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Axolotl

Axolotl is a tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures.

Features:

<table> <tr> <td>

Table of Contents

</td> <td> <div align="center"> <img src="image/axolotl.png" alt="axolotl" width="160"> <div> <p> <b>Axolotl provides a unified repository for fine-tuning <br />a variety of AI models with ease</b> </p> <p> Go ahead and Axolotl questions!! </p> <img src="https://github.com/OpenAccess-AI-Collective/axolotl/actions/workflows/pre-commit.yml/badge.svg?branch=main" alt="pre-commit"> <img alt="PyTest Status" src="https://github.com/OpenAccess-AI-Collective/axolotl/actions/workflows/tests.yml/badge.svg?branch=main"> </div> </div> </td> </tr> </table>

Axolotl supports

fp16/fp32loraqloragptqgptq w/flash attnflash attnxformers attn
llama✅✅✅✅✅✅✅
Pythia✅✅✅❌❌❌❓
cerebras✅✅✅❌❌❌❓
btlm✅✅✅❌❌❌❓
mpt✅❌❓❌❌❌❓
falcon✅✅✅❌❌❌❓
gpt-j✅✅✅❌❌❓❓
XGen✅❓✅❓❓❓✅
phi✅✅✅❓❓❓❓

Quickstart ⚡

Get started with Axolotl in just a few steps! This quickstart guide will walk you through setting up and running a basic fine-tuning task.

Requirements: Python >=3.9 and Pytorch >=2.0.

git clone https://github.com/OpenAccess-AI-Collective/axolotl
cd axolotl

pip3 install packaging
pip3 install -e '.[flash-attn,deepspeed]'
pip3 install -U git+https://github.com/huggingface/peft.git

# finetune lora
accelerate launch -m axolotl.cli.train examples/openllama-3b/lora.yml

# inference
accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
    --lora_model_dir="./lora-out"

Installation

Environment

Docker

docker run --gpus '"all"' --rm -it winglian/axolotl:main-py3.10-cu118-2.0.1

Or run on the current files for development:

docker compose up -d
<details> <summary>Docker advanced</summary>

A more powerful Docker command to run would be this:

docker run --gpus '"all"' --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=volume,src=axolotl,target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-py3.10-cu118-2.0.1

It additionally:

More information on nvidia website

</details>

Conda/Pip venv

  1. Install python >=3.9

  2. Install pytorch stable https://pytorch.org/get-started/locally/

  3. Install Axolotl along with python dependencies

    pip3 install packaging
pip3 install -e '.[flash-attn,deepspeed]'

  4. (Optional) Login to Huggingface to use gated models/datasets.

    huggingface-cli login

    Get the token at huggingface.co/settings/tokens

LambdaLabs

<details> <summary>Click to Expand</summary>
  1. Install python
sudo apt update
sudo apt install -y python3.9

sudo update-alternatives --install /usr/bin/python python /usr/bin/python3.9 1
sudo update-alternatives --config python # pick 3.9 if given option
python -V # should be 3.9
  1. Install pip
wget https://bootstrap.pypa.io/get-pip.py
python get-pip.py
  1. Install torch
pip3 install -U torch --index-url https://download.pytorch.org/whl/cu118
  1. Axolotl
git clone https://github.com/OpenAccess-AI-Collective/axolotl
cd axolotl

pip3 install packaging
pip3 install -e '.[flash-attn,deepspeed]'
pip3 install protobuf==3.20.3
pip3 install -U --ignore-installed requests Pillow psutil scipy
  1. Set path
export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH
</details>

Windows

Please use WSL or Docker!

Dataset

Axolotl supports a variety of dataset formats. Below are some of the formats you can use. Have dataset(s) in one of the following format (JSONL recommended):

<details> <summary>See other formats</summary> </details>

How to add custom prompts

For a dataset that is preprocessed for instruction purposes:

{"instruction": "...", "output": "..."}

You can use this example in your YAML config:

datasets:
  - path: repo
    type:
      system_prompt: ""
      field_system: system
      format: "[INST] {instruction} [/INST]"
      no_input_format: "[INST] {instruction} [/INST]"

How to use your custom pretokenized dataset

Config

See examples for quick start. It is recommended to duplicate and modify to your needs. The most important options are:

<details> <summary>All yaml options (click me)</summary> 
# This is the huggingface model that contains *.pt, *.safetensors, or *.bin files
# This can also be a relative path to a model on disk
base_model: ./llama-7b-hf
# You can specify an ignore pattern if the model repo contains more than 1 model type (*.pt, etc)
base_model_ignore_patterns:
# If the base_model repo on hf hub doesn't include configuration .json files,
# You can set that here, or leave this empty to default to base_model
base_model_config: ./llama-7b-hf
# You can specify to choose a specific model revision from huggingface hub
model_revision:
# Optional tokenizer configuration override in case you want to use a different tokenizer
# than the one defined in the base model
tokenizer_config:
# If you want to specify the type of model to load, AutoModelForCausalLM is a good choice too
model_type: AutoModelForCausalLM
# Corresponding tokenizer for the model AutoTokenizer is a good choice
tokenizer_type: AutoTokenizer
# Trust remote code for untrusted source
trust_remote_code:
# use_fast option for tokenizer loading from_pretrained, default to True
tokenizer_use_fast:
# Whether to use the legacy tokenizer setting, defaults to True
tokenizer_legacy:
# Resize the model embeddings when new tokens are added to multiples of 32
# This is reported to improve training speed on some models
resize_token_embeddings_to_32x:

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

# Whether you are training a 4-bit GPTQ quantized model
gptq: true
gptq_groupsize: 128 # group size
gptq_model_v1: false # v1 or v2

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

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

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

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

    # Optional[str] fastchat conversation type, only used with type: sharegpt
    conversation:  # Options (see Conversation 'name'): https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py

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

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

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

# Axolotl attempts to save the dataset as an arrow after packing the data together so
# subsequent training attempts load faster, relative path
dataset_prepared_path: data/last_run_prepared
# Push prepared dataset to hub
push_dataset_to_hub: # repo path
# The maximum number of processes to use while preprocessing your input dataset. This defaults to `os.cpu_count()`
# if not set.
dataset_processes: # defaults to os.cpu_count() if not set
# push checkpoints to hub
hub_model_id: # repo path to push finetuned model
# how to push checkpoints to hub
# https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.hub_strategy
hub_strategy:
# Whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets
# Required to be true when used in combination with `push_dataset_to_hub`
hf_use_auth_token: # boolean
# How much of the dataset to set aside as evaluation. 1 = 100%, 0.50 = 50%, etc. 0 for no eval.
val_set_size: 0.04
# Num shards for whole dataset
dataset_shard_num:
# Index of shard to use for whole dataset
dataset_shard_idx:

# The maximum length of an input to train with, this should typically be less than 2048
# as most models have a token/context limit of 2048
sequence_len: 2048
# Pad inputs so each step uses constant sized buffers
# This will reduce memory fragmentation and may prevent OOMs, by re-using memory more efficiently
pad_to_sequence_len:
# Max sequence length to concatenate training samples together up to
# Inspired by StackLLaMA. see https://huggingface.co/blog/stackllama#supervised-fine-tuning
# FutureWarning: This will soon be DEPRECATED
max_packed_sequence_len: 1024
# Use efficient multi-packing with block diagonal attention and per sequence position_ids. Recommend set to 'true'
sample_packing:
# Set to 'false' if getting errors during eval with sample_packing on.
eval_sample_packing:
# You can set these packing optimizations AFTER starting a training at least once.
# The trainer will provide recommended values for these values.
sample_packing_eff_est:
total_num_tokens:

# If you want to use 'lora' or 'qlora' or leave blank to train all parameters in original model
adapter: lora
# If you already have a lora model trained that you want to load, put that here.
# This means after training, if you want to test the model, you should set this to the value of `lora_out_dir`.
lora_model_dir:

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

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

# Once you complete training, the model will be saved to the following directory.
# If you merge the adapter to the base model, a subdirectory `merged` will be created under this directory.
# Make sure `lora_model_dir` points to this directory if you want to use the trained model.
lora_out_dir:
lora_fan_in_fan_out: false

# ReLoRA configuration
# Must use either 'lora' or 'qlora' adapter, and does not support fsdp or deepspeed
relora_steps: # Number of steps per ReLoRA restart
relora_warmup_steps: # Number of per-restart warmup steps
relora_cpu_offload: # True to perform lora weight merges on cpu during restarts, for modest gpu memory savings

# wandb configuration if you're using it
wandb_mode: # "offline" to save run metadata locally and not sync to the server, "disabled" to turn off wandb
wandb_project: # Your wandb project name
wandb_entity: # A wandb Team name if using a Team
wandb_watch:
wandb_run_id: # Set the name of your wandb run
wandb_log_model: # "checkpoint" to log model to wandb Artifacts every `save_steps` or "end" to log only at the end of training

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

# Whether to use torch.compile and which backend to use
torch_compile:  # bool
torch_compile_backend:  # Optional[str]

# Training hyperparameters

# If greater than 1, backpropagation will be skipped and the gradients will be accumulated for the given number of steps.
gradient_accumulation_steps: 1
# The number of samples to include in each batch. This is the number of samples sent to each GPU.
micro_batch_size: 2
eval_batch_size:
num_epochs: 4
warmup_steps: 100
learning_rate: 0.00003
lr_quadratic_warmup:
logging_steps:
save_strategy: # Set to `no` to skip checkpoint saves
save_steps: # Leave empty to save at each epoch
eval_steps: # Leave empty to eval at each epoch, integers for every N steps. decimal for fraction of total steps
save_total_limit: # Checkpoints saved at a time
# Maximum number of iterations to train for. It precedes num_epochs which means that
# if both are set, num_epochs will not be guaranteed.
# e.g., when 1 epoch is 1000 steps => `num_epochs: 2` and `max_steps: 100` will train for 100 steps
max_steps:

eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
eval_table_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128

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

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

# Whether to use gradient checkpointing https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing
gradient_checkpointing: false

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

# Specify a scheduler and kwargs to use with the optimizer
lr_scheduler: # 'one_cycle' | 'log_sweep' | empty for cosine
lr_scheduler_kwargs:

# For one_cycle optim
lr_div_factor: # Learning rate div factor

# For log_sweep optim
log_sweep_min_lr:
log_sweep_max_lr:

# Specify optimizer
# Valid values are driven by the Transformers OptimizerNames class, see:
# https://github.com/huggingface/transformers/blob/95b374952dc27d8511541d6f5a4e22c9ec11fb24/src/transformers/training_args.py#L134
#
# Note that not all optimizers may be available in your environment, ex: 'adamw_anyprecision' is part of
# torchdistx, 'adamw_bnb_8bit' is part of bnb.optim.Adam8bit, etc. When in doubt, it is recommended to start with the optimizer used
# in the examples/ for your model and fine-tuning use case.
#
# Valid values for 'optimizer' include:
# - adamw_hf
# - adamw_torch
# - adamw_torch_fused
# - adamw_torch_xla
# - adamw_apex_fused
# - adafactor
# - adamw_anyprecision
# - sgd
# - adagrad
# - adamw_bnb_8bit
# - lion_8bit
# - lion_32bit
# - paged_adamw_32bit
# - paged_adamw_8bit
# - paged_lion_32bit
# - paged_lion_8bit
optimizer:
# Specify weight decay
weight_decay:
# adamw hyperparams
adam_beta1:
adam_beta2:
adam_epsilon:
# Gradient clipping max norm
max_grad_norm:

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

# Whether to bettertransformers
flash_optimum:
# Whether to use xformers attention patch https://github.com/facebookresearch/xformers:
xformers_attention:
# Whether to use flash attention patch https://github.com/Dao-AILab/flash-attention:
flash_attention:
flash_attn_cross_entropy:  # Whether to use flash-attention cross entropy implementation - advanced use only
flash_attn_rms_norm:  # Whether to use flash-attention rms norm implementation - advanced use only
flash_attn_fuse_qkv: # Whether to fuse QKV into a single operation
flash_attn_fuse_mlp: # Whether to fuse part of the MLP into a single operation
# Whether to use scaled-dot-product attention
# https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
sdp_attention:
# Landmark attention (only llama)
landmark_attention:
# xpos RoPE see https://github.com/kaiokendev/cutoff-len-is-context-len/blob/main/util/xpos_rope_llama_monkey_patch.py
# LLaMA only
xpos_rope:
# RoPE Scaling https://github.com/huggingface/transformers/pull/24653
rope_scaling:
  type: # linear | dynamic
  factor: # float

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

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

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

# Add extra tokens.
tokens:

# FSDP
fsdp:
fsdp_config:

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

# Advanced DDP Arguments
ddp_timeout:
ddp_bucket_cap_mb:
ddp_broadcast_buffers:

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

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

# Debug mode
debug:

# Seed
seed:

# Allow overwrite yml config using from cli
strict:
</details> <details> <summary> Understanding of batch size and gradient accumulation steps </summary> <br/> Gradient accumulation means accumulating gradients over several mini-batches and updating the model weights afterward. When the samples in each batch are diverse, this technique doesn't significantly impact learning.

This method allows for effective training with larger effective batch sizes without needing proportionally larger memory. Here's why:

  1. Memory Consumption with Batch Size: The primary reason increasing the batch size impacts memory is due to the storage requirements for intermediate activations. When you forward propagate a batch through a network, you have to store the activations at each layer for each sample in the batch, because these activations are used during backpropagation to compute gradients. Therefore, larger batches mean more activations, leading to greater GPU memory consumption.

  2. Gradient Accumulation: With gradient accumulation, you're effectively simulating a larger batch size by accumulating gradients over several smaller batches (or micro-batches). However, at any given time, you're only forward and backward propagating a micro-batch. This means you only store activations for the micro-batch, not the full accumulated batch. As a result, you can simulate the effect of a larger batch size without the memory cost of storing activations for a large batch.

Example 1: Micro batch size: 3 Gradient accumulation steps: 2 Number of GPUs: 3 Total batch size = 3 * 2 * 3 = 18

| GPU 1          | GPU 2          | GPU 3          |
|----------------|----------------|----------------|
| S1, S2, S3     | S4, S5, S6     | S7, S8, S9     |
| e1, e2, e3     | e4, e5, e6     | e7, e8, e9     |
|----------------|----------------|----------------|
| → (accumulate) | → (accumulate) | → (accumulate) |
|----------------|----------------|----------------|
| S10, S11, S12  | S13, S14, S15  | S16, S17, S18  |
| e10, e11, e12  | e13, e14, e15  | e16, e17, e18  |
|----------------|----------------|----------------|
| → (apply)      | → (apply)      | → (apply)      |

Accumulated gradient for the weight w1 after the second iteration (considering all GPUs):
Total gradient for w1 = e1 + e2 + e3 + e4 + e5 + e6 + e7 + e8 + e9 + e10 + e11 + e12 + e13 + e14 + e15 + e16 + e17 + e18

Weight update for w1:
w1_new = w1_old - learning rate x (Total gradient for w1 / 18)

Example 2: Micro batch size: 2 Gradient accumulation steps: 1 Number of GPUs: 3 Total batch size = 2 * 1 * 3 = 6

| GPU 1     | GPU 2     | GPU 3     |
|-----------|-----------|-----------|
| S1, S2    | S3, S4    | S5, S6    |
| e1, e2    | e3, e4    | e5, e6    |
|-----------|-----------|-----------|
| → (apply) | → (apply) | → (apply) |

Accumulated gradient for the weight w1 (considering all GPUs):
Total gradient for w1 = e1 + e2 + e3 + e4 + e5 + e6

Weight update for w1:
w1_new = w1_old - learning rate × (Total gradient for w1 / 6)
</details>

Train

Run

accelerate launch -m axolotl.cli.train your_config.yml

Preprocess dataset

You can optionally pre-tokenize dataset with the following before finetuning. This is recommended for large datasets.

python -m axolotl.cli.preprocess your_config.yml

Multi-GPU

Below are the options available in axolotl for training with multiple GPUs. Note that DeepSpeed is the recommended multi-GPU option currently because FSDP may experience loss instability.

DeepSpeed

Deepspeed is an optimization suite for multi-gpu systems allowing you to train much larger models than you might typically be able to fit into your GPU's VRAM. More information about the various optimization types for deepspeed is available at https://huggingface.co/docs/accelerate/main/en/usage_guides/deepspeed#what-is-integrated

We provide several default deepspeed JSON configurations for ZeRO stage 1, 2, and 3.

deepspeed: deepspeed/zero1.json

accelerate launch -m axolotl.cli.train examples/llama-2/config.py --deepspeed deepspeed/zero1.json
FSDP
fsdp:
  - full_shard
  - auto_wrap
fsdp_config:
  fsdp_offload_params: true
  fsdp_state_dict_type: FULL_STATE_DICT
  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
Weights & Biases Logging
wandb_mode:
wandb_project:
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:

Inference

Pass the appropriate flag to the train command:

Please use --sample_packing False if you have it on and receive the error similar to below:

RuntimeError: stack expects each tensor to be equal size, but got [1, 32, 1, 128] at entry 0 and [1, 32, 8, 128] at entry 1

Merge LORA to base

Add below flag to train command above

python3 -m axolotl.cli.merge_lora examples/your_config.yml --lora_model_dir="./completed-model" --load_in_8bit=False --load_in_4bit=False

If you run out of CUDA memory, you can try to merge in system RAM with

CUDA_VISIBLE_DEVICES="" python3 -m axolotl.cli.merge_lora ...

Common Errors 🧰

See also the FAQ's.

If you encounter a 'Cuda out of memory' error, it means your GPU ran out of memory during the training process. Here's how to resolve it:

Please reduce any below

failed (exitcode: -9)

Usually means your system has run out of system memory. Similarly, you should consider reducing the same settings as when you run out of VRAM. Additionally, look into upgrading your system RAM which should be simpler than GPU upgrades.

RuntimeError: expected scalar type Float but found Half

Try set fp16: true

NotImplementedError: No operator found for memory_efficient_attention_forward ...

Try to turn off xformers.

accelerate config missing

It's safe to ignore it.

NCCL Timeouts during training

See the NCCL guide.

Need help? 🙋♂️

Join our Discord server where we can help you

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Community Showcase

Check out some of the projects and models that have been built using Axolotl! Have a model you'd like to add to our Community Showcase? Open a PR with your model.

Open Access AI Collective

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Contributing 🤝

Please read the contributing guide

Bugs? Please check the open issues else create a new Issue.

PRs are greatly welcome!

Please run below to setup env

pip3 install -r requirements-dev.txt -r requirements-tests.txt
pre-commit install

# test
pytest tests/