fp16/fp32 | lora | qlora | gptq | gptq w/ lora | gptq w/flash attn | flash attn | xformers attn | |
---|---|---|---|---|---|---|---|---|
llama | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
Pythia | ✅ | ✅ | ✅ | ❌ | ❓ | ❌ | ❌ | ❓ |
cerebras | ✅ | ✅ | ✅ | ❌ | ❓ | ❌ | ❌ | ✅ |
mpt | ✅ | ❌ | ❓ | ❌ | ❓ | ❌ | ❌ | ❓ |
falcon | ✅ | ✅ | ✅ | ❌ | ❓ | ❌ | ❌ | ✅ |
gpt-j | ✅ | ✅ | ✅ | ❌ | ❓ | ❌ | ❓ | ✅ |
Requirements: Python 3.9 and Pytorch 2.0.
git clone https://github.com/OpenAccess-AI-Collective/axolotl
pip3 install -e .
pip3 install -U git+https://github.com/huggingface/peft.git
accelerate config
# finetune lora
accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml
# inference
accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml \
--inference --lora_model_dir="./lora-out"
-
Docker
docker run --gpus '"all"' --rm -it winglian/axolotl:main-py3.9-cu118-2.0.0
winglian/axolotl-runpod:main-py3.9-cu118-2.0.0
: for runpodwinglian/axolotl-runpod:main-py3.9-cu118-2.0.0-gptq
: for gptqwinglian/axolotl:dev
: dev branch (not usually up to date)
Or run on the current files for development:
docker compose up -d
-
Conda/Pip venv
-
Install python 3.9
-
Install pytorch stable https://pytorch.org/get-started/locally/
-
Install python dependencies with ONE of the following:
- Recommended, supports QLoRA, NO gptq/int4 support
pip3 install -e . pip3 install -U git+https://github.com/huggingface/peft.git
- gptq/int4 support, NO QLoRA
pip3 install -e .[gptq]
- same as above but not recommended
pip3 install -e .[gptq_triton]
- Recommended, supports QLoRA, NO gptq/int4 support
-
-
LambdaLabs
Click to Expand
- 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
- Install pip
wget https://bootstrap.pypa.io/get-pip.py python get-pip.py
- Install torch
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
- Axolotl
git clone https://github.com/OpenAccess-AI-Collective/axolotl cd axolotl pip3 install -e . # change depend on needs pip3 install protobuf==3.20.3 pip3 install -U requests pip3 install -U --ignore-installed psutil pip3 install -U scipy pip3 install git+https://github.com/huggingface/peft.git # not for gptq
- Set path
export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH
Have dataset(s) in one of the following format (JSONL recommended):
alpaca
: instruction; input(optional){"instruction": "...", "input": "...", "output": "..."}
sharegpt:chat
: 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": "..."}
alpaca_chat
: basic instruct for alpaca chat{"instruction": "...", "input": "...", "response": "..."}
alpaca_chat.load_qa
: question and answer for alpaca chat{"question": "...", "answer": "..."}
alpaca_chat.load_concise
: question and answer for alpaca chat, for concise answers{"instruction": "...", "input": "...", "response": "..."}
alpaca_chat.load_camel_ai
: question and answer for alpaca chat, for load_camel_ai{"message_1": "...", "message_2": "..."}
context_qa
: in context question answering from an article{"article": "...", "question": "...", "answer": "..."}
context_qa.load_404
: in context question answering from an article, with default response for no answer from context{"article": "...", "unanswerable_question": "..."}
creative_acr.load_answer
: instruction and revision{"instruction": "...", "revision": "..."}
creative_acr.load_critique
: critique{"scores": "...", "critiques": "...", "instruction": "...", "answer": "..."}
creative_acr.load_revise
: critique and revise{"scores": "...", "critiques": "...", "instruction": "...", "answer": "...", "revision": "..."}
pygmalion
: pygmalion{"conversations": [{"role": "...", "value": "..."}]}
sharegpt_simple.load_role
: conversations whererole
is used instead offrom
{"conversations": [{"role": "...", "value": "..."}]}
sharegpt_jokes
: creates a chat where bot is asked to tell a joke, then explain why the joke is funny{"conversations": [{"title": "...", "text": "...", "explanation": "..."}]}
- Add your method to a file in prompt_strategies. Please see other files as example.
- Use your custom file name as the dataset type.
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 bfloat16: true # require >=ampere, use instead of bf16 when you don't want AMP (automatic mixed precision) float16: true # use instead of fp16 when you don't want AMP
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
# 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 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
gradient_accumulation_steps: 1
micro_batch_size: 2
eval_batch_size: 2
num_epochs: 3
warmup_steps: 100
learning_rate: 0.00003
logging_steps:
save_steps:
eval_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
# 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
optimizer:
# specify weight decay
weight_decay:
# adamw hyperparams
adam_beta1:
adam_beta2:
adam_epsilon:
# Gradient clipping max norm
max_grad_norm:
# 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/HazyResearch/flash-attention:
flash_attention: # require a100 for llama
# 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:
# 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="./lora-output-dir"
- Full weights finetune:
--inference --base_model="./completed-model"
- Full weights finetune w/ a prompt from a text file:
cat /tmp/prompt.txt | python scripts/finetune.py configs/your_config.yml \ --base_model="./completed-model" --inference --prompter=None --load_in_8bit=True
Add below flag to train command above
--merge_lora --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 scripts/finetune.py ...
Cuda out of memory
Please reduce any below
micro_batch_size
eval_batch_size
gradient_accumulation_steps
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!
Please run below to setup env
pip3 install -r requirements-dev.txt -r requirements-tests.txt
pre-commit install
# test
pytest tests/