whether to include tokens trainer per second in the training metrics. This
# iterates over the entire dataset once, so it takes some time.
include_tokens_per_second: bool | None
# NEFT https://arxiv.org/abs/2310.05914, set this to a number (paper default is 5) to
# add noise to embeddings. Currently only supported on Llama and Mistral
neftune_noise_alpha: float | None
# Parameter controlling the relative ratio loss weight in the ORPO loss. Passed to
# `beta` in `ORPOConfig` due to trl mapping.
orpo_alpha: float | None
# Weighting of NLL term in loss from RPO paper
rpo_alpha: float | None
# Target reward margin for the SimPO loss
simpo_gamma: float | None
# Weight of the BC regularizer
cpo_alpha: float | None
# Factor for desirable loss term in KTO loss
kto_desirable_weight: float | None
# Factor for undesirable loss term in KTO loss
kto_undesirable_weight: float | None
# The beta parameter for the RL training
rl_beta: float | None
# Defines the max memory usage per gpu on the system. Passed through to transformers
# when loading the model.
max_memory: dict[int | Literal['cpu', 'disk'], int | str] | None
# Limit the memory for all available GPUs to this amount (if an integer, expressed in
# gigabytes); default: unset
gpu_memory_limit: int | str | None
# Whether to use low_cpu_mem_usage
low_cpu_mem_usage: bool | None
# The name of the chat template to use for training, following values are supported:
# tokenizer_default: Uses the chat template that is available in the
# tokenizer_config.json. If the chat template is not available in the tokenizer, it will
# raise an error. This is the default value.
# alpaca/inst/chatml/gemma/cohere/llama3/phi_3/deepseek_v2/jamba: These chat templates
# are available in the axolotl codebase at src/axolotl/utils/chat_templates.py.
# tokenizer_default_fallback_*: where * is the name of the chat template to fallback to.
# E.g. tokenizer_default_fallback_chatml. This is useful when the chat template is not
# available in the tokenizer. jinja: Uses a custom jinja template for the chat template.
# The custom jinja template should be provided in the chat_template_jinja field. The
# selected chat template will be saved to the tokenizer_config.json for easier
# inferencing
chat_template: ChatTemplate | Annotated[str, StringConstraints(pattern='^tokenizer_default_fallback_')] | None
# Custom jinja template for chat template. This will be only used if chat_template is
# set to `jinja` or `null` (in which case chat_template is automatically set to
# `jinja`). Default is null.
chat_template_jinja: str | None
# Custom EOT (End-of-Turn) tokens to mask/unmask during training. These tokens mark the
# boundaries between conversation turns. For example: ['/INST', '</s>',
# '[/SYSTEM_PROMPT]']. If not specified, defaults to just the model's eos_token. This is
# useful for templates that use multiple delimiter tokens.
eot_tokens: list[str] | None
# Changes the default system message. Currently only supports chatml.
default_system_message: str | None
fix_untrained_tokens: int | list[int] | None
is_preprocess: bool | None
preprocess_iterable: bool | None
# Total number of tokens - internal use
total_num_tokens: int | None
total_supervised_tokens: int | None
# You can set these packing optimizations AFTER starting a training at least once. The
# trainer will provide recommended values for these values.
sample_packing_eff_est: float | None
axolotl_config_path: str | None
# Internal use only - Used to identify which the model is based on
is_falcon_derived_model: bool | None
# Internal use only - Used to identify which the model is based on
is_llama_derived_model: bool | None
# Internal use only - Used to identify which the model is based on. Please note that if
# you set this to true, `padding_side` will be set to 'left' by default
is_mistral_derived_model: bool | None
# Internal use only - Used to identify which the model is based on
is_qwen_derived_model: bool | None
# Add plugins to extend the pipeline. See `src/axolotl/integrations` for the available
# plugins or doc below for more details.
# https://docs.axolotl.ai/docs/custom_integrations.html
plugins: list[str] | None
# This is the huggingface model that contains *.pt, *.safetensors, or *.bin files. This
# can also be a relative path to a model on disk
base_model: str (required)
# If the base_model repo on hf hub doesn't include configuration .json files, You can
# set that here, or leave this empty to default to base_model
base_model_config: str | None
cls_model_config: str | None
# Optional tokenizer configuration path in case you want to use a different tokenizer
# than the one defined in the base model
tokenizer_config: str | None
# use_fast option for tokenizer loading from_pretrained, default to True
tokenizer_use_fast: bool | None
# Whether to use the legacy tokenizer setting, defaults to True
tokenizer_legacy: bool | None
# Whether to use mistral-common tokenizer. If set to True, it will use the mistral-
# common tokenizer.
tokenizer_use_mistral_common: bool | None
# Corresponding tokenizer for the model AutoTokenizer is a good choice
tokenizer_type: str | None
# transformers processor class
processor_type: str | None
# Trust remote code for untrusted source
trust_remote_code: bool | None
# Where to save the full-finetuned model to
output_dir: str = ./model-out
# push checkpoints to hub
hub_model_id: str | None
# how to push checkpoints to hub
hub_strategy: str | None
# Save model as safetensors (require safetensors package). Default True
save_safetensors: bool | None = True
# This will attempt to quantize the model down to 8 bits and use adam 8 bit optimizer
load_in_8bit: bool | None = False
# Use bitsandbytes 4 bit
load_in_4bit: bool | None = False
# If you want to use 'lora' or 'qlora' or leave blank to train all parameters in
# original model
adapter: str | None
# If you already have a lora model trained that you want to load, put that here. This
# means after training, if you want to test the model, you should set this to the value
# of `output_dir`. Note that if you merge an adapter to the base model, a new
# subdirectory `merged` will be created under the `output_dir`.
lora_model_dir: str | None
lora_r: int | None
lora_alpha: int | None
lora_fan_in_fan_out: bool | None
lora_target_modules: str | list[str] | None
# If true, will target all linear modules
lora_target_linear: bool | None
# If you added new tokens to the tokenizer, you may need to save some LoRA modules
# because they need to know the new tokens. For LLaMA and Mistral, you need to save
# `embed_tokens` and `lm_head`. It may vary for other models. `embed_tokens` converts
# tokens to embeddings, and `lm_head` converts embeddings to token probabilities.
lora_modules_to_save: list[str] | None
lora_dropout: float | None = 0.0
# The layer indices to transform, otherwise, apply to all layers
peft_layers_to_transform: list[int] | None
peft_layers_pattern: list[str] | None
peft:
# Configuration options for loftq initialization for LoRA
loftq_config:
# typically 4 bits
loftq_bits: int = 4
# Whether to use DoRA.
peft_use_dora: bool | None
# Whether to use RSLoRA.
peft_use_rslora: bool | None
# List of layer indices to replicate.
peft_layer_replication: list[tuple[int, int]] | None
# How to initialize LoRA weights. Default to True which is MS original implementation.
peft_init_lora_weights: bool | str | None
# load qlora model in sharded format for FSDP using answer.ai technique.
qlora_sharded_model_loading: bool | None = False
# Do the LoRA/PEFT loading on CPU -- this is required if the base model is so large it
# takes up most or all of the available GPU VRAM, e.g. during a model and LoRA merge
lora_on_cpu: bool | None
# Whether you are training a 4-bit GPTQ quantized model
gptq: bool | None
# optional overrides to the bnb 4bit quantization configuration
bnb_config_kwargs: dict[str, Any] | None
# loraplus learning rate ratio lr_B / lr_A. Recommended value is 2^4.
loraplus_lr_ratio: float | None
# loraplus learning rate for lora embedding layers. Default value is 1e-6.
loraplus_lr_embedding: float | None = 1e-06
merge_lora: bool | None
# Number of steps per ReLoRA restart
relora_steps: int | None
# Number of per-restart warmup steps
relora_warmup_steps: int | None
# Number of anneal steps for each relora cycle
relora_anneal_steps: int | None
# threshold for optimizer magnitude when pruning
relora_prune_ratio: float | None
# True to perform lora weight merges on cpu during restarts, for modest gpu memory
# savings
relora_cpu_offload: bool | None
# If greater than 1, backpropagation will be skipped and the gradients will be
# accumulated for the given number of steps.
gradient_accumulation_steps: int | None = 1
# The number of samples to include in each batch. This is the number of samples sent to
# each GPU. Batch size per gpu = micro_batch_size * gradient_accumulation_steps
micro_batch_size: int | None = 1
# Total batch size, we do not recommended setting this manually
batch_size: int | None
# per gpu micro batch size for evals, defaults to value of micro_batch_size
eval_batch_size: int | None
# whether to find batch size that fits in memory. Passed to underlying transformers
# Trainer
auto_find_batch_size: bool | None
# Whether to mask out or include the human's prompt from the training labels
train_on_inputs: bool | None = False
# Group similarly sized data to minimize padding. May be slower to start, as it must
# download and sort the entire dataset. Note that training loss may have an oscillating
# pattern with this enabled.
group_by_length: bool | None
learning_rate: str | float (required)
embedding_lr: float | None
embedding_lr_scale: float | None
# Specify weight decay
weight_decay: float | None = 0.0
# Specify optimizer
optimizer: OptimizerNames | CustomSupportedOptimizers | None = OptimizerNames.ADAMW_TORCH_FUSED
# Dictionary of arguments to pass to the optimizer
optim_args: str | dict[str, Any] | None
# The target modules to optimize, i.e. the module names that you would like to train,
# right now this is used only for GaLore algorithm
optim_target_modules: list[str] | Literal['all_linear'] | None
# Path to torch distx for optim 'adamw_anyprecision'
torchdistx_path: str | None
lr_scheduler: SchedulerType | Literal['one_cycle'] | Literal['rex'] | None = SchedulerType.COSINE
# Specify a scheduler and kwargs to use with the optimizer
lr_scheduler_kwargs: dict[str, Any] | None
lr_quadratic_warmup: bool | None
# decay lr to some percentage of the peak lr, e.g. cosine_min_lr_ratio=0.1 for 10% of
# peak lr
cosine_min_lr_ratio: float | None
# freeze lr at some percentage of the step, e.g. cosine_constant_lr_ratio=0.8 means
# start cosine_min_lr at 80% of training step
cosine_constant_lr_ratio: float | None
# Learning rate div factor
lr_div_factor: float | None
lr_groups: list[LrGroup] | None
# adamw hyperparams
adam_epsilon: float | None
# only used for CAME Optimizer
adam_epsilon2: float | None
# adamw hyperparams
adam_beta1: float | None
# adamw hyperparams
adam_beta2: float | None
# only used for CAME Optimizer
adam_beta3: float | None
# Gradient clipping max norm
max_grad_norm: float | None
num_epochs: float = 1.0
use_wandb: bool | None
# Set the name of your wandb run
wandb_name: str | None
# Set the ID of your wandb run
wandb_run_id: str | None
# "offline" to save run metadata locally and not sync to the server, "disabled" to turn
# off wandb
wandb_mode: str | None
# Your wandb project name
wandb_project: str | None
# A wandb Team name if using a Team
wandb_entity: str | None
wandb_watch: str | None
# "checkpoint" to log model to wandb Artifacts every `save_steps` or "end" to log only
# at the end of training
wandb_log_model: str | None
use_mlflow: bool | None
# URI to mlflow
mlflow_tracking_uri: str | None
# Your experiment name
mlflow_experiment_name: str | None
# Your run name
mlflow_run_name: str | None
# set to true to copy each saved checkpoint on each save to mlflow artifact registry
hf_mlflow_log_artifacts: bool | None
# Enable or disable Comet integration.
use_comet: bool | None
# API key for Comet. Recommended to set via `comet login`.
comet_api_key: str | None
# Workspace name in Comet. Defaults to the user's default workspace.
comet_workspace: str | None
# Project name in Comet. Defaults to Uncategorized.
comet_project_name: str | None
# Identifier for the experiment. Used to append data to an existing experiment or
# control the key of new experiments. Default to a random key.
comet_experiment_key: str | None
# Create a new experiment ("create") or log to an existing one ("get"). Default
# ("get_or_create") auto-selects based on configuration.
comet_mode: str | None
# Set to True to log data to Comet server, or False for offline storage. Default is
# True.
comet_online: bool | None
# Dictionary for additional configuration settings, see the doc for more details.
comet_experiment_config: dict[str, Any] | None
# the number of activate layers in LISA
lisa_n_layers: int | None
# how often to switch layers in LISA
lisa_step_interval: int | None
# path under the model to access the layers
lisa_layers_attribute: str | None = model.layers
gradio_title: str | None
gradio_share: bool | None
gradio_server_name: str | None
gradio_server_port: int | None
gradio_max_new_tokens: int | None
gradio_temperature: float | None
use_ray: bool = False
ray_run_name: str | None
ray_num_workers: int = 1
resources_per_worker: dict
# The size of the image to resize to. It can be an integer (resized into padded-square
# image) or a tuple (width, height).If not provided, we will attempt to load from
# preprocessor.size, otherwise, images won't be resized.
image_size: int | tuple[int, int] | None
# The resampling algorithm to use for image resizing. Default is bilinear. Please refer
# to PIL.Image.Resampling for more details.
image_resize_algorithm: Literal['bilinear', 'bicubic', 'lanczos'] | Resampling | None
# optional overrides to the base model configuration
overrides_of_model_config: dict[str, Any] | None
# optional overrides the base model loading from_pretrained
overrides_of_model_kwargs: dict[str, Any] | None
# If you want to specify the type of model to load, AutoModelForCausalLM is a good
# choice too
type_of_model: str | None
# You can specify to choose a specific model revision from huggingface hub
revision_of_model: str | None
max_packed_sequence_len: int | None
rope_scaling: Any | None
noisy_embedding_alpha: float | None
dpo_beta: float | None
evaluation_strategy: str | None
```