fp16/fp32 | fp16/fp32 w/ lora | qlora | 4bit-quant | 4bit-quant w/flash attention | flash attention | xformers attention | |
---|---|---|---|---|---|---|---|
llama | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
Pythia | ✅ | ✅ | ❓ | ❌ | ❌ | ❌ | ❓ |
cerebras | ✅ | ✅ | ❓ | ❌ | ❌ | ❌ | ❓ |
mpt | ✅ | ❌ | ❓ | ❌ | ❌ | ❌ | ❓ |
falcon | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❓ |
Requirements: Python 3.9.
git clone https://github.com/OpenAccess-AI-Collective/axolotl
pip3 install -e .[int4]
accelerate config
# finetune lora
accelerate launch scripts/finetune.py examples/lora-openllama-3b/config.yml
# inference
accelerate launch scripts/finetune.py examples/lora-openllama-3b/config.yml \
--inference --lora_model_dir="./lora-out"
-
Docker
docker run --gpus '"all"' --rm -it winglian/axolotl:main
winglian/axolotl:dev
: dev branchwinglian/axolotl-runpod:main
: for runpod
-
Conda/Pip venv
-
Install python 3.9
-
Install python dependencies with ONE of the following:
pip3 install -e .[int4]
(recommended)pip3 install -e .[int4_triton]
pip3 install -e .
-
Have dataset(s) in one of the following format (JSONL recommended):
alpaca
: instruction; input(optional){"instruction": "...", "input": "...", "output": "..."}
sharegpt
: conversations{"conversations": [{"from": "...", "value": "..."}]}
completion
: raw corpus{"text": "..."}
See other formats
jeopardy
: question and answer{"question": "...", "category": "...", "answer": "..."}
oasst
: instruction{"INSTRUCTION": "...", "RESPONSE": "..."}
gpteacher
: instruction; input(optional){"instruction": "...", "input": "...", "response": "..."}
reflection
: instruction with reflect; input(optional){"instruction": "...", "input": "...", "output": "...", "reflection": "...", "corrected": "..."}
explainchoice
: question, choices, (solution OR explanation){"question": "...", "choices": ["..."], "solution": "...", "explanation": "..."}
concisechoice
: question, choices, (solution OR explanation){"question": "...", "choices": ["..."], "solution": "...", "explanation": "..."}
summarizetldr
: article and summary{"article": "...", "summary": "..."}
Have some new format to propose? Check if it's already defined in data.py in
dev
branch!
Optionally, download some datasets, see data/README.md
See sample configs in configs folder or examples for quick start. It is recommended to duplicate and modify to your needs. The most important options are:
-
model
base_model: ./llama-7b-hf # local or huggingface repo
Note: The code will load the right architecture.
-
dataset
datasets: - path: vicgalle/alpaca-gpt4 # local or huggingface repo type: alpaca # format from earlier sequence_len: 2048 # max token length / prompt
-
loading
load_in_4bit: true load_in_8bit: true bf16: true # require >=ampere fp16: true tf32: true # require >=ampere
Note: Repo does not do 4-bit quantization.
-
lora
adapter: lora # qlora or leave blank for full finetune lora_r: 8 lora_alpha: 16 lora_dropout: 0.05 lora_target_modules: - q_proj - v_proj
All yaml options
# this is the huggingface model that contains *.pt, *.safetensors, or *.bin files
# this can also be a relative path to a model on disk
base_model: ./llama-7b-hf
# you can specify an ignore pattern if the model repo contains more than 1 model type (*.pt, etc)
base_model_ignore_patterns:
# if the base_model repo on hf hub doesn't include configuration .json files,
# you can set that here, or leave this empty to default to base_model
base_model_config: ./llama-7b-hf
# 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:
# 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
# a list of one or more datasets to finetune the model with
datasets:
# this can be either a hf dataset, or relative path
- path: vicgalle/alpaca-gpt4
# The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
type: alpaca # format OR format:prompt_style (chat/instruct)
data_files: # path to source data files
shards: # number of shards to split data into
# 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
# 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
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
# max sequence length to concatenate training samples together up to
# inspired by StackLLaMA. see https://huggingface.co/blog/stackllama#supervised-fine-tuning
max_packed_sequence_len: 1024
# 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
# lora hyperparameters
lora_model_dir:
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
lora_modules_to_save:
# - embed_tokens
# - lm_head
lora_out_dir:
lora_fan_in_fan_out: false
# wandb configuration if you're using it
wandb_mode:
wandb_project:
wandb_watch:
wandb_run_id:
wandb_log_model: # 'checkpoint'
# where to save the finished model to
output_dir: ./completed-model
# training hyperparameters
batch_size: 8
micro_batch_size: 2
eval_batch_size: 2
num_epochs: 3
warmup_steps: 100
learning_rate: 0.00003
logging_steps:
# whether to mask out or include the human's prompt from the training labels
train_on_inputs: false
# don't use this, leads to wonky training (according to someone on the internet)
group_by_length: false
# does not work with current implementation of 4-bit LoRA
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
optimizer:
# specify weight decay
weight_decay:
# whether to use xformers attention patch https://github.com/facebookresearch/xformers:
xformers_attention:
# whether to use flash attention patch https://github.com/HazyResearch/flash-attention:
flash_attention: # require a100 for llama
# 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
special_tokens:
# bos_token: "<s>"
# eos_token: "</s>"
# unk_token: "<unk>"
# add extra tokens
tokens:
# FSDP
fsdp:
fsdp_config:
# Deepspeed
deepspeed:
# Path to torch distx for optim 'adamw_anyprecision'
torchdistx_path:
# Set padding for data collator to 'longest'
collator_pad_to_longest:
# Debug mode
debug:
# Seed
seed:
# Allow overwrite yml config using from cli
strict:
Configure accelerate
accelerate config
# Edit manually
# nano ~/.cache/huggingface/accelerate/default_config.yaml
Run
accelerate launch scripts/finetune.py configs/your_config.yml
Pass the appropriate flag to the train command:
- Pretrained LORA:
--inference --lora_model_dir ./completed-model
- Full weights finetune:
--inference --base_model ./completed-model
Add below flag to train command above
--merge_lora --lora_model_dir="./completed-model" --load_in_8bit=False --load_in_4bit=False
Cuda out of memory
Please reduce any below
micro_batch_size
eval_batch_size
sequence_len
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.
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Bugs? Please check for open issue else create a new Issue.
PRs are greatly welcome!