# Axolotl - Api **Pages:** 150 --- ## cli.cloud.modal_ **URL:** https://docs.axolotl.ai/docs/api/cli.cloud.modal_.html **Contents:** - cli.cloud.modal_ - Classes - ModalCloud - Functions - run_cmd Modal Cloud support from CLI Modal Cloud implementation. Run a command inside a folder, with Modal Volume reloading before and commit on success. **Examples:** Example 1 (python): ```python cli.cloud.modal_.ModalCloud(config, app=None) ``` Example 2 (python): ```python cli.cloud.modal_.run_cmd(cmd, run_folder, volumes=None) ``` --- ## core.trainers.base **URL:** https://docs.axolotl.ai/docs/api/core.trainers.base.html **Contents:** - core.trainers.base - Classes - AxolotlTrainer - Methods - log - Parameters - push_to_hub - store_metrics - Parameters Module for customized trainers Extend the base Trainer for axolotl helpers Log logs on the various objects watching training, including stored metrics. Overwrite the push_to_hub method in order to force-add the tags when pushing the model on the Hub. Please refer to ~transformers.Trainer.push_to_hub for more details. Store metrics with specified reduction type. **Examples:** Example 1 (python): ```python core.trainers.base.AxolotlTrainer( *_args, bench_data_collator=None, eval_data_collator=None, dataset_tags=None, **kwargs, ) ``` Example 2 (python): ```python core.trainers.base.AxolotlTrainer.log(logs, start_time=None) ``` Example 3 (python): ```python core.trainers.base.AxolotlTrainer.push_to_hub(*args, **kwargs) ``` Example 4 (python): ```python core.trainers.base.AxolotlTrainer.store_metrics( metrics, train_eval='train', reduction='mean', ) ``` --- ## prompt_strategies.input_output **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.input_output.html **Contents:** - prompt_strategies.input_output - Classes - RawInputOutputPrompter - RawInputOutputStrategy prompt_strategies.input_output Module for plain input/output prompt pairs prompter for raw i/o data Prompt Strategy class for input/output pairs **Examples:** Example 1 (python): ```python prompt_strategies.input_output.RawInputOutputPrompter() ``` Example 2 (python): ```python prompt_strategies.input_output.RawInputOutputStrategy( *args, eos_token=None, **kwargs, ) ``` --- ## prompt_strategies.completion **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.completion.html **Contents:** - prompt_strategies.completion - Classes - CompletionPromptTokenizingStrategy - CompletionPrompter prompt_strategies.completion Basic completion text Tokenizing strategy for Completion prompts. Prompter for completion **Examples:** Example 1 (python): ```python prompt_strategies.completion.CompletionPromptTokenizingStrategy( *args, max_length=None, **kwargs, ) ``` Example 2 (python): ```python prompt_strategies.completion.CompletionPrompter() ``` --- ## utils.collators.core **URL:** https://docs.axolotl.ai/docs/api/utils.collators.core.html **Contents:** - utils.collators.core basic shared collator constants --- ## monkeypatch.data.batch_dataset_fetcher **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.data.batch_dataset_fetcher.html **Contents:** - monkeypatch.data.batch_dataset_fetcher - Functions - apply_multipack_dataloader_patch - patch_fetchers - patched_worker_loop - remove_multipack_dataloader_patch monkeypatch.data.batch_dataset_fetcher Monkey patches for the dataset fetcher to handle batches of packed indexes. This patch allows DataLoader to correctly process batches that contain multiple bins of packed sequences. Apply patches to PyTorch’s DataLoader components. Worker loop that ensures patches are applied in worker processes. Remove the monkeypatch and restore original PyTorch DataLoader behavior. **Examples:** Example 1 (python): ```python monkeypatch.data.batch_dataset_fetcher.apply_multipack_dataloader_patch() ``` Example 2 (python): ```python monkeypatch.data.batch_dataset_fetcher.patch_fetchers() ``` Example 3 (python): ```python monkeypatch.data.batch_dataset_fetcher.patched_worker_loop(*args, **kwargs) ``` Example 4 (python): ```python monkeypatch.data.batch_dataset_fetcher.remove_multipack_dataloader_patch() ``` --- ## core.datasets.chat **URL:** https://docs.axolotl.ai/docs/api/core.datasets.chat.html **Contents:** - core.datasets.chat - Classes - TokenizedChatDataset Tokenized chat dataset **Examples:** Example 1 (python): ```python core.datasets.chat.TokenizedChatDataset( data, model_transform, *args, message_transform=None, formatter=None, process_count=None, keep_in_memory=False, **kwargs, ) ``` --- ## utils.freeze **URL:** https://docs.axolotl.ai/docs/api/utils.freeze.html **Contents:** - utils.freeze - Classes - LayerNamePattern - Methods - match - Functions - freeze_layers_except module to freeze/unfreeze parameters by name Represents a regex pattern for layer names, potentially including a parameter index range. Checks if the given layer name matches the regex pattern. Parameters: - name (str): The layer name to check. Returns: - bool: True if the layer name matches the pattern, False otherwise. Freezes all layers of the given model except for the layers that match given regex patterns. Periods in the patterns are treated as literal periods, not as wildcard characters. Parameters: - model (nn.Module): The PyTorch model to be modified. - regex_patterns (list of str): List of regex patterns to match layer names to keep unfrozen. Note that you cannot use a dot as a wildcard character in the patterns since it is reserved for separating layer names. Also, to match the entire layer name, the pattern should start with “^” and end with “\(", otherwise it will match any part of the layer name. The range pattern part is optional and it is not compiled as a regex pattern which means you must put "\)” before the range pattern if you want to match the entire layer name. E.g., [“^model.embed_tokens.weight\([:32000]", "layers.2[0-9]+.block_sparse_moe.gate.[a-z]+\)”] Returns: None; the model is modified in place. **Examples:** Example 1 (python): ```python utils.freeze.LayerNamePattern(pattern) ``` Example 2 (python): ```python utils.freeze.LayerNamePattern.match(name) ``` Example 3 (python): ```python utils.freeze.freeze_layers_except(model, regex_patterns) ``` --- ## monkeypatch.unsloth_ **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.unsloth_.html **Contents:** - monkeypatch.unsloth_ module for patching with unsloth optimizations --- ## utils.schemas.datasets **URL:** https://docs.axolotl.ai/docs/api/utils.schemas.datasets.html **Contents:** - utils.schemas.datasets - Classes - DPODataset - KTODataset - PretrainingDataset - SFTDataset - Methods - handle_legacy_message_fields - StepwiseSupervisedDataset - UserDefinedDPOType utils.schemas.datasets Pydantic models for datasets-related configuration DPO configuration subset KTO configuration subset Pretraining dataset configuration subset SFT configuration subset Handle backwards compatibility between legacy message field mapping and new property mapping system. Stepwise supervised dataset configuration subset User defined typing for DPO User defined typing for KTO Structure for user defined prompt types **Examples:** Example 1 (python): ```python utils.schemas.datasets.DPODataset() ``` Example 2 (python): ```python utils.schemas.datasets.KTODataset() ``` Example 3 (python): ```python utils.schemas.datasets.PretrainingDataset() ``` Example 4 (python): ```python utils.schemas.datasets.SFTDataset() ``` --- ## core.chat.format.llama3x **URL:** https://docs.axolotl.ai/docs/api/core.chat.format.llama3x.html **Contents:** - core.chat.format.llama3x core.chat.format.llama3x Llama 3.x chat formatting functions for MessageContents --- ## datasets **URL:** https://docs.axolotl.ai/docs/api/datasets.html **Contents:** - datasets - Classes - TokenizedPromptDataset - Parameters Module containing dataset functionality. We want this to be a wrapper for an existing dataset that we have loaded. Lets use the concept of middlewares to wrap each dataset. We’ll use the collators later on to pad the datasets. Dataset that returns tokenized prompts from a stream of text files. **Examples:** Example 1 (python): ```python datasets.TokenizedPromptDataset( prompt_tokenizer, dataset, process_count=None, keep_in_memory=False, **kwargs, ) ``` --- ## prompt_strategies.bradley_terry.llama3 **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.bradley_terry.llama3.html **Contents:** - prompt_strategies.bradley_terry.llama3 - Functions - icr prompt_strategies.bradley_terry.llama3 chatml transforms for datasets with system, input, chosen, rejected to match llama3 chat template chatml transforms for datasets with system, input, chosen, rejected ex. https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs **Examples:** Example 1 (python): ```python prompt_strategies.bradley_terry.llama3.icr(cfg, **kwargs) ``` --- ## common.datasets **URL:** https://docs.axolotl.ai/docs/api/common.datasets.html **Contents:** - common.datasets - Classes - TrainDatasetMeta - Functions - load_datasets - Parameters - Returns - load_preference_datasets - Parameters - Returns Dataset loading utilities. Dataclass with fields for training and validation datasets and metadata. Loads one or more training or evaluation datasets, calling axolotl.utils.data.prepare_datasets. Optionally, logs out debug information. Loads one or more training or evaluation datasets for RL training using paired preference data, calling axolotl.utils.data.rl.prepare_preference_datasets. Optionally, logs out debug information. Randomly sample num_samples samples with replacement from dataset. **Examples:** Example 1 (python): ```python common.datasets.TrainDatasetMeta( train_dataset, eval_dataset=None, total_num_steps=None, ) ``` Example 2 (python): ```python common.datasets.load_datasets(cfg, cli_args=None, debug=False) ``` Example 3 (python): ```python common.datasets.load_preference_datasets(cfg, cli_args=None) ``` Example 4 (python): ```python common.datasets.sample_dataset(dataset, num_samples) ``` --- ## cli.train **URL:** https://docs.axolotl.ai/docs/api/cli.train.html **Contents:** - cli.train - Functions - do_cli - Parameters - do_train - Parameters CLI to run training on a model. Parses axolotl config, CLI args, and calls do_train. Trains a transformers model by first loading the dataset(s) specified in the axolotl config, and then calling axolotl.train.train. Also runs the plugin manager’s post_train_unload once training completes. **Examples:** Example 1 (python): ```python cli.train.do_cli(config=Path('examples/'), **kwargs) ``` Example 2 (python): ```python cli.train.do_train(cfg, cli_args) ``` --- ## cli.utils.fetch **URL:** https://docs.axolotl.ai/docs/api/cli.utils.fetch.html **Contents:** - cli.utils.fetch - Functions - fetch_from_github - Parameters Utilities for axolotl fetch CLI command. Sync files from a specific directory in the GitHub repository. Only downloads files that don’t exist locally or have changed. **Examples:** Example 1 (python): ```python cli.utils.fetch.fetch_from_github(dir_prefix, dest_dir=None, max_workers=5) ``` --- ## utils.tokenization **URL:** https://docs.axolotl.ai/docs/api/utils.tokenization.html **Contents:** - utils.tokenization - Functions - color_token_for_rl_debug - process_tokens_for_rl_debug Module for tokenization utilities Helper function to color tokens based on their type. Helper function to process and color tokens. **Examples:** Example 1 (python): ```python utils.tokenization.color_token_for_rl_debug( decoded_token, encoded_token, color, text_only, ) ``` Example 2 (python): ```python utils.tokenization.process_tokens_for_rl_debug( tokens, color, tokenizer, text_only, ) ``` --- ## core.trainers.grpo.sampler **URL:** https://docs.axolotl.ai/docs/api/core.trainers.grpo.sampler.html **Contents:** - core.trainers.grpo.sampler - Classes - SequenceParallelRepeatRandomSampler - Parameters - Methods - set_epoch - Parameters core.trainers.grpo.sampler Repeat random sampler (similar to the one implemented in https://github.com/huggingface/trl/blob/main/trl/trainer/grpo_trainer.py) that adds sequence parallelism functionality; i.e., duplicating data across ranks in the same sequence parallel group. Sampler for GRPO training with sequence parallelism. This sampler ensures: - Ranks in the same sequence parallel (SP) group receive identical data. - Each index is repeated multiple times for sampling different completions. - Entire batches are repeated for reuse in multiple updates. - Data is properly distributed across SP groups. In the table below, the values represent dataset indices. Each SP group has context_parallel_size = 2 GPUs working together on the same data. There are 2 SP groups (SP0 and SP1), with world_size = 4 total GPUs. grad_accum=2 ▲ ▲ 0 0 [0 0 0 1 1 1] [2 2 2 3 3 3] <- SP groups get different data ▼ | 0 1 [0 0 0 1 1 1] [2 2 2 3 3 3] <- Same data for each SP group GPU | | 1 2 [0 0 0 1 1 1] [2 2 2 3 3 3] <- Repeat same indices for iterations num_iterations=2 ▼ 1 3 [0 0 0 1 1 1] [2 2 2 3 3 3] <- When using gradient accumulation Sets the epoch for this sampler. **Examples:** Example 1 (python): ```python core.trainers.grpo.sampler.SequenceParallelRepeatRandomSampler( dataset, mini_repeat_count, world_size, rank, batch_size=1, repeat_count=1, context_parallel_size=1, shuffle=True, seed=0, drop_last=False, ) ``` Example 2 (unknown): ```unknown Sequence Parallel Groups | SP0 | SP1 | | GPU 0 | GPU 1 | GPU 2 | GPU 3 | global_step step <---> mini_repeat_count=3 <----------> batch_size=2 per SP group ``` Example 3 (unknown): ```unknown 2 4 [4 4 4 5 5 5] [6 6 6 7 7 7] <- New batch of data indices 2 5 [4 4 4 5 5 5] [6 6 6 7 7 7] ... ``` Example 4 (python): ```python core.trainers.grpo.sampler.SequenceParallelRepeatRandomSampler.set_epoch(epoch) ``` --- ## evaluate **URL:** https://docs.axolotl.ai/docs/api/evaluate.html **Contents:** - evaluate - Functions - evaluate - Parameters - Returns - evaluate_dataset - Parameters - Returns Module for evaluating models. Evaluate a model on training and validation datasets. Helper function to evaluate a single dataset. **Examples:** Example 1 (python): ```python evaluate.evaluate(cfg, dataset_meta) ``` Example 2 (python): ```python evaluate.evaluate_dataset(trainer, dataset, dataset_type, flash_optimum=False) ``` --- ## utils.optimizers.adopt **URL:** https://docs.axolotl.ai/docs/api/utils.optimizers.adopt.html **Contents:** - utils.optimizers.adopt - Functions - adopt utils.optimizers.adopt Copied from https://github.com/iShohei220/adopt ADOPT: Modified Adam Can Converge with Any β2 with the Optimal Rate (2024) Taniguchi, Shohei and Harada, Keno and Minegishi, Gouki and Oshima, Yuta and Jeong, Seong Cheol and Nagahara, Go and Iiyama, Tomoshi and Suzuki, Masahiro and Iwasawa, Yusuke and Matsuo, Yutaka Functional API that performs ADOPT algorithm computation. **Examples:** Example 1 (python): ```python utils.optimizers.adopt.adopt( params, grads, exp_avgs, exp_avg_sqs, state_steps, foreach=None, capturable=False, differentiable=False, fused=None, grad_scale=None, found_inf=None, has_complex=False, *, beta1, beta2, lr, clip_lambda, weight_decay, decouple, eps, maximize, ) ``` --- ## prompt_tokenizers **URL:** https://docs.axolotl.ai/docs/api/prompt_tokenizers.html **Contents:** - prompt_tokenizers - Classes - AlpacaMultipleChoicePromptTokenizingStrategy - AlpacaPromptTokenizingStrategy - AlpacaReflectionPTStrategy - DatasetWrappingStrategy - GPTeacherPromptTokenizingStrategy - InstructionPromptTokenizingStrategy - InvalidDataException - JeopardyPromptTokenizingStrategy Module containing PromptTokenizingStrategy and Prompter classes Tokenizing strategy for Alpaca Multiple Choice prompts. Tokenizing strategy for Alpaca prompts. Tokenizing strategy for Alpaca Reflection prompts. Abstract class for wrapping datasets for Chat Messages Tokenizing strategy for GPTeacher prompts. Tokenizing strategy for instruction-based prompts. Exception raised when the data is invalid Tokenizing strategy for Jeopardy prompts. Tokenizing strategy for NomicGPT4All prompts. Tokenizing strategy for OpenAssistant prompts. Abstract class for tokenizing strategies Tokenizing strategy for Reflection prompts. Tokenizing strategy for SummarizeTLDR prompts. Parses the tokenized prompt and append the tokenized input_ids, attention_mask and labels to the result Returns the default values for the tokenize prompt function **Examples:** Example 1 (python): ```python prompt_tokenizers.AlpacaMultipleChoicePromptTokenizingStrategy( prompter, tokenizer, train_on_inputs=False, sequence_len=2048, ) ``` Example 2 (python): ```python prompt_tokenizers.AlpacaPromptTokenizingStrategy( prompter, tokenizer, train_on_inputs=False, sequence_len=2048, ) ``` Example 3 (python): ```python prompt_tokenizers.AlpacaReflectionPTStrategy( prompter, tokenizer, train_on_inputs=False, sequence_len=2048, ) ``` Example 4 (python): ```python prompt_tokenizers.DatasetWrappingStrategy() ``` --- ## cli.art **URL:** https://docs.axolotl.ai/docs/api/cli.art.html **Contents:** - cli.art - Functions - print_axolotl_text_art Axolotl ASCII logo utils. Prints axolotl ASCII art. **Examples:** Example 1 (python): ```python cli.art.print_axolotl_text_art() ``` --- ## utils.callbacks.perplexity **URL:** https://docs.axolotl.ai/docs/api/utils.callbacks.perplexity.html **Contents:** - utils.callbacks.perplexity - Classes - Perplexity - Methods - compute utils.callbacks.perplexity callback to calculate perplexity as an evaluation metric. Calculate perplexity as defined in https://huggingface.co/docs/transformers/en/perplexity. This is a custom variant that doesn’t re-tokenize the input or re-load the model. Compute perplexity in a fixed length sliding window across the sequence. **Examples:** Example 1 (python): ```python utils.callbacks.perplexity.Perplexity(tokenizer, max_seq_len, stride=512) ``` Example 2 (python): ```python utils.callbacks.perplexity.Perplexity.compute(model, references=None) ``` --- ## cli.utils.train **URL:** https://docs.axolotl.ai/docs/api/cli.utils.train.html **Contents:** - cli.utils.train - Functions - build_command - Parameters - Returns - generate_config_files - Parameters - launch_training Utilities for axolotl train CLI command. Build command list from base command and options. Generate list of configuration files to process. Yields a tuple of the configuration file name and a boolean indicating whether this is a group of configurations (i.e., a sweep). Execute training with the given configuration. **Examples:** Example 1 (python): ```python cli.utils.train.build_command(base_cmd, options) ``` Example 2 (python): ```python cli.utils.train.generate_config_files(config, sweep) ``` Example 3 (python): ```python cli.utils.train.launch_training( cfg_file, launcher, cloud, kwargs, launcher_args=None, use_exec=False, ) ``` --- ## cli.vllm_serve **URL:** https://docs.axolotl.ai/docs/api/cli.vllm_serve.html **Contents:** - cli.vllm_serve - Classes - AxolotlScriptArguments - Functions - do_vllm_serve - Returns CLI to start the vllm server for online RL Additional arguments for the VLLM server Starts the VLLM server for serving LLM models used for online RL Args :param cfg: Parsed doct of the YAML config :param cli_args: dict of additional command-line arguments of type VllmServeCliArgs **Examples:** Example 1 (python): ```python cli.vllm_serve.AxolotlScriptArguments( reasoning_parser='', enable_reasoning=None, ) ``` Example 2 (python): ```python cli.vllm_serve.do_vllm_serve(config, cli_args) ``` --- ## convert **URL:** https://docs.axolotl.ai/docs/api/convert.html **Contents:** - convert - Classes - FileReader - FileWriter - JsonParser - JsonToJsonlConverter - JsonlSerializer - StdoutWriter Module containing File Reader, File Writer, Json Parser, and Jsonl Serializer classes Reads a file and returns its contents as a string Writes a string to a file Parses a string as JSON and returns the result Converts a JSON file to JSONL Serializes a list of JSON objects into a JSONL string Writes a string to stdout **Examples:** Example 1 (python): ```python convert.FileReader() ``` Example 2 (python): ```python convert.FileWriter(file_path) ``` Example 3 (python): ```python convert.JsonParser() ``` Example 4 (python): ```python convert.JsonToJsonlConverter( file_reader, file_writer, json_parser, jsonl_serializer, ) ``` --- ## monkeypatch.utils **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.utils.html **Contents:** - monkeypatch.utils - Functions - get_cu_seqlens - get_cu_seqlens_from_pos_ids - mask_2d_to_4d Shared utils for the monkeypatches generate a cumulative sequence length mask for flash attention using attn mask generate a cumulative sequence length mask for flash attention using pos ids Expands attention_mask from [bsz, seq_len] to [bsz, 1, tgt_seq_len, src_seq_len]. This expansion handles packed sequences so that sequences share the same attention mask integer value when they attend to each other within that sequence. This expansion transforms the mask to lower triangular form to prevent future peeking. **Examples:** Example 1 (python): ```python monkeypatch.utils.get_cu_seqlens(attn_mask) ``` Example 2 (python): ```python monkeypatch.utils.get_cu_seqlens_from_pos_ids(position_ids) ``` Example 3 (python): ```python monkeypatch.utils.mask_2d_to_4d(mask, dtype, tgt_len=None) ``` --- ## prompt_strategies.pygmalion **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.pygmalion.html **Contents:** - prompt_strategies.pygmalion - Classes - PygmalionPromptTokenizingStrategy - PygmalionPrompter prompt_strategies.pygmalion Module containing the PygmalionPromptTokenizingStrategy and PygmalionPrompter class Tokenizing strategy for Pygmalion. Prompter for Pygmalion. **Examples:** Example 1 (python): ```python prompt_strategies.pygmalion.PygmalionPromptTokenizingStrategy( prompter, tokenizer, *args, **kwargs, ) ``` Example 2 (python): ```python prompt_strategies.pygmalion.PygmalionPrompter(*args, **kwargs) ``` --- ## utils.callbacks.mlflow_ **URL:** https://docs.axolotl.ai/docs/api/utils.callbacks.mlflow_.html **Contents:** - utils.callbacks.mlflow_ - Classes - SaveAxolotlConfigtoMlflowCallback utils.callbacks.mlflow_ MLFlow module for trainer callbacks Callback to save axolotl config to mlflow **Examples:** Example 1 (python): ```python utils.callbacks.mlflow_.SaveAxolotlConfigtoMlflowCallback(axolotl_config_path) ``` --- ## loaders.adapter **URL:** https://docs.axolotl.ai/docs/api/loaders.adapter.html **Contents:** - loaders.adapter - Functions - setup_quantized_meta_for_peft - setup_quantized_peft_meta_for_training Adapter loading functionality, including LoRA / QLoRA and associated utils Replaces quant_state.to with a dummy function to prevent PEFT from moving quant_state to meta device Replaces dummy quant_state.to method with the original function to allow training to continue **Examples:** Example 1 (python): ```python loaders.adapter.setup_quantized_meta_for_peft(model) ``` Example 2 (python): ```python loaders.adapter.setup_quantized_peft_meta_for_training(model) ``` --- ## cli.cloud.base **URL:** https://docs.axolotl.ai/docs/api/cli.cloud.base.html **Contents:** - cli.cloud.base - Classes - Cloud base class for cloud platforms from cli Abstract base class for cloud platforms. **Examples:** Example 1 (python): ```python cli.cloud.base.Cloud() ``` --- ## monkeypatch.llama_attn_hijack_flash **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.llama_attn_hijack_flash.html **Contents:** - monkeypatch.llama_attn_hijack_flash - Functions - flashattn_forward_with_s2attn monkeypatch.llama_attn_hijack_flash Flash attention monkey patch for llama model Input shape: Batch x Time x Channel From: https://github.com/dvlab-research/LongLoRA/blob/main/llama_attn_replace.py attention_mask: [bsz, q_len] cu_seqlens will be ignored if provided max_seqlen will be ignored if provided **Examples:** Example 1 (python): ```python monkeypatch.llama_attn_hijack_flash.flashattn_forward_with_s2attn( self, hidden_states, attention_mask=None, position_ids=None, past_key_value=None, output_attentions=False, use_cache=False, padding_mask=None, cu_seqlens=None, max_seqlen=None, ) ``` --- ## monkeypatch.llama_patch_multipack **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.llama_patch_multipack.html **Contents:** - monkeypatch.llama_patch_multipack monkeypatch.llama_patch_multipack Patched LlamaAttention to use torch.nn.functional.scaled_dot_product_attention --- ## cli.inference **URL:** https://docs.axolotl.ai/docs/api/cli.inference.html **Contents:** - cli.inference - Functions - do_cli - Parameters - do_inference - Parameters - do_inference_gradio - Parameters - get_multi_line_input - Returns CLI to run inference on a trained model. Parses axolotl config, CLI args, and calls do_inference or do_inference_gradio. Runs inference on the command line in a loop. User input is accepted, a chat template is (optionally) applied, and the model specified in the axolotl config is used to generate completions according to a default generation config. Runs inference in a Gradio interface. User input is accepted, a chat template is (optionally) applied, and the model specified in the axolotl config is used to generate completions according to a default generation config. Gets multi-line input from terminal. **Examples:** Example 1 (python): ```python cli.inference.do_cli(config=Path('examples/'), gradio=False, **kwargs) ``` Example 2 (python): ```python cli.inference.do_inference(cfg, cli_args) ``` Example 3 (python): ```python cli.inference.do_inference_gradio(cfg, cli_args) ``` Example 4 (python): ```python cli.inference.get_multi_line_input() ``` --- ## loaders.tokenizer **URL:** https://docs.axolotl.ai/docs/api/loaders.tokenizer.html **Contents:** - loaders.tokenizer - Functions - load_tokenizer - modify_tokenizer_files - Parameters - Returns Tokenizer loading functionality and associated utils Load and configure the tokenizer based on the provided config. Modify tokenizer files to replace added_tokens strings, save to output directory, and return the path to the modified tokenizer. This only works with reserved tokens that were added to the tokenizer, not tokens already part of the vocab. Ref: https://github.com/huggingface/transformers/issues/27974#issuecomment-1854188941 **Examples:** Example 1 (python): ```python loaders.tokenizer.load_tokenizer(cfg) ``` Example 2 (python): ```python loaders.tokenizer.modify_tokenizer_files( tokenizer_path, token_mappings, output_dir, ) ``` --- ## cli.utils.sweeps **URL:** https://docs.axolotl.ai/docs/api/cli.utils.sweeps.html **Contents:** - cli.utils.sweeps - Functions - generate_sweep_configs - Parameters - Returns - Example Utilities for handling sweeps over configs for axolotl train CLI command Recursively generates all possible configurations by applying sweeps to the base config. sweeps_config = { ‘learning_rate’: [0.1, 0.01], ’_’: [ {‘load_in_8bit’: True, ‘adapter’: ‘lora’}, {‘load_in_4bit’: True, ‘adapter’: ‘qlora’} ] } **Examples:** Example 1 (python): ```python cli.utils.sweeps.generate_sweep_configs(base_config, sweeps_config) ``` --- ## prompt_strategies.dpo.chatml **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.dpo.chatml.html **Contents:** - prompt_strategies.dpo.chatml - Functions - argilla_chat - icr - intel - ultra prompt_strategies.dpo.chatml DPO strategies for chatml for argilla/dpo-mix-7k conversations chatml transforms for datasets with system, input, chosen, rejected ex. https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs For Intel Orca DPO Pairs for ultrafeedback binarized conversations **Examples:** Example 1 (python): ```python prompt_strategies.dpo.chatml.argilla_chat(cfg, **kwargs) ``` Example 2 (python): ```python prompt_strategies.dpo.chatml.icr(cfg, **kwargs) ``` Example 3 (python): ```python prompt_strategies.dpo.chatml.intel(cfg, **kwargs) ``` Example 4 (python): ```python prompt_strategies.dpo.chatml.ultra(cfg, **kwargs) ``` --- ## cli.quantize **URL:** https://docs.axolotl.ai/docs/api/cli.quantize.html **Contents:** - cli.quantize - Functions - do_quantize - Parameters CLI to post-training quantize a model using torchao Quantizes a model’s model’s weights **Examples:** Example 1 (python): ```python cli.quantize.do_quantize(config, cli_args) ``` --- ## utils.dict **URL:** https://docs.axolotl.ai/docs/api/utils.dict.html **Contents:** - utils.dict - Classes - DictDefault - Functions - remove_none_values Module containing the DictDefault class A Dict that returns None instead of returning empty Dict for missing keys. Remove null from a dictionary-like obj or list. These can appear due to Dataset loading causing schema merge. See https://github.com/axolotl-ai-cloud/axolotl/pull/2909 **Examples:** Example 1 (python): ```python utils.dict.DictDefault() ``` Example 2 (python): ```python utils.dict.remove_none_values(obj) ``` --- ## API Reference **URL:** https://docs.axolotl.ai/docs/api/ **Contents:** - API Reference - Core - CLI - Trainers - Model Loading - Mixins - Context Managers - Prompt Strategies - Kernels - Monkey Patches Core functionality for training Command-line interface Training implementations Functionality for loading and patching models, tokenizers, etc. Mixin classes for augmenting trainers Context managers for altering trainer behaviors Prompt formatting strategies Low-level performance optimizations Runtime patches for model optimizations Pydantic data models for Axolotl config Third-party integrations and extensions Common utilities and shared functionality Custom model implementations Data processing utilities --- ## monkeypatch.lora_kernels **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.lora_kernels.html **Contents:** - monkeypatch.lora_kernels - Classes - FakeMLP - Functions - apply_lora_kernel_patches - Parameters - Returns - Raises - Note - get_attention_cls_from_config monkeypatch.lora_kernels Module for patching custom LoRA Triton kernels and torch.autograd functions. placeholder MLP for triton patching Applies optimized Triton kernel patches to a PEFT model. Patches a PEFT model with optimized implementations for MLP and attention computations. The optimizations include custom Triton kernels for activation functions and specialized autograd functions for LoRA computations. The optimizations require LoRA adapters with no dropout and no bias terms. The function will skip patching if these conditions aren’t met. Get the appropriate attention class by inspecting the model config. Uses dynamic import to support any model architecture that follows the standard transformers naming convention. Get the layers of the model. Handles text-only and multimodal models. Original implementation of output projection without optimizations. Original implementation of QKV projection without optimizations. Given an axolotl config, this method patches the inferred attention class forward pass with optimized LoRA implementations. It modifies the attention class to use optimized QKV and output projections. The original implementation is preserved and can be restored if needed. **Examples:** Example 1 (python): ```python monkeypatch.lora_kernels.FakeMLP(gate_proj, up_proj, down_proj) ``` Example 2 (python): ```python monkeypatch.lora_kernels.apply_lora_kernel_patches(model, cfg) ``` Example 3 (python): ```python monkeypatch.lora_kernels.get_attention_cls_from_config(cfg) ``` Example 4 (python): ```python monkeypatch.lora_kernels.get_layers(model) ``` --- ## monkeypatch.stablelm_attn_hijack_flash **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.stablelm_attn_hijack_flash.html **Contents:** - monkeypatch.stablelm_attn_hijack_flash - Functions - repeat_kv - rotate_half monkeypatch.stablelm_attn_hijack_flash PyTorch StableLM Epoch model. This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) Rotates half the hidden dims of the input. **Examples:** Example 1 (python): ```python monkeypatch.stablelm_attn_hijack_flash.repeat_kv(hidden_states, n_rep) ``` Example 2 (python): ```python monkeypatch.stablelm_attn_hijack_flash.rotate_half(x) ``` --- ## core.trainers.mixins.rng_state_loader **URL:** https://docs.axolotl.ai/docs/api/core.trainers.mixins.rng_state_loader.html **Contents:** - core.trainers.mixins.rng_state_loader - Classes - RngLoaderMixin core.trainers.mixins.rng_state_loader Temporary fix/override for bug in resume from checkpoint See https://github.com/huggingface/transformers/pull/37162 TODO: Remove when upstream added PR to release mixin for method override to load RNG states from a checkpoint **Examples:** Example 1 (python): ```python core.trainers.mixins.rng_state_loader.RngLoaderMixin() ``` --- ## core.trainers.utils **URL:** https://docs.axolotl.ai/docs/api/core.trainers.utils.html **Contents:** - core.trainers.utils Utils for Axolotl trainers --- ## core.training_args **URL:** https://docs.axolotl.ai/docs/api/core.training_args.html **Contents:** - core.training_args - Classes - AxolotlCPOConfig - AxolotlKTOConfig - AxolotlORPOConfig - AxolotlPRMConfig - AxolotlRewardConfig - AxolotlTrainingArguments extra axolotl specific training args CPO config for CPO training KTO config for KTO training ORPO config for ORPO training PRM config for PRM training Reward config for Reward training Training arguments for Causal trainer This code is duplicated due to HF TrainingArguments not setting output_dir with a default value so it can’t be used as a mixin. **Examples:** Example 1 (python): ```python core.training_args.AxolotlCPOConfig(simpo_gamma=None) ``` Example 2 (python): ```python core.training_args.AxolotlKTOConfig() ``` Example 3 (python): ```python core.training_args.AxolotlORPOConfig() ``` Example 4 (python): ```python core.training_args.AxolotlPRMConfig() ``` --- ## monkeypatch.btlm_attn_hijack_flash **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.btlm_attn_hijack_flash.html **Contents:** - monkeypatch.btlm_attn_hijack_flash monkeypatch.btlm_attn_hijack_flash Flash attention monkey patch for cerebras btlm model --- ## prompt_strategies.dpo.passthrough **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.dpo.passthrough.html **Contents:** - prompt_strategies.dpo.passthrough prompt_strategies.dpo.passthrough DPO prompt strategies passthrough/zero-processing strategy --- ## kernels.swiglu **URL:** https://docs.axolotl.ai/docs/api/kernels.swiglu.html **Contents:** - kernels.swiglu - Functions - swiglu_backward - Parameters - Returns - swiglu_forward - Parameters - Returns Module for definition of SwiGLU Triton kernels. See “GLU Variants Improve Transformer” (https://arxiv.org/abs/2002.05202). Credit to unsloth (https://unsloth.ai/) for inspiration for this implementation. SwiGLU backward pass using in-place operations. SwiGLU forward pass. Computes SwiGLU activation: x * sigmoid(x) * up, where x is the gate tensor. **Examples:** Example 1 (python): ```python kernels.swiglu.swiglu_backward(grad_output, gate, up) ``` Example 2 (python): ```python kernels.swiglu.swiglu_forward(gate, up) ``` --- ## core.trainers.grpo.trainer **URL:** https://docs.axolotl.ai/docs/api/core.trainers.grpo.trainer.html **Contents:** - core.trainers.grpo.trainer - Classes - AxolotlGRPOSequenceParallelTrainer - Methods - get_train_dataloader - AxolotlGRPOTrainer core.trainers.grpo.trainer Axolotl GRPO trainers (with and without sequence parallelism handling) Extend the base GRPOTrainer for sequence parallelism handling Get dataloader for training Extend the base GRPOTrainer for axolotl helpers **Examples:** Example 1 (python): ```python core.trainers.grpo.trainer.AxolotlGRPOSequenceParallelTrainer( model, reward_funcs, args=None, train_dataset=None, eval_dataset=None, processing_class=None, reward_processing_classes=None, callbacks=None, optimizers=(None, None), peft_config=None, optimizer_cls_and_kwargs=None, ) ``` Example 2 (python): ```python core.trainers.grpo.trainer.AxolotlGRPOSequenceParallelTrainer.get_train_dataloader( ) ``` Example 3 (python): ```python core.trainers.grpo.trainer.AxolotlGRPOTrainer(*args, **kwargs) ``` --- ## prompt_strategies.user_defined **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.user_defined.html **Contents:** - prompt_strategies.user_defined - Classes - UserDefinedDatasetConfig - UserDefinedPromptTokenizationStrategy prompt_strategies.user_defined User Defined prompts with configuration from the YML config dataclass configuration representing a userdefined dataset type Prompt Tokenization Strategy for user defined prompts **Examples:** Example 1 (python): ```python prompt_strategies.user_defined.UserDefinedDatasetConfig( system_prompt='', field_system='system', field_instruction='instruction', field_input='input', field_output='output', format='{instruction} {input} ', no_input_format='{instruction} ', system_format='{system}', ) ``` Example 2 (python): ```python prompt_strategies.user_defined.UserDefinedPromptTokenizationStrategy( prompter, tokenizer, train_on_inputs=False, sequence_len=2048, ) ``` --- ## utils.schemas.training **URL:** https://docs.axolotl.ai/docs/api/utils.schemas.training.html **Contents:** - utils.schemas.training - Classes - HyperparametersConfig - JaggedLRConfig - LrGroup utils.schemas.training Pydantic models for training hyperparameters Training hyperparams configuration subset JaggedLR configuration subset, can be used w/ ReLoRA training Custom learning rate group configuration **Examples:** Example 1 (python): ```python utils.schemas.training.HyperparametersConfig() ``` Example 2 (python): ```python utils.schemas.training.JaggedLRConfig() ``` Example 3 (python): ```python utils.schemas.training.LrGroup() ``` --- ## utils.quantization **URL:** https://docs.axolotl.ai/docs/api/utils.quantization.html **Contents:** - utils.quantization - Functions - convert_qat_model - get_quantization_config - Parameters - Returns - Raises - prepare_model_for_qat - Parameters - Raises Utilities for quantization including QAT and PTQ using torchao. This function converts a QAT model which has fake quantized layers back to the original model. This function is used to build a post-training quantization config. This function is used to prepare a model for QAT by swapping the model’s linear layers with fake quantized linear layers, and optionally the embedding weights with fake quantized embedding weights. This function is used to quantize a model. **Examples:** Example 1 (python): ```python utils.quantization.convert_qat_model(model, quantize_embedding=False) ``` Example 2 (python): ```python utils.quantization.get_quantization_config( weight_dtype, activation_dtype=None, group_size=None, ) ``` Example 3 (python): ```python utils.quantization.prepare_model_for_qat( model, weight_dtype, group_size=None, activation_dtype=None, quantize_embedding=False, ) ``` Example 4 (python): ```python utils.quantization.quantize_model( model, weight_dtype, group_size=None, activation_dtype=None, quantize_embedding=None, ) ``` --- ## logging_config **URL:** https://docs.axolotl.ai/docs/api/logging_config.html **Contents:** - logging_config - Classes - AxolotlLogger - AxolotlOrWarnErrorFilter - ColorfulFormatter - Functions - configure_logging Common logging module for axolotl. Logger that applies filtering to non-axolotl loggers. Allows ANY WARNING or higher (unless overridden by LOG_LEVEL). Allows axolotl.* at INFO or higher (unless overridden by AXOLOTL_LOG_LEVEL). Drops all other records (i.e. non-axolotl.INFO, DEBUG, etc. by default). Formatter to add coloring to log messages by log type Configure with default logging **Examples:** Example 1 (python): ```python logging_config.AxolotlLogger(name, level=logging.NOTSET) ``` Example 2 (python): ```python logging_config.AxolotlOrWarnErrorFilter(**kwargs) ``` Example 3 (python): ```python logging_config.ColorfulFormatter() ``` Example 4 (python): ```python logging_config.configure_logging() ``` --- ## prompt_strategies.stepwise_supervised **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.stepwise_supervised.html **Contents:** - prompt_strategies.stepwise_supervised - Classes - StepwiseSupervisedPromptTokenizingStrategy prompt_strategies.stepwise_supervised Module for stepwise datasets, typically including a prompt and reasoning traces, and (optionally) per-step, or per-prompt-trace labels for reward modelling. Tokenizing strategy for supervised stepwise datasets, typically used for COT-reasoning. These datasets should include the following columns: - prompt: the prompt text - completions: a list of n completion steps - labels: a list of n labels indicating the “correctness” of each step **Examples:** Example 1 (python): ```python prompt_strategies.stepwise_supervised.StepwiseSupervisedPromptTokenizingStrategy( tokenizer, sequence_len=2048, step_separator='\n', max_completion_length=None, train_on_last_step_only=False, ) ``` --- ## utils.schemas.model **URL:** https://docs.axolotl.ai/docs/api/utils.schemas.model.html **Contents:** - utils.schemas.model - Classes - ModelInputConfig - ModelOutputConfig - SpecialTokensConfig Pydantic models for model input / output, etc. configuration Model configuration subset model save configuration subset Special tokens configuration subset **Examples:** Example 1 (python): ```python utils.schemas.model.ModelInputConfig() ``` Example 2 (python): ```python utils.schemas.model.ModelOutputConfig() ``` Example 3 (python): ```python utils.schemas.model.SpecialTokensConfig() ``` --- ## utils.schemas.enums **URL:** https://docs.axolotl.ai/docs/api/utils.schemas.enums.html **Contents:** - utils.schemas.enums - Classes - ChatTemplate - CustomSupportedOptimizers - RLType - RingAttnFunc Enums for Axolotl input config Chat templates configuration subset Custom supported optimizers RL trainer type configuration subset Enum class for supported ring-flash-attn implementations **Examples:** Example 1 (python): ```python utils.schemas.enums.ChatTemplate() ``` Example 2 (python): ```python utils.schemas.enums.CustomSupportedOptimizers() ``` Example 3 (python): ```python utils.schemas.enums.RLType() ``` Example 4 (python): ```python utils.schemas.enums.RingAttnFunc() ``` --- ## core.trainers.trl **URL:** https://docs.axolotl.ai/docs/api/core.trainers.trl.html **Contents:** - core.trainers.trl - Classes - AxolotlCPOTrainer - AxolotlKTOTrainer - AxolotlORPOTrainer - AxolotlPRMTrainer - AxolotlRewardTrainer Module for TRL RL trainers Extend the base CPOTrainer for axolotl helpers Extend the base KTOTrainer for axolotl helpers Extend the base ORPOTrainer for axolotl helpers Extend the base trl.PRMTrainer for axolotl helpers Extend the base RewardTrainer for axolotl helpers **Examples:** Example 1 (python): ```python core.trainers.trl.AxolotlCPOTrainer(*args, **kwargs) ``` Example 2 (python): ```python core.trainers.trl.AxolotlKTOTrainer(*args, **kwargs) ``` Example 3 (python): ```python core.trainers.trl.AxolotlORPOTrainer(*args, **kwargs) ``` Example 4 (python): ```python core.trainers.trl.AxolotlPRMTrainer(*args, **kwargs) ``` --- ## utils.schedulers **URL:** https://docs.axolotl.ai/docs/api/utils.schedulers.html **Contents:** - utils.schedulers - Classes - InterpolatingLogScheduler - JaggedLRRestartScheduler - RexLR - Parameters - Functions - get_cosine_schedule_with_min_lr - Create a learning rate schedule which has - get_cosine_schedule_with_quadratic_warmup Module for custom LRScheduler class A scheduler that interpolates learning rates in a logarithmic fashion Wraps another scheduler to apply per-lora-restart learning rate warmups. Reflected Exponential (REX) learning rate scheduler. Create a schedule with a learning rate that decreases following the values of the cosine function between the initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the initial lr set in the optimizer. torch.optim.lr_scheduler.LambdaLR with the appropriate schedule. Implementation of Continual Pre-Training of Large Language Models: How to (re)warm your model? (https://arxiv.org/pdf/2308.04014.pdf) Create a schedule with a learning rate that decreases following the values of the cosine function between the initial lr set in the optimizer to min_lr_ratio until num_training_steps * constant_lr_ratio, after constant_rate returns constant value of min_rate , after a warmup period during which it increases linearly between 0 and the initial lr set in the optimizer. torch.optim.lr_scheduler.LambdaLR with the appropriate schedule. **Examples:** Example 1 (python): ```python utils.schedulers.InterpolatingLogScheduler( optimizer, num_steps, min_lr, max_lr, last_epoch=-1, ) ``` Example 2 (python): ```python utils.schedulers.JaggedLRRestartScheduler( optimizer, inner_schedule, jagged_restart_steps, jagged_restart_warmup_steps, jagged_restart_anneal_steps=1, min_lr_scale=0.001, ) ``` Example 3 (python): ```python utils.schedulers.RexLR( optimizer, max_lr, min_lr, total_steps=0, num_warmup_steps=0, last_step=0, ) ``` Example 4 (python): ```python utils.schedulers.get_cosine_schedule_with_min_lr( optimizer, num_warmup_steps, num_training_steps, min_lr_ratio=0.0, ) ``` --- ## cli.merge_lora **URL:** https://docs.axolotl.ai/docs/api/cli.merge_lora.html **Contents:** - cli.merge_lora - Functions - do_cli - Parameters - Raises - do_merge_lora - Parameters CLI to merge a trained LoRA into a base model. Parses axolotl config, CLI args, and calls do_merge_lora. Note that various config values will be overwritten to allow the LoRA merge logic to work as expected (load_in_8bit=False, load_in4bit=False, flash_attention=False, etc.). Calls transformers’ merge_and_unload on the model given in the axolotl config along with the LoRA adapters to combine them into a single base model. **Examples:** Example 1 (python): ```python cli.merge_lora.do_cli(config=Path('examples/'), **kwargs) ``` Example 2 (python): ```python cli.merge_lora.do_merge_lora(cfg) ``` --- ## prompt_strategies.alpaca_w_system **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.alpaca_w_system.html **Contents:** - prompt_strategies.alpaca_w_system - Classes - InstructionWSystemPromptTokenizingStrategy - OpenOrcaPromptTokenizingStrategy - OpenOrcaSystemDataPrompter - SystemDataPrompter prompt_strategies.alpaca_w_system Prompt strategies loader for alpaca instruction datasets with system prompts Tokenizing strategy for instruction-based prompts. Tokenizing strategy for OpenOrca datasets Alpaca Style Prompter that uses system prompts from the dataset, with OpenOrca prompts Alpaca Style Prompter that uses system prompts from the dataset **Examples:** Example 1 (python): ```python prompt_strategies.alpaca_w_system.InstructionWSystemPromptTokenizingStrategy( prompter, tokenizer, train_on_inputs=False, sequence_len=2048, ) ``` Example 2 (python): ```python prompt_strategies.alpaca_w_system.OpenOrcaPromptTokenizingStrategy( prompter, tokenizer, train_on_inputs=False, sequence_len=2048, ) ``` Example 3 (python): ```python prompt_strategies.alpaca_w_system.OpenOrcaSystemDataPrompter( prompt_style=PromptStyle.INSTRUCT.value, ) ``` Example 4 (python): ```python prompt_strategies.alpaca_w_system.SystemDataPrompter( prompt_style=PromptStyle.INSTRUCT.value, ) ``` --- ## loaders.patch_manager **URL:** https://docs.axolotl.ai/docs/api/loaders.patch_manager.html **Contents:** - loaders.patch_manager - Classes - PatchManager - Attributes - Methods - apply_post_model_load_patches - apply_post_plugin_pre_model_load_patches - apply_pre_model_load_patches loaders.patch_manager Patch manager class implementation to complement axolotl.loaders.ModelLoader. Applies pre- and post-model load patches for various fixes and optimizations. Manages the application of patches during the model loading process. Apply patches that require the model instance. Apply post plugin-pre_model_load load patches based on config. Apply pre-model load patches based on config. **Examples:** Example 1 (python): ```python loaders.patch_manager.PatchManager(cfg, model_config, inference=False) ``` Example 2 (python): ```python loaders.patch_manager.PatchManager.apply_post_model_load_patches(model) ``` Example 3 (python): ```python loaders.patch_manager.PatchManager.apply_post_plugin_pre_model_load_patches() ``` Example 4 (python): ```python loaders.patch_manager.PatchManager.apply_pre_model_load_patches() ``` --- ## utils.schemas.peft **URL:** https://docs.axolotl.ai/docs/api/utils.schemas.peft.html **Contents:** - utils.schemas.peft - Classes - LoftQConfig - LoraConfig - PeftConfig - ReLoRAConfig Pydantic models for PEFT-related configuration LoftQ configuration subset Peft / LoRA configuration subset peftq configuration subset ReLoRA configuration subset **Examples:** Example 1 (python): ```python utils.schemas.peft.LoftQConfig() ``` Example 2 (python): ```python utils.schemas.peft.LoraConfig() ``` Example 3 (python): ```python utils.schemas.peft.PeftConfig() ``` Example 4 (python): ```python utils.schemas.peft.ReLoRAConfig() ``` --- ## common.const **URL:** https://docs.axolotl.ai/docs/api/common.const.html **Contents:** - common.const Various shared constants --- ## prompt_strategies.kto.user_defined **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.kto.user_defined.html **Contents:** - prompt_strategies.kto.user_defined prompt_strategies.kto.user_defined User-defined KTO strategies --- ## prompt_strategies.base **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.base.html **Contents:** - prompt_strategies.base prompt_strategies.base module for base dataset transform strategies --- ## cli.delinearize_llama4 **URL:** https://docs.axolotl.ai/docs/api/cli.delinearize_llama4.html **Contents:** - cli.delinearize_llama4 - Functions - do_cli - Parameters cli.delinearize_llama4 CLI tool to delinearize quantized/Linearized Llama-4 models. Convert a patched HF format Llama4 model (with separated projections) back to the original HF format (with fused projections). **Examples:** Example 1 (python): ```python cli.delinearize_llama4.do_cli(model, output) ``` --- ## integrations.base **URL:** https://docs.axolotl.ai/docs/api/integrations.base.html **Contents:** - integrations.base - Classes - BaseOptimizerFactory - Methods - get_decay_parameter_names - BasePlugin - Note - Methods - add_callbacks_post_trainer - Parameters Base class for all plugins. A plugin is a reusable, modular, and self-contained piece of code that extends the functionality of Axolotl. Plugins can be used to integrate third-party models, modify the training process, or add new features. To create a new plugin, you need to inherit from the BasePlugin class and implement the required methods. Base class for factories to create custom optimizers Get all parameter names that weight decay will be applied to. This function filters out parameters in two ways: 1. By layer type (instances of layers specified in ALL_LAYERNORM_LAYERS) 2. By parameter name patterns (containing ‘bias’, or variation of ‘norm’) Base class for all plugins. Defines the interface for plugin methods. A plugin is a reusable, modular, and self-contained piece of code that extends the functionality of Axolotl. Plugins can be used to integrate third-party models, modify the training process, or add new features. To create a new plugin, you need to inherit from the BasePlugin class and implement the required methods. Plugin methods include: - register(cfg): Registers the plugin with the given configuration. - load_datasets(cfg): Loads and preprocesses the dataset for training. - pre_model_load(cfg): Performs actions before the model is loaded. - post_model_build(cfg, model): Performs actions after the model is loaded, but before LoRA adapters are applied. - pre_lora_load(cfg, model): Performs actions before LoRA weights are loaded. - post_lora_load(cfg, model): Performs actions after LoRA weights are loaded. - post_model_load(cfg, model): Performs actions after the model is loaded, inclusive of any adapters. - post_trainer_create(cfg, trainer): Performs actions after the trainer is created. - create_optimizer(cfg, trainer): Creates and returns an optimizer for training. - create_lr_scheduler(cfg, trainer, optimizer, num_training_steps): Creates and returns a learning rate scheduler. - add_callbacks_pre_trainer(cfg, model): Adds callbacks to the trainer before training. - add_callbacks_post_trainer(cfg, trainer): Adds callbacks to the trainer after training. Adds callbacks to the trainer after creating the trainer. This is useful for callbacks that require access to the model or trainer. Set up callbacks before creating the trainer. Creates and returns a learning rate scheduler. Creates and returns an optimizer for training. Returns a custom class for the collator. Returns a pydantic model for the plugin’s input arguments. Returns a custom class for the trainer. Returns custom training arguments to set on TrainingArgs. Returns a dataclass model for the plugin’s training arguments. Loads and preprocesses the dataset for training. Performs actions after LoRA weights are loaded. Performs actions after the model is built/loaded, but before any adapters are applied. Performs actions after the model is loaded. Performs actions after training is complete. Performs actions after training is complete and the model is unloaded. Performs actions after the trainer is created. Performs actions before LoRA weights are loaded. Performs actions before the model is loaded. Registers the plugin with the given configuration as an unparsed dict. The PluginManager class is responsible for loading and managing plugins. It should be a singleton so it can be accessed from anywhere in the codebase. Key methods include: - get_instance(): Static method to get the singleton instance of PluginManager. - register(plugin_name: str): Registers a new plugin by its name. - pre_model_load(cfg): Calls the pre_model_load method of all registered plugins. Calls the add_callbacks_post_trainer method of all registered plugins. Calls the add_callbacks_pre_trainer method of all registered plugins. Calls the create_lr_scheduler method of all registered plugins and returns the first non-None scheduler. Calls the create_optimizer method of all registered plugins and returns the first non-None optimizer. Calls the get_collator_cls_and_kwargs method of all registered plugins and returns the first non-None collator class. Parameters: cfg (dict): The configuration for the plugins. is_eval (bool): Whether this is an eval split. Returns: object: The collator class, or None if none was found. Returns a list of Pydantic classes for all registered plugins’ input arguments.’ Returns the singleton instance of PluginManager. If the instance doesn’t exist, it creates a new one. Calls the get_trainer_cls method of all registered plugins and returns the first non-None trainer class. Calls the get_training_args method of all registered plugins and returns the combined training arguments. Parameters: cfg (dict): The configuration for the plugins. Returns: object: The training arguments Returns a list of dataclasses for all registered plugins’ training args mixins’ Returns: list[str]: A list of dataclsses Calls the load_datasets method of each registered plugin. Calls the post_lora_load method of all registered plugins. Calls the post_model_build method of all registered plugins after the model has been built / loaded, but before any adapters have been applied. Calls the post_model_load method of all registered plugins after the model has been loaded inclusive of any adapters. Calls the post_train method of all registered plugins. Calls the post_train_unload method of all registered plugins. Calls the post_trainer_create method of all registered plugins. Calls the pre_lora_load method of all registered plugins. Calls the pre_model_load method of all registered plugins. Registers a new plugin by its name. Loads a plugin based on the given plugin name. The plugin name should be in the format “module_name.class_name”. This function splits the plugin name into module and class, imports the module, retrieves the class from the module, and creates an instance of the class. **Examples:** Example 1 (python): ```python integrations.base.BaseOptimizerFactory() ``` Example 2 (python): ```python integrations.base.BaseOptimizerFactory.get_decay_parameter_names(model) ``` Example 3 (python): ```python integrations.base.BasePlugin() ``` Example 4 (python): ```python integrations.base.BasePlugin.add_callbacks_post_trainer(cfg, trainer) ``` --- ## prompt_strategies.chat_template **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.chat_template.html **Contents:** - prompt_strategies.chat_template - Classes - ChatTemplatePrompter - Methods - build_prompt - Parameters - ChatTemplateStrategy - Methods - find_first_eot_token - find_turn prompt_strategies.chat_template HF Chat Templates prompt strategy Prompter for HF chat templates Build a prompt from a conversation. Tokenizing strategy for instruction-based prompts. Find the first EOT token in the input_ids starting from start_idx. Locate the starting and ending indices of the specified turn in a conversation. Public method that can handle either a single prompt or a batch of prompts. Mistral prompter for chat template. Mistral strategy for chat template. Find the first EOT token in the input_ids starting from start_idx. Load chat template strategy based on configuration. **Examples:** Example 1 (python): ```python prompt_strategies.chat_template.ChatTemplatePrompter( tokenizer, chat_template, processor=None, max_length=2048, message_property_mappings=None, message_field_training=None, message_field_training_detail=None, field_messages='messages', field_system='system', field_tools='tools', field_thinking='reasoning_content', roles=None, template_thinking_key='reasoning_content', chat_template_kwargs=None, drop_system_message=False, ) ``` Example 2 (python): ```python prompt_strategies.chat_template.ChatTemplatePrompter.build_prompt( conversation, add_generation_prompt=False, images=None, tools=None, ) ``` Example 3 (python): ```python prompt_strategies.chat_template.ChatTemplateStrategy( prompter, tokenizer, train_on_inputs, sequence_len, roles_to_train=None, train_on_eos=None, train_on_eot=None, eot_tokens=None, split_thinking=False, ) ``` Example 4 (python): ```python prompt_strategies.chat_template.ChatTemplateStrategy.find_first_eot_token( input_ids, start_idx, ) ``` --- ## kernels.quantize **URL:** https://docs.axolotl.ai/docs/api/kernels.quantize.html **Contents:** - kernels.quantize - Functions - dequantize - Parameters - Returns - Raises - Note Dequantization utilities for bitsandbytes integration. Fast NF4 dequantization using bitsandbytes CUDA kernels. Performs efficient dequantization of weights from NF4 format using bitsandbytes’ optimized CUDA implementations. Supports both legacy list and new QuantState formats. Uses CUDA streams for better performance when available in newer bitsandbytes versions (>0.43.3). **Examples:** Example 1 (python): ```python kernels.quantize.dequantize(W, quant_state=None, out=None) ``` --- ## integrations.spectrum.args **URL:** https://docs.axolotl.ai/docs/api/integrations.spectrum.args.html **Contents:** - integrations.spectrum.args - Classes - SpectrumArgs integrations.spectrum.args Module for handling Spectrum input arguments. Input args for Spectrum. **Examples:** Example 1 (python): ```python integrations.spectrum.args.SpectrumArgs() ``` --- ## prompt_strategies.alpaca_chat **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.alpaca_chat.html **Contents:** - prompt_strategies.alpaca_chat - Classes - AlpacaChatPrompter - AlpacaConcisePrompter - AlpacaQAPromptTokenizingStrategy - CamelAIPromptTokenizingStrategy - NoSystemPrompter prompt_strategies.alpaca_chat Module for Alpaca prompt strategy classes Alpaca Chat Prompter extending the system prompt to for chat-instruct answers Alpaca Prompter extending the system prompt to ask for concise chat-instruct answers Tokenizing strategy for AlpacaQA Tokenizing strategy for CamelAI datasets Null Prompter with no system prompts **Examples:** Example 1 (python): ```python prompt_strategies.alpaca_chat.AlpacaChatPrompter() ``` Example 2 (python): ```python prompt_strategies.alpaca_chat.AlpacaConcisePrompter( prompt_style=PromptStyle.INSTRUCT.value, ) ``` Example 3 (python): ```python prompt_strategies.alpaca_chat.AlpacaQAPromptTokenizingStrategy( prompter, tokenizer, train_on_inputs=False, sequence_len=2048, ) ``` Example 4 (python): ```python prompt_strategies.alpaca_chat.CamelAIPromptTokenizingStrategy( prompter, tokenizer, train_on_inputs=False, sequence_len=2048, ) ``` --- ## utils.collators.mamba **URL:** https://docs.axolotl.ai/docs/api/utils.collators.mamba.html **Contents:** - utils.collators.mamba - Classes - MambaDataCollator utils.collators.mamba Collator for State Space Models (Mamba) **Examples:** Example 1 (python): ```python utils.collators.mamba.MambaDataCollator(tokenizer) ``` --- ## prompt_strategies.messages.chat **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.messages.chat.html **Contents:** - prompt_strategies.messages.chat - Classes - ChatMessageDatasetWrappingStrategy prompt_strategies.messages.chat Chat dataset wrapping strategy for new internal messages representations Chat dataset wrapping strategy for new internal messages representations **Examples:** Example 1 (python): ```python prompt_strategies.messages.chat.ChatMessageDatasetWrappingStrategy( processor, message_transform=None, formatter=None, **kwargs, ) ``` --- ## train **URL:** https://docs.axolotl.ai/docs/api/train.html **Contents:** - train - Functions - create_model_card - Parameters - execute_training - Parameters - handle_untrained_tokens_fix - Parameters - save_initial_configs - Parameters Prepare and train a model on a dataset. Can also infer from a model or merge lora Create a model card for the trained model if needed. Execute the training process with appropriate SDP kernel configurations. Apply fixes for untrained tokens if configured. Save initial configurations before training. Save the trained model according to configuration and training setup. Load the tokenizer, processor (for multimodal models), and model based on configuration. Load model, tokenizer, trainer, etc. Helper function to encapsulate the full trainer setup. Set up the Axolotl badge and add the Axolotl config to the model card if available. Set up the reference model for RL training if needed. Set up signal handler for graceful termination. Train a model on the given dataset. **Examples:** Example 1 (python): ```python train.create_model_card(cfg, trainer) ``` Example 2 (python): ```python train.execute_training(cfg, trainer, resume_from_checkpoint) ``` Example 3 (python): ```python train.handle_untrained_tokens_fix( cfg, model, tokenizer, train_dataset, safe_serialization, ) ``` Example 4 (python): ```python train.save_initial_configs(cfg, tokenizer, model, peft_config, processor) ``` --- ## cli.utils.load **URL:** https://docs.axolotl.ai/docs/api/cli.utils.load.html **Contents:** - cli.utils.load - Functions - load_model_and_tokenizer - Parameters - Returns Utilities for model, tokenizer, etc. loading. Helper function for loading a model, tokenizer, and processor specified in the given axolotl config. **Examples:** Example 1 (python): ```python cli.utils.load.load_model_and_tokenizer(cfg, inference=False) ``` --- ## loaders.model **URL:** https://docs.axolotl.ai/docs/api/loaders.model.html **Contents:** - loaders.model - Classes - ModelLoader - The loading process includes - Attributes - Methods - load - Returns Model loader class implementation for loading, configuring, and patching various models. Manages model configuration, initialization and application of patches during model loading. This class orchestrates the entire process of loading a model from configuration to final preparation. It handles device mapping, quantization, attention mechanisms, adapter integration, and various optimizations. Load and prepare the model with all configurations and patches. **Examples:** Example 1 (python): ```python loaders.model.ModelLoader( cfg, tokenizer, *, inference=False, reference_model=False, **kwargs, ) ``` Example 2 (python): ```python loaders.model.ModelLoader.load() ``` --- ## utils.distributed **URL:** https://docs.axolotl.ai/docs/api/utils.distributed.html **Contents:** - utils.distributed - Functions - barrier - cleanup_distributed - compute_and_broadcast - gather_from_all_ranks - gather_scalar_from_all_ranks - is_distributed - is_main_process - Returns Utilities for distributed functionality. Acts as a barrier to wait for all processes. This ensures that all processes reach the barrier before proceeding further. Destroy process group if torch distributed is initialized. Called in training early termination or when training successfully completes. Compute a value using the function ‘fn’ only on the specified rank (default is 0). The value is then broadcasted to all other ranks. Args: - fn (callable): A function that computes the value. This should not have any side effects. - rank (int, optional): The rank that computes the value. Default is 0. Returns: - The computed value (int or float). Run a callable ‘fn’ on all ranks and gather the results on the specified rank. Args: - fn (callable): A function that computes the value. This should not have any side effects. - rank (int, optional): The rank that gathers the values. Default is 0. - world_size (int, optional): Total number of processes in the current distributed setup. Returns: - A list of computed values from all ranks if on the gathering rank, otherwise None. Run a callable ‘fn’ on all ranks and gather the results on the specified rank. Args: - fn (callable): A function that computes the value. This should not have any side effects. - rank (int, optional): The rank that gathers the values. Default is 0. - world_size (int, optional): Total number of processes in the current distributed setup. Returns: - A list of computed values from all ranks if on the gathering rank, otherwise None. Check if distributed training is initialized. Check if the current process is the main process. If not in distributed mode, always return True. We use a simpler logic when the distributed state is not initialized: we just log on the 0-th local rank. Run a callable ‘fn1’ on all ranks, gather the results, reduce them using ‘fn2’, and then broadcast the reduced result to all ranks. Args: - fn1 (callable): A function that computes the value on each rank. - fn2 (callable): A reduction function that takes a list of values and returns a single value. - world_size (int, optional): Total number of processes in the current distributed setup. Returns: - The reduced and broadcasted value. runs the wrapped context so that rank 0 runs first before other ranks **Examples:** Example 1 (python): ```python utils.distributed.barrier() ``` Example 2 (python): ```python utils.distributed.cleanup_distributed() ``` Example 3 (python): ```python utils.distributed.compute_and_broadcast(fn) ``` Example 4 (python): ```python utils.distributed.gather_from_all_ranks(fn, world_size=1) ``` --- ## cli.config **URL:** https://docs.axolotl.ai/docs/api/cli.config.html **Contents:** - cli.config - Functions - check_remote_config - Parameters - Returns - Raises - choose_config - Parameters - Returns - Raises Configuration loading and processing. First, determines if the passed config is a valid HTTPS URL. Then, attempts to query for it and parse its content, first as JSON, then as YAML (YAML is preferred). Finally, the parsed content is written to a local file and its path is returned. Helper method for choosing a axolotl config YAML file (considering only files ending with .yml or .yaml). If more than one config file exists in the passed path, the user is prompted to choose one. Loads the axolotl configuration stored at config, validates it, and performs various setup. Registers the plugins for the given configuration. **Examples:** Example 1 (python): ```python cli.config.check_remote_config(config) ``` Example 2 (python): ```python cli.config.choose_config(path) ``` Example 3 (python): ```python cli.config.load_cfg(config=Path('examples/'), **kwargs) ``` Example 4 (python): ```python cli.config.prepare_plugins(cfg) ``` --- ## cli.checks **URL:** https://docs.axolotl.ai/docs/api/cli.checks.html **Contents:** - cli.checks - Functions - check_accelerate_default_config - check_user_token - Returns - Raises Various checks for Axolotl CLI. Logs at warning level if no accelerate config file is found. Checks for HF user info. Check is skipped if HF_HUB_OFFLINE=1. **Examples:** Example 1 (python): ```python cli.checks.check_accelerate_default_config() ``` Example 2 (python): ```python cli.checks.check_user_token() ``` --- ## prompt_strategies.llama2_chat **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.llama2_chat.html **Contents:** - prompt_strategies.llama2_chat - Classes - LLama2ChatTokenizingStrategy - Llama2ChatConversation - Methods - append_message - get_prompt - Llama2ChatPrompter prompt_strategies.llama2_chat Prompt Strategy for finetuning Llama2 chat models see also https://github.com/facebookresearch/llama/blob/6c7fe276574e78057f917549435a2554000a876d/llama/generation.py#L213 for ma reference implementation. This implementation is based on the Vicuna PR and the fastchat repo, see also: https://github.com/lm-sys/FastChat/blob/cdd7730686cb1bf9ae2b768ee171bdf7d1ff04f3/fastchat/conversation.py#L847 Use dataset type: “llama2_chat” in config.yml to use this prompt style. E.g. in the config.yml: The dataset itself should look like this: in a jsonl file. The first message should be from the human, the second from gpt. For a custom system message, the first “from” can be “system” (followed by alternating “human” and “gpt” turns). Important: Don’t use “special_tokens:” in your config.yml if you are not sure what you are doing! Tokenizing strategy for Llama2 prompts. adapted from https://github.com/lm-sys/FastChat/blob/main/fastchat/train/train.py A class that manages prompt templates and keeps all conversation history. copied from https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py Append a new message. Get the prompt for generation. A prompter that generates prompts for Llama2 models. **Examples:** Example 1 (unknown): ```unknown datasets: - path: llama_finetune_train.jsonl type: llama2_chat ``` Example 2 (unknown): ```unknown {'conversations':[{"from": "human", "value": "Who are you?"}, {"from": "gpt", "value": "I am Vicuna"},...]} ``` Example 3 (python): ```python prompt_strategies.llama2_chat.LLama2ChatTokenizingStrategy(*args, **kwargs) ``` Example 4 (python): ```python prompt_strategies.llama2_chat.Llama2ChatConversation( name='llama2', system="[INST] <>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n<>\n\n", roles=('[INST]', '[/INST]'), messages=list(), offset=0, ) ``` --- ## cli.utils **URL:** https://docs.axolotl.ai/docs/api/cli.utils.html **Contents:** - cli.utils Init for axolotl.cli.utils module. --- ## cli.utils.args **URL:** https://docs.axolotl.ai/docs/api/cli.utils.args.html **Contents:** - cli.utils.args - Functions - add_options_from_config - Parameters - Returns - add_options_from_dataclass - Parameters - Returns - filter_none_kwargs - Parameters Utilities for axolotl CLI args. Create Click options from the fields of a Pydantic model. Create Click options from the fields of a dataclass. Wraps function to remove None-valued kwargs. **Examples:** Example 1 (python): ```python cli.utils.args.add_options_from_config(config_class) ``` Example 2 (python): ```python cli.utils.args.add_options_from_dataclass(config_class) ``` Example 3 (python): ```python cli.utils.args.filter_none_kwargs(func) ``` --- ## integrations.grokfast.optimizer **URL:** https://docs.axolotl.ai/docs/api/integrations.grokfast.optimizer.html **Contents:** - integrations.grokfast.optimizer integrations.grokfast.optimizer --- ## core.builders.causal **URL:** https://docs.axolotl.ai/docs/api/core.builders.causal.html **Contents:** - core.builders.causal - Classes - HFCausalTrainerBuilder Builder for causal trainers Build the HuggingFace training args/trainer for causal models and reward modeling using TRL. **Examples:** Example 1 (python): ```python core.builders.causal.HFCausalTrainerBuilder( cfg, model, tokenizer, processor=None, ) ``` --- ## prompt_strategies.dpo.user_defined **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.dpo.user_defined.html **Contents:** - prompt_strategies.dpo.user_defined prompt_strategies.dpo.user_defined User-defined DPO strategies --- ## cli.evaluate **URL:** https://docs.axolotl.ai/docs/api/cli.evaluate.html **Contents:** - cli.evaluate - Functions - do_cli - Parameters - do_evaluate - Parameters CLI to run evaluation on a model. Parses axolotl config, CLI args, and calls do_evaluate. Evaluates a transformers model by first loading the dataset(s) specified in the axolotl config, and then calling axolotl.evaluate.evaluate, which computes evaluation metrics on the given dataset(s) and writes them to disk. **Examples:** Example 1 (python): ```python cli.evaluate.do_cli(config=Path('examples/'), **kwargs) ``` Example 2 (python): ```python cli.evaluate.do_evaluate(cfg, cli_args) ``` --- ## utils.schemas.utils **URL:** https://docs.axolotl.ai/docs/api/utils.schemas.utils.html **Contents:** - utils.schemas.utils - Functions - handle_legacy_message_fields_logic - Parameters - Returns - Raises Utilities for Axolotl Pydantic models Handle backwards compatibility between legacy message field mapping and new property mapping system. Previously, the config only supported mapping ‘role’ and ‘content’ fields via dedicated config options: - message_field_role: Mapped to the role field - message_field_content: Mapped to the content field The new system uses message_property_mappings to support arbitrary field mappings: message_property_mappings: role: source_role_field content: source_content_field additional_field: source_field **Examples:** Example 1 (python): ```python utils.schemas.utils.handle_legacy_message_fields_logic(data) ``` --- ## prompt_strategies.alpaca_instruct **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.alpaca_instruct.html **Contents:** - prompt_strategies.alpaca_instruct prompt_strategies.alpaca_instruct Module loading the AlpacaInstructPromptTokenizingStrategy class --- ## utils.callbacks.lisa **URL:** https://docs.axolotl.ai/docs/api/utils.callbacks.lisa.html **Contents:** - utils.callbacks.lisa Adapted from https://github.com/OptimalScale/LMFlow/pull/701 for HF transformers & Axolotl Arxiv: https://arxiv.org/abs/2403.17919 License: Apache 2.0 --- ## models.mamba.modeling_mamba **URL:** https://docs.axolotl.ai/docs/api/models.mamba.modeling_mamba.html **Contents:** - models.mamba.modeling_mamba models.mamba.modeling_mamba --- ## prompt_strategies.metharme **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.metharme.html **Contents:** - prompt_strategies.metharme - Classes - MetharmePromptTokenizingStrategy - MetharmePrompter prompt_strategies.metharme Module containing the MetharmenPromptTokenizingStrategy and MetharmePrompter class Tokenizing strategy for the Metharme models Prompter for the Metharme models. **Examples:** Example 1 (python): ```python prompt_strategies.metharme.MetharmePromptTokenizingStrategy( prompter, tokenizer, train_on_inputs=False, sequence_len=2048, ) ``` Example 2 (python): ```python prompt_strategies.metharme.MetharmePrompter(*args, **kwargs) ``` --- ## core.trainers.mamba **URL:** https://docs.axolotl.ai/docs/api/core.trainers.mamba.html **Contents:** - core.trainers.mamba - Classes - AxolotlMambaTrainer Module for mamba trainer Mamba specific trainer to handle loss calculation **Examples:** Example 1 (python): ```python core.trainers.mamba.AxolotlMambaTrainer( *_args, bench_data_collator=None, eval_data_collator=None, dataset_tags=None, **kwargs, ) ``` --- ## utils.ctx_managers.sequence_parallel **URL:** https://docs.axolotl.ai/docs/api/utils.ctx_managers.sequence_parallel.html **Contents:** - utils.ctx_managers.sequence_parallel - Classes - AllGatherWithGrad - Methods - backward - Parameters - Returns - forward - Parameters - Returns utils.ctx_managers.sequence_parallel Module for Axolotl trainer sequence parallelism manager and utilities Custom autograd function for all-gather to preserve gradients. Backward pass for all-gather operation. Extracts the gradient slice corresponding to this rank’s original input from the full gradient tensor. Forward pass of all-gather of data with sequence dimension. Context manager for sequence parallelism operations. This class provides a context that will automatically apply sequence parallelism during model forward passes using a pre-forward hook, and gather outputs from across the sequence parallelism group using a post-forward hook. Apply sequence parallelism slicing to a batch. Special handling is implemented for integer logits_to_keep, which indicates to only keep the last N tokens in the sequence during generation. **Examples:** Example 1 (python): ```python utils.ctx_managers.sequence_parallel.AllGatherWithGrad() ``` Example 2 (python): ```python utils.ctx_managers.sequence_parallel.AllGatherWithGrad.backward( ctx, grad_output, ) ``` Example 3 (python): ```python utils.ctx_managers.sequence_parallel.AllGatherWithGrad.forward( ctx, input_tensor, group, ) ``` Example 4 (python): ```python utils.ctx_managers.sequence_parallel.SequenceParallelContextManager( models, context_parallel_size, gradient_accumulation_steps, ring_attn_func, heads_k_stride, gather_outputs, device_mesh=None, ) ``` --- ## utils.callbacks.qat **URL:** https://docs.axolotl.ai/docs/api/utils.callbacks.qat.html **Contents:** - utils.callbacks.qat - Classes - QATCallback - Functions - toggle_fake_quant - Parameters QAT Callback for HF Causal Trainer Callback to toggle fake quantization for the model. Toggle fake quantization for any fake quantized linear or embedding layers in the model. **Examples:** Example 1 (python): ```python utils.callbacks.qat.QATCallback(cfg) ``` Example 2 (python): ```python utils.callbacks.qat.toggle_fake_quant(mod, enable) ``` --- ## prompt_strategies.dpo.zephyr **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.dpo.zephyr.html **Contents:** - prompt_strategies.dpo.zephyr prompt_strategies.dpo.zephyr DPO strategies for zephyr --- ## kernels.utils **URL:** https://docs.axolotl.ai/docs/api/kernels.utils.html **Contents:** - kernels.utils Utilities for axolotl.kernels submodules. --- ## monkeypatch.multipack **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.multipack.html **Contents:** - monkeypatch.multipack monkeypatch.multipack multipack patching for v2 of sample packing --- ## cli.main **URL:** https://docs.axolotl.ai/docs/api/cli.main.html **Contents:** - cli.main - Functions - cli - evaluate - Parameters - fetch - Parameters - inference - Parameters - merge_lora Click CLI definitions for various axolotl commands. Axolotl CLI - Train and fine-tune large language models Fetch example configs or other resources. Available directories: - examples: Example configuration files - deepspeed_configs: DeepSpeed configuration files Run inference with a trained model. Merge trained LoRA adapters into a base model. Merge sharded FSDP model weights. Preprocess datasets before training. Train or fine-tune a model. **Examples:** Example 1 (python): ```python cli.main.cli() ``` Example 2 (python): ```python cli.main.evaluate(ctx, config, launcher, **kwargs) ``` Example 3 (python): ```python cli.main.fetch(directory, dest) ``` Example 4 (python): ```python cli.main.inference(ctx, config, launcher, gradio, **kwargs) ``` --- ## core.trainers.mixins.optimizer **URL:** https://docs.axolotl.ai/docs/api/core.trainers.mixins.optimizer.html **Contents:** - core.trainers.mixins.optimizer - Classes - OptimizerInitMixin - OptimizerMixin core.trainers.mixins.optimizer Module for Axolotl trainer optimizer mixin Mixin to handle common optimizer initialization logic for Trainers (mostly TRL) that do not accept optimizer_cls_and_kwargs as kwarg in constructor. Mixin class for shared handling of building custom optimizers **Examples:** Example 1 (python): ```python core.trainers.mixins.optimizer.OptimizerInitMixin(*args, **kwargs) ``` Example 2 (python): ```python core.trainers.mixins.optimizer.OptimizerMixin() ``` --- ## integrations.kd.trainer **URL:** https://docs.axolotl.ai/docs/api/integrations.kd.trainer.html **Contents:** - integrations.kd.trainer - Classes - AxolotlKDTrainer - Methods - compute_loss integrations.kd.trainer Custom trainer subclass for Knowledge Distillation (KD) How the loss is computed by Trainer. By default, all models return the loss in the first element. Subclass and override for custom behavior. **Examples:** Example 1 (python): ```python integrations.kd.trainer.AxolotlKDTrainer(*args, **kwargs) ``` Example 2 (python): ```python integrations.kd.trainer.AxolotlKDTrainer.compute_loss( model, inputs, return_outputs=False, num_items_in_batch=None, ) ``` --- ## integrations.lm_eval.args **URL:** https://docs.axolotl.ai/docs/api/integrations.lm_eval.args.html **Contents:** - integrations.lm_eval.args - Classes - LMEvalArgs integrations.lm_eval.args Module for handling lm eval harness input arguments. Input args for lm eval harness **Examples:** Example 1 (python): ```python integrations.lm_eval.args.LMEvalArgs() ``` --- ## integrations.cut_cross_entropy.args **URL:** https://docs.axolotl.ai/docs/api/integrations.cut_cross_entropy.args.html **Contents:** - integrations.cut_cross_entropy.args - Classes - CutCrossEntropyArgs integrations.cut_cross_entropy.args Module for handling Cut Cross Entropy input arguments. Input args for Cut Cross Entropy. **Examples:** Example 1 (python): ```python integrations.cut_cross_entropy.args.CutCrossEntropyArgs() ``` --- ## monkeypatch.mistral_attn_hijack_flash **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.mistral_attn_hijack_flash.html **Contents:** - monkeypatch.mistral_attn_hijack_flash monkeypatch.mistral_attn_hijack_flash Flash attention monkey patch for mistral model --- ## loaders.constants **URL:** https://docs.axolotl.ai/docs/api/loaders.constants.html **Contents:** - loaders.constants Shared constants for axolotl.loaders module --- ## utils.bench **URL:** https://docs.axolotl.ai/docs/api/utils.bench.html **Contents:** - utils.bench - Functions - check_cuda_device Benchmarking and measurement utilities wraps a function and returns the default value instead of running the wrapped function if cuda isn’t available or the device is auto :param default_value: :return: **Examples:** Example 1 (python): ```python utils.bench.check_cuda_device(default_value) ``` --- ## utils.trainer **URL:** https://docs.axolotl.ai/docs/api/utils.trainer.html **Contents:** - utils.trainer - Functions - add_pose_position_ids - add_position_ids - drop_long_seq - setup_trainer - Parameters - Returns Module containing the Trainer class and related functions use the PoSE technique to extend the context length by randomly skipping positions in the context. We only want to skip right before tokens in the split_on_token_ids list. We should attempt to randomly distribute the skips, but we don’t need the final position_ids to be the full context_len. There may be multiple turns in the context, so we want to make sure we take into account the maximum possible number of skips remaining in each sample. Handle both single-example and batched data. - single example: sample[‘input_ids’] is a list[int] - batched data: sample[‘input_ids’] is a list[list[int]] Drop samples whose sequence length is either too long (> sequence_len) or too short (< min_sequence_len). Works for both single-example (list[int]) or batched (list[list[int]]). Helper method for instantiating and building a (causal or RLHF) trainer. **Examples:** Example 1 (python): ```python utils.trainer.add_pose_position_ids( sample, max_context_len=32768, split_on_token_ids=None, chunks=2, ) ``` Example 2 (python): ```python utils.trainer.add_position_ids(sample) ``` Example 3 (python): ```python utils.trainer.drop_long_seq(sample, sequence_len=2048, min_sequence_len=2) ``` Example 4 (python): ```python utils.trainer.setup_trainer( cfg, train_dataset, eval_dataset, model, tokenizer, processor, total_num_steps, model_ref=None, peft_config=None, ) ``` --- ## utils.schemas.config **URL:** https://docs.axolotl.ai/docs/api/utils.schemas.config.html **Contents:** - utils.schemas.config - Classes - AxolotlConfigWCapabilities - AxolotlInputConfig Module with Pydantic models for configuration. wrapper to valdiate GPU capabilities with the configured options Wrapper of all config options. **Examples:** Example 1 (python): ```python utils.schemas.config.AxolotlConfigWCapabilities() ``` Example 2 (python): ```python utils.schemas.config.AxolotlInputConfig() ``` --- ## cli.args **URL:** https://docs.axolotl.ai/docs/api/cli.args.html **Contents:** - cli.args - Classes - EvaluateCliArgs - InferenceCliArgs - PreprocessCliArgs - QuantizeCliArgs - TrainerCliArgs - VllmServeCliArgs Module for axolotl CLI command arguments. Dataclass with CLI arguments for axolotl evaluate command. Dataclass with CLI arguments for axolotl inference command. Dataclass with CLI arguments for axolotl preprocess command. Dataclass with CLI arguments for axolotl quantize command. Dataclass with CLI arguments for axolotl train command. Dataclass with CLI arguments for axolotl vllm-serve command. **Examples:** Example 1 (python): ```python cli.args.EvaluateCliArgs( debug=False, debug_text_only=False, debug_num_examples=0, ) ``` Example 2 (python): ```python cli.args.InferenceCliArgs(prompter=None) ``` Example 3 (python): ```python cli.args.PreprocessCliArgs( debug=False, debug_text_only=False, debug_num_examples=1, prompter=None, download=True, iterable=False, ) ``` Example 4 (python): ```python cli.args.QuantizeCliArgs( base_model=None, weight_dtype=None, activation_dtype=None, quantize_embedding=None, group_size=None, output_dir=None, hub_model_id=None, ) ``` --- ## common.architectures **URL:** https://docs.axolotl.ai/docs/api/common.architectures.html **Contents:** - common.architectures Common architecture specific constants --- ## cli.merge_sharded_fsdp_weights **URL:** https://docs.axolotl.ai/docs/api/cli.merge_sharded_fsdp_weights.html **Contents:** - cli.merge_sharded_fsdp_weights - Classes - BFloat16CastPlanner - Functions - do_cli - Parameters - merge_fsdp_weights - Parameters - Raises cli.merge_sharded_fsdp_weights CLI to merge sharded FSDP model checkpoints into a single combined checkpoint. A custom planner to cast tensors to bfloat16 on the fly during loading. Parses axolotl config, CLI args, and calls merge_fsdp_weights. Merge the weights from sharded FSDP model checkpoints into a single combined checkpoint. Should be used if SHARDED_STATE_DICT was used for the model. Weights will be saved to {output_path}/model.safetensors if safe_serialization else pytorch_model.bin. Note: this is a CPU-bound process. **Examples:** Example 1 (python): ```python cli.merge_sharded_fsdp_weights.BFloat16CastPlanner() ``` Example 2 (python): ```python cli.merge_sharded_fsdp_weights.do_cli(config=Path('examples/'), **kwargs) ``` Example 3 (python): ```python cli.merge_sharded_fsdp_weights.merge_fsdp_weights( checkpoint_dir, output_path, safe_serialization=False, remove_checkpoint_dir=False, ) ``` --- ## utils.data.streaming **URL:** https://docs.axolotl.ai/docs/api/utils.data.streaming.html **Contents:** - utils.data.streaming Data handling specific to streaming datasets. --- ## core.chat.format.chatml **URL:** https://docs.axolotl.ai/docs/api/core.chat.format.chatml.html **Contents:** - core.chat.format.chatml core.chat.format.chatml ChatML transformation functions for MessageContents --- ## prompt_strategies.kto.chatml **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.kto.chatml.html **Contents:** - prompt_strategies.kto.chatml - Functions - argilla_chat - intel - ultra prompt_strategies.kto.chatml KTO strategies for chatml for argilla/kto-mix-15k conversations For Intel Orca KTO ex: argilla/distilabel-intel-orca-kto for ultrafeedback binarized conversations ex: argilla/ultrafeedback-binarized-preferences-cleaned-kto **Examples:** Example 1 (python): ```python prompt_strategies.kto.chatml.argilla_chat(cfg, **kwargs) ``` Example 2 (python): ```python prompt_strategies.kto.chatml.intel(cfg, **kwargs) ``` Example 3 (python): ```python prompt_strategies.kto.chatml.ultra(cfg, **kwargs) ``` --- ## utils.schemas.trl **URL:** https://docs.axolotl.ai/docs/api/utils.schemas.trl.html **Contents:** - utils.schemas.trl - Classes - TRLConfig Pydantic models for TRL trainer configuration **Examples:** Example 1 (python): ```python utils.schemas.trl.TRLConfig() ``` --- ## monkeypatch.llama_attn_hijack_xformers **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.llama_attn_hijack_xformers.html **Contents:** - monkeypatch.llama_attn_hijack_xformers monkeypatch.llama_attn_hijack_xformers Directly copied the code from https://raw.githubusercontent.com/oobabooga/text-generation-webui/main/modules/llama_attn_hijack.py and made some adjustments --- ## kernels.geglu **URL:** https://docs.axolotl.ai/docs/api/kernels.geglu.html **Contents:** - kernels.geglu - Functions - geglu_backward - Parameters - Returns - Note - geglu_forward - Parameters - Returns Module for definition of GEGLU Triton kernels. See “GLU Variants Improve Transformer” (https://arxiv.org/abs/2002.05202). Credit to unsloth (https://unsloth.ai/) for inspiration for this implementation. GEGLU backward pass using in-place operations. This function modifies its input tensors in-place to store results. **Examples:** Example 1 (python): ```python kernels.geglu.geglu_backward(grad_output, gate, up) ``` Example 2 (python): ```python kernels.geglu.geglu_forward(gate, up) ``` --- ## utils.callbacks.profiler **URL:** https://docs.axolotl.ai/docs/api/utils.callbacks.profiler.html **Contents:** - utils.callbacks.profiler - Classes - PytorchProfilerCallback utils.callbacks.profiler HF Trainer callback for creating pytorch profiling snapshots PyTorch Profiler callback to create snapshots of GPU memory usage at specified steps. **Examples:** Example 1 (python): ```python utils.callbacks.profiler.PytorchProfilerCallback( steps_to_profile=5, profiler_steps_start=0, ) ``` --- ## kernels.lora **URL:** https://docs.axolotl.ai/docs/api/kernels.lora.html **Contents:** - kernels.lora - Classes - LoRA_MLP - Methods - backward - Parameters - Returns - forward - Parameters - Returns Module for definition of Low-Rank Adaptation (LoRA) Triton kernels. See “LoRA: Low-Rank Adaptation of Large Language Models” (https://arxiv.org/abs/2106.09685). Credit to unsloth (https://unsloth.ai/) for inspiration for this implementation. Optimized LoRA MLP implementation. Performs backward pass computation for LoRA MLP. Forward pass for LoRA MLP. Optimized LoRA implementation for output projection. Backward pass computing gradients for LoRA output projection. Forward pass for output projection with LoRA. Optimized LoRA QKV implementation with quantization support. Implements efficient computation of query, key, value projections with LoRA, supporting quantization and memory optimization. Backward pass computing gradients for LoRA QKV. Forward pass computing Q, K, V projections with LoRA. Applies LoRA to MLP layer with GEGLU activation. Applies LoRA to MLP layer with SwiGLU activation. Applies LoRA to output projection layer. Applies LoRA to compute Query, Key, Value projections. Gets LoRA parameters from a projection module. Efficient fused matmul + LoRA computation. **Examples:** Example 1 (python): ```python kernels.lora.LoRA_MLP() ``` Example 2 (python): ```python kernels.lora.LoRA_MLP.backward(ctx, grad_output) ``` Example 3 (python): ```python kernels.lora.LoRA_MLP.forward( ctx, X, gate_weight, gate_bias, gate_quant, gate_A, gate_B, gate_scale, up_weight, up_bias, up_quant, up_A, up_B, up_scale, down_weight, down_bias, down_quant, down_A, down_B, down_scale, activation_fn, activation_fn_backward, inplace=True, ) ``` Example 4 (python): ```python kernels.lora.LoRA_O() ``` --- ## monkeypatch.trainer_fsdp_optim **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.trainer_fsdp_optim.html **Contents:** - monkeypatch.trainer_fsdp_optim - Functions - patch_training_loop_for_fsdp monkeypatch.trainer_fsdp_optim fix for FSDP optimizer save in trainer w 4.47.0 monkeypatch for fixing the training loop for fsdp with optimizer save **Examples:** Example 1 (python): ```python monkeypatch.trainer_fsdp_optim.patch_training_loop_for_fsdp() ``` --- ## utils.schemas.multimodal **URL:** https://docs.axolotl.ai/docs/api/utils.schemas.multimodal.html **Contents:** - utils.schemas.multimodal - Classes - MultiModalConfig - Methods - convert_image_resize_algorithm utils.schemas.multimodal Pydantic models for multimodal-related configuration Multi-modal configuration subset Convert the image resize algorithm to a PIL.Image.Resampling enum. **Examples:** Example 1 (python): ```python utils.schemas.multimodal.MultiModalConfig() ``` Example 2 (python): ```python utils.schemas.multimodal.MultiModalConfig.convert_image_resize_algorithm( image_resize_algorithm, ) ``` --- ## prompt_strategies.dpo.llama3 **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.dpo.llama3.html **Contents:** - prompt_strategies.dpo.llama3 - Functions - argilla_chat - icr - intel - ultra prompt_strategies.dpo.llama3 DPO strategies for llama-3 chat template for argilla/dpo-mix-7k conversations chatml transforms for datasets with system, input, chosen, rejected ex. https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs For Intel Orca DPO Pairs for ultrafeedback binarized conversations **Examples:** Example 1 (python): ```python prompt_strategies.dpo.llama3.argilla_chat(cfg, **kwargs) ``` Example 2 (python): ```python prompt_strategies.dpo.llama3.icr(cfg, **kwargs) ``` Example 3 (python): ```python prompt_strategies.dpo.llama3.intel(cfg, **kwargs) ``` Example 4 (python): ```python prompt_strategies.dpo.llama3.ultra(cfg, **kwargs) ``` --- ## core.chat.format.shared **URL:** https://docs.axolotl.ai/docs/api/core.chat.format.shared.html **Contents:** - core.chat.format.shared core.chat.format.shared shared functions for format transforms --- ## monkeypatch.llama_expand_mask **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.llama_expand_mask.html **Contents:** - monkeypatch.llama_expand_mask monkeypatch.llama_expand_mask expands the binary attention mask per 3.2.2 of https://arxiv.org/pdf/2107.02027.pdf --- ## core.chat.messages **URL:** https://docs.axolotl.ai/docs/api/core.chat.messages.html **Contents:** - core.chat.messages - Classes - ChatFormattedChats - Chats - MessageContentTypes - MessageContents - MessageRoles - Messages - PreferenceChats - SpecialToken internal message representations of chat messages Chat formatted chats with formatter and optional train on inputs top level data structure for chat conversations Message content types for text, image, audio, tool calls, and tool responses Message contents with type, value, metadata, weight, newline, and end of contents Message roles for the system, user, assistant, and tools Messages with role, content, metadata, weight, and chat formatting representation for preference data for chat Special tokens for beginning of string and end of string Tool with description, function, and parameters Tool call contents with name, arguments, and optional id Tool call function with name and arguments Tool response contents with name, content, and optional id **Examples:** Example 1 (python): ```python core.chat.messages.ChatFormattedChats() ``` Example 2 (python): ```python core.chat.messages.Chats() ``` Example 3 (python): ```python core.chat.messages.MessageContentTypes() ``` Example 4 (python): ```python core.chat.messages.MessageContents() ``` --- ## core.datasets.transforms.chat_builder **URL:** https://docs.axolotl.ai/docs/api/core.datasets.transforms.chat_builder.html **Contents:** - core.datasets.transforms.chat_builder - Functions - chat_message_transform_builder - Parameters - Returns core.datasets.transforms.chat_builder This module contains a function that builds a transform that takes a row from the dataset and converts it to a Chat. Builds a transform that takes a row from the dataset and converts it to a Chat **Examples:** Example 1 (python): ```python core.datasets.transforms.chat_builder.chat_message_transform_builder( train_on_inputs=False, conversations_field='messages', message_field_role=None, message_field_content=None, message_field_training=None, ) ``` --- ## utils.chat_templates **URL:** https://docs.axolotl.ai/docs/api/utils.chat_templates.html **Contents:** - utils.chat_templates This module provides functionality for selecting chat templates based on user choices. These templates are used for formatting messages in a conversation. --- ## core.trainers.dpo.trainer **URL:** https://docs.axolotl.ai/docs/api/core.trainers.dpo.trainer.html **Contents:** - core.trainers.dpo.trainer - Classes - AxolotlDPOTrainer - Methods - push_to_hub core.trainers.dpo.trainer DPO trainer for axolotl Extend the base DPOTrainer for axolotl helpers. Overwrite the push_to_hub method in order to force-add the tags when pushing the model on the Hub. Please refer to ~transformers.Trainer.push_to_hub for more details. **Examples:** Example 1 (python): ```python core.trainers.dpo.trainer.AxolotlDPOTrainer(*args, dataset_tags=None, **kwargs) ``` Example 2 (python): ```python core.trainers.dpo.trainer.AxolotlDPOTrainer.push_to_hub(*args, **kwargs) ``` --- ## monkeypatch.gradient_checkpointing.offload_disk **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.gradient_checkpointing.offload_disk.html **Contents:** - monkeypatch.gradient_checkpointing.offload_disk - Classes - Disco - Methods - backward - forward - get_instance - DiskOffloadManager - Methods - cleanup monkeypatch.gradient_checkpointing.offload_disk DISCO - DIsk-based Storage and Checkpointing with Optimized prefetching Disco: DIsk-based Storage and Checkpointing with Optimized prefetching Advanced disk-based gradient checkpointer with prefetching. Backward pass that loads activations from disk with prefetching Forward pass that offloads activations to disk asynchronously Get or create the offload manager Manages offloaded tensors and handles prefetching in a separate thread. Includes synchronization to prevent race conditions. Clean up all temp files and stop prefetch thread with proper synchronization Clean up a specific tensor file after it’s been used Load tensor from disk or prefetch cache with proper synchronization Save tensor to disk asynchronously and return file path with thread-safe operations Trigger prefetching of the next N tensors with proper synchronization Wait for a tensor to be saved to disk **Examples:** Example 1 (python): ```python monkeypatch.gradient_checkpointing.offload_disk.Disco() ``` Example 2 (python): ```python monkeypatch.gradient_checkpointing.offload_disk.Disco.backward( ctx, *grad_outputs, ) ``` Example 3 (python): ```python monkeypatch.gradient_checkpointing.offload_disk.Disco.forward( ctx, forward_function, hidden_states, *args, prefetch_size=1, prefetch_to_gpu=True, save_workers=4, ) ``` Example 4 (python): ```python monkeypatch.gradient_checkpointing.offload_disk.Disco.get_instance( prefetch_size=1, prefetch_to_gpu=True, save_workers=4, ) ``` --- ## utils.samplers.multipack **URL:** https://docs.axolotl.ai/docs/api/utils.samplers.multipack.html **Contents:** - utils.samplers.multipack - Classes - MultipackBatchSampler - Methods - efficiency - gather_efficiency - Returns - gather_len_batches - generate_batches - Parameters utils.samplers.multipack Multipack Batch Sampler - An efficient batch sampler for packing variable-length sequences into fixed-capacity batches to optimize memory usage and training throughput. Batch sampler class for efficient packing of variable-length sequences This sampler packs sequences into fixed-capacity bins (batches) to maximize GPU memory utilization and training throughput by reducing padding. It supports both parallel packing (using FFD algorithm) and sequential packing (preserving original sequence order). Calculate the packing efficiency (ratio of tokens used to total token slots). Higher is better - 1.0 would mean perfect packing with no wasted space. Gather and synchronize packing efficiency estimates across all distributed ranks. Gather and synchronize batch counts across all distributed ranks. Returns the minimum number of batches available on any rank. Generate packed batches for training. Set the epoch number, used for reproducible shuffling across epochs Sequential allocator that preserves example order. First-fit-decreasing bin packing algorithm check. Checks if sequences with the given lengths could fit in the specified number of bins. Pack a group of sequences into bins using First-Fit Decreasing algorithm. Pack sequences into bins using parallel processing. Returns: List of bins, where each bin contains indices of sequences assigned to it. **Examples:** Example 1 (python): ```python utils.samplers.multipack.MultipackBatchSampler( sampler, batch_size, batch_max_len, lengths, packing_efficiency_estimate=1.0, drop_last=True, num_count_samples=4, sequential=False, group_size=100000, bin_size=200, num_processes=None, safe_mode=True, mp_start_method='fork', **kwargs, ) ``` Example 2 (python): ```python utils.samplers.multipack.MultipackBatchSampler.efficiency() ``` Example 3 (python): ```python utils.samplers.multipack.MultipackBatchSampler.gather_efficiency() ``` Example 4 (python): ```python utils.samplers.multipack.MultipackBatchSampler.gather_len_batches(num) ``` --- ## core.trainers.mixins.scheduler **URL:** https://docs.axolotl.ai/docs/api/core.trainers.mixins.scheduler.html **Contents:** - core.trainers.mixins.scheduler - Classes - SchedulerMixin - Methods - create_scheduler - Parameters core.trainers.mixins.scheduler Module for Axolotl trainer scheduler mixin Mixin class for scheduler setup in CausalTrainer. Set up the scheduler. The optimizer of the trainer must have been set up either before this method is called or passed as an argument. **Examples:** Example 1 (python): ```python core.trainers.mixins.scheduler.SchedulerMixin() ``` Example 2 (python): ```python core.trainers.mixins.scheduler.SchedulerMixin.create_scheduler( num_training_steps, optimizer=None, ) ``` --- ## utils.collators.batching **URL:** https://docs.axolotl.ai/docs/api/utils.collators.batching.html **Contents:** - utils.collators.batching - Classes - BatchSamplerDataCollatorForSeq2Seq - DataCollatorForSeq2Seq - Parameters - PretrainingBatchSamplerDataCollatorForSeq2Seq - V2BatchSamplerDataCollatorForSeq2Seq utils.collators.batching Data collators for axolotl to pad labels and position_ids for packed sequences Collator for multipack specific to the using the BatchSampler Data collator that will dynamically pad the inputs received, as well as the labels and position_ids Collator for multipack specific to the using the BatchSampler Collator for multipack specific to the using the BatchSampler **Examples:** Example 1 (python): ```python utils.collators.batching.BatchSamplerDataCollatorForSeq2Seq( tokenizer, model=None, padding=True, max_length=None, pad_to_multiple_of=None, label_pad_token_id=-100, position_pad_token_id=0, return_tensors='pt', ) ``` Example 2 (python): ```python utils.collators.batching.DataCollatorForSeq2Seq( tokenizer, model=None, padding=True, max_length=None, pad_to_multiple_of=None, label_pad_token_id=-100, position_pad_token_id=0, return_tensors='pt', ) ``` Example 3 (python): ```python utils.collators.batching.PretrainingBatchSamplerDataCollatorForSeq2Seq( *args, multipack_attn=True, **kwargs, ) ``` Example 4 (python): ```python utils.collators.batching.V2BatchSamplerDataCollatorForSeq2Seq( tokenizer, model=None, padding=True, max_length=None, pad_to_multiple_of=None, label_pad_token_id=-100, position_pad_token_id=0, return_tensors='pt', squash_position_ids=False, ) ``` --- ## prompt_strategies.orcamini **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.orcamini.html **Contents:** - prompt_strategies.orcamini - Classes - OrcaMiniPrompter prompt_strategies.orcamini Prompt Strategy for finetuning Orca Mini (v2) models see also https://huggingface.co/psmathur/orca_mini_v2_7b for more information Use dataset type: orcamini in config.yml to use this prompt style. Compared to the alpaca_w_system.open_orca dataset type, this one specifies the system prompt with “### System:”. Not suited/tested for multiple-turn conversations without further adjustments. Adjusted Prompter for Orca Mini (v2) datasets **Examples:** Example 1 (python): ```python prompt_strategies.orcamini.OrcaMiniPrompter( prompt_style=PromptStyle.INSTRUCT.value, ) ``` --- ## prompt_strategies.dpo.chat_template **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.dpo.chat_template.html **Contents:** - prompt_strategies.dpo.chat_template - Functions - argilla_chat - Parameters - Returns - Dataset format prompt_strategies.dpo.chat_template DPO prompt strategies for using tokenizer chat templates. DPO chat template strategy for argilla-style datasets. For argilla-style datasets where chosen/rejected contain full conversations instead of single response messages. Extracts the conversation history from the chosen field and formats both chosen/rejected responses using the configured chat template. { “chosen”: [ {“role”: “user”, “content”: “…”}, {“role”: “assistant”, “content”: “…”} ], “rejected”: [ {“role”: “user”, “content”: “…”}, {“role”: “assistant”, “content”: “…”} ] } **Examples:** Example 1 (python): ```python prompt_strategies.dpo.chat_template.argilla_chat(cfg, dataset_idx=0, **kwargs) ``` --- ## monkeypatch.relora **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.relora.html **Contents:** - monkeypatch.relora - Classes - ReLoRACallback Implements the ReLoRA training procedure from https://arxiv.org/abs/2307.05695, minus the initial full fine-tune. Callback to merge LoRA weights into the base model and save full-weight checkpoints **Examples:** Example 1 (python): ```python monkeypatch.relora.ReLoRACallback(cfg) ``` --- ## monkeypatch.transformers_fa_utils **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.transformers_fa_utils.html **Contents:** - monkeypatch.transformers_fa_utils - Functions - fixed_fa_peft_integration_check - Parameters monkeypatch.transformers_fa_utils see https://github.com/huggingface/transformers/pull/35834 PEFT usually casts the layer norms in float32 for training stability reasons therefore the input hidden states gets silently casted in float32. Hence, we need cast them back in float16 / bfloat16 just to be sure everything works as expected. This might slowdown training & inference so it is recommended to not cast the LayerNorms! **Examples:** Example 1 (python): ```python monkeypatch.transformers_fa_utils.fixed_fa_peft_integration_check( query, key, value, target_dtype=None, preferred_dtype=None, ) ``` --- ## utils.collators.mm_chat **URL:** https://docs.axolotl.ai/docs/api/utils.collators.mm_chat.html **Contents:** - utils.collators.mm_chat - Classes - MultiModalChatDataCollator utils.collators.mm_chat Collators for multi-modal chat messages and packing Collator for multi-modal chat messages **Examples:** Example 1 (python): ```python utils.collators.mm_chat.MultiModalChatDataCollator( tokenizer, processing_strategy, packing=False, return_tensors='pt', padding=True, pad_to_multiple_of=None, ) ``` --- ## utils.lora **URL:** https://docs.axolotl.ai/docs/api/utils.lora.html **Contents:** - utils.lora - Functions - get_lora_merged_state_dict - Parameters - Returns module to get the state dict of a merged lora model Create and return a state_dict that has the LoRA deltas merged into the base model’s weights, without modifying model in place. **Examples:** Example 1 (python): ```python utils.lora.get_lora_merged_state_dict(model) ``` --- ## utils.model_shard_quant **URL:** https://docs.axolotl.ai/docs/api/utils.model_shard_quant.html **Contents:** - utils.model_shard_quant - Functions - load_and_quantize utils.model_shard_quant module to handle loading model on cpu/meta device for FSDP Loads value tensor into submodule of module, optionally skipping skip_names and converting to dtype. Quantizes Params4bit on device then places on “cpu” if to_cpu=True or “meta” if to_meta=True. **Examples:** Example 1 (python): ```python utils.model_shard_quant.load_and_quantize( module, name, value, device=None, dtype=None, skip_names=None, to_cpu=False, to_meta=False, verbose=False, quant_method='bnb', ) ``` --- ## monkeypatch.gradient_checkpointing.offload_cpu **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.gradient_checkpointing.offload_cpu.html **Contents:** - monkeypatch.gradient_checkpointing.offload_cpu - Classes - CPU_Offloaded_Gradient_Checkpointer monkeypatch.gradient_checkpointing.offload_cpu CPU offloaded checkpointing Saves VRAM by smartly offloading to RAM. Tiny hit to performance, since we mask the movement via non blocking calls. **Examples:** Example 1 (python): ```python monkeypatch.gradient_checkpointing.offload_cpu.CPU_Offloaded_Gradient_Checkpointer( ) ``` --- ## core.builders.base **URL:** https://docs.axolotl.ai/docs/api/core.builders.base.html **Contents:** - core.builders.base - Classes - TrainerBuilderBase - Methods - get_post_trainer_create_callbacks Base class for trainer builder Base class for trainer builder. Callbacks added after the trainer is created, usually b/c these need access to the trainer **Examples:** Example 1 (python): ```python core.builders.base.TrainerBuilderBase(cfg, model, tokenizer, processor=None) ``` Example 2 (python): ```python core.builders.base.TrainerBuilderBase.get_post_trainer_create_callbacks(trainer) ``` --- ## core.builders.rl **URL:** https://docs.axolotl.ai/docs/api/core.builders.rl.html **Contents:** - core.builders.rl - Classes - HFRLTrainerBuilder Builder for RLHF trainers Trainer factory class for TRL-based RLHF trainers (e.g. DPO) **Examples:** Example 1 (python): ```python core.builders.rl.HFRLTrainerBuilder(cfg, model, tokenizer, processor=None) ``` --- ## utils.schemas.integrations **URL:** https://docs.axolotl.ai/docs/api/utils.schemas.integrations.html **Contents:** - utils.schemas.integrations - Classes - CometConfig - GradioConfig - LISAConfig - MLFlowConfig - OpenTelemetryConfig - RayConfig - WandbConfig utils.schemas.integrations Pydantic models for Axolotl integrations Comet configuration subset Gradio configuration subset LISA configuration subset MLFlow configuration subset OpenTelemetry configuration subset Ray launcher configuration subset Wandb configuration subset **Examples:** Example 1 (python): ```python utils.schemas.integrations.CometConfig() ``` Example 2 (python): ```python utils.schemas.integrations.GradioConfig() ``` Example 3 (python): ```python utils.schemas.integrations.LISAConfig() ``` Example 4 (python): ```python utils.schemas.integrations.MLFlowConfig() ``` --- ## utils.data.sft **URL:** https://docs.axolotl.ai/docs/api/utils.data.sft.html **Contents:** - utils.data.sft - Functions - prepare_datasets - Parameters - Returns Data handling specific to SFT. Prepare training and evaluation datasets based on configuration. **Examples:** Example 1 (python): ```python utils.data.sft.prepare_datasets(cfg, tokenizer, processor=None) ``` --- ## integrations.liger.args **URL:** https://docs.axolotl.ai/docs/api/integrations.liger.args.html **Contents:** - integrations.liger.args - Classes - LigerArgs integrations.liger.args Module for handling LIGER input arguments. Input args for LIGER. **Examples:** Example 1 (python): ```python integrations.liger.args.LigerArgs() ``` --- ## monkeypatch.mixtral **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.mixtral.html **Contents:** - monkeypatch.mixtral Patches to support multipack for mixtral --- ## cli.preprocess **URL:** https://docs.axolotl.ai/docs/api/cli.preprocess.html **Contents:** - cli.preprocess - Functions - do_cli - Parameters - do_preprocess - Parameters CLI to run preprocessing of a dataset. Parses axolotl config, CLI args, and calls do_preprocess. Preprocesses dataset specified in axolotl config. **Examples:** Example 1 (python): ```python cli.preprocess.do_cli(config=Path('examples/'), **kwargs) ``` Example 2 (python): ```python cli.preprocess.do_preprocess(cfg, cli_args) ``` --- ## prompt_strategies.kto.llama3 **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.kto.llama3.html **Contents:** - prompt_strategies.kto.llama3 - Functions - argilla_chat - intel - ultra prompt_strategies.kto.llama3 KTO strategies for llama-3 chat template for argilla/kto-mix-15k conversations For Intel Orca KTO ex: argilla/distilabel-intel-orca-kto for ultrafeedback binarized conversations ex: argilla/ultrafeedback-binarized-preferences-cleaned-kto **Examples:** Example 1 (python): ```python prompt_strategies.kto.llama3.argilla_chat(cfg, **kwargs) ``` Example 2 (python): ```python prompt_strategies.kto.llama3.intel(cfg, **kwargs) ``` Example 3 (python): ```python prompt_strategies.kto.llama3.ultra(cfg, **kwargs) ``` --- ## prompt_strategies.orpo.chat_template **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.orpo.chat_template.html **Contents:** - prompt_strategies.orpo.chat_template - Classes - Message - MessageList - ORPODatasetParsingStrategy - Methods - get_chosen_conversation_thread - get_prompt - get_rejected_conversation_thread - ORPOPrompter prompt_strategies.orpo.chat_template chatml prompt tokenization strategy for ORPO Strategy to parse chosen rejected dataset into messagelist Dataset structure mappings Map the data to extract everything up to the last turn Dataset structure mappings Single Turn prompter for ORPO rejected_input_ids input_ids rejected_attention_mask attention_mask rejected_labels labels chatml transforms for datasets with system, input, chosen, rejected **Examples:** Example 1 (python): ```python prompt_strategies.orpo.chat_template.Message() ``` Example 2 (python): ```python prompt_strategies.orpo.chat_template.MessageList() ``` Example 3 (python): ```python prompt_strategies.orpo.chat_template.ORPODatasetParsingStrategy() ``` Example 4 (python): ```python prompt_strategies.orpo.chat_template.ORPODatasetParsingStrategy.get_chosen_conversation_thread( prompt, ) ``` --- ## loaders.processor **URL:** https://docs.axolotl.ai/docs/api/loaders.processor.html **Contents:** - loaders.processor Processor loading functionality for multi-modal models --- ## utils.callbacks.comet_ **URL:** https://docs.axolotl.ai/docs/api/utils.callbacks.comet_.html **Contents:** - utils.callbacks.comet_ - Classes - SaveAxolotlConfigtoCometCallback utils.callbacks.comet_ Comet module for trainer callbacks Callback to save axolotl config to comet **Examples:** Example 1 (python): ```python utils.callbacks.comet_.SaveAxolotlConfigtoCometCallback(axolotl_config_path) ``` ---