## Axolotl supports
| | fp16/fp32 | fp16/fp32 w/ lora | 4bit-quant | 4bit-quant w/flash attention | flash attention | xformers attention |
|----------|:----------|:------------------|------------|------------------------------|-----------------|--------------------|
| llama | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| Pythia | ✅ | ✅ | ❌ | ❌ | ❌ | ❓ |
| cerebras | ✅ | ✅ | ❌ | ❌ | ❌ | ❓ |
| mpt | ✅ | ❌ | ❌ | ❌ | ❌ | ❓ |
## Quick start
**Requirements**: Python 3.9.
```bash
git clone https://github.com/OpenAccess-AI-Collective/axolotl
pip3 install -e .[int4]
accelerate config
accelerate launch scripts/finetune.py examples/4bit-lora-7b/config.yml
```
## Requirements and Installation
### Environment
- Docker
```bash
docker run --gpus '"all"' --rm -it winglian/axolotl:main
```
- `winglian/axolotl:dev`: dev branch
- `winglian/axolotl-runpod:main`: for runpod
- `--gpus '"device=0"'`: to select one gpu
- `-v $PWD:/workspace/axolotl`: to mount current path for dev
- Conda/Pip venv
1. Install python **3.9**
2. Install python dependencies with ONE of the following:
- `pip3 install -e .[int4]` (recommended)
- `pip3 install -e .[int4_triton]`
- `pip3 install -e .`
### Dataset
Have dataset(s) in one of the following format (JSONL recommended):
- `alpaca`: instruction; input(optional)
```json
{"instruction": "...", "input": "...", "output": "..."}
```
- `sharegpt`: conversations
```json
{"conversations": [{"from": "...", "value": "..."}]}
```
- `completion`: raw corpus
```json
{"text": "..."}
```
See all formats
- `alpaca`: instruction; input(optional)
```json
{"instruction": "...", "input": "...", "output": "..."}
```
- `jeopardy`: question and answer
```json
{"question": "...", "category": "...", "answer": "..."}
```
- `oasst`: instruction
```json
{"INSTRUCTION": "...", "RESPONSE": "..."}
```
- `gpteacher`: instruction; input(optional)
```json
{"instruction": "...", "input": "...", "response": "..."}
```
- `reflection`: instruction with reflect; input(optional)
```json
{"instruction": "...", "input": "...", "output": "...", "reflection": "...", "corrected": "..."}
```
- `sharegpt`: conversations
```json
{"conversations": [{"from": "...", "value": "..."}]}
```
- `completion`: raw corpus
```json
{"text": "..."}
```
Optionally, download some datasets, see [data/README.md](data/README.md)
### Config
See sample configs in [configs](configs) folder or [examples](examples) for quick start. It is recommended to duplicate and modify to your needs. The most important options are:
- model
```yaml
base_model: ./llama-7b-hf # local or huggingface repo
```
Note: The code will load the right architecture.
- dataset
```yaml
datasets:
- path: vicgalle/alpaca-gpt4 # local or huggingface repo
type: alpaca # format from earlier
```
- loading
```yaml
load_4bit: true
load_in_8bit: true
bf16: true
fp16: true
tf32: true
```
Note: Repo does not do 4-bit quantization.
- lora
```yaml
adapter: lora # blank for full finetune
lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
- q_proj
- v_proj
```
All yaml options
```yaml
# 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
# whether you are training a 4-bit quantized model
load_4bit: 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 CUDA bf16
bf16: true
# Use CUDA fp16
fp16: true
# Use CUDA tf32
tf32: true
# 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
data_files: # path to source data files
# 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
# How much of the dataset to set aside as evaluation. 1 = 100%, 0.50 = 50%, etc
val_set_size: 0.04
# 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, 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_modules_to_save:
# - embed_tokens
# - lm_head
lora_out_dir:
lora_fan_in_fan_out: false
# wandb configuration if you're using it
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 to use with the optimizer. only one_cycle is supported currently
lr_scheduler:
# 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:
# 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: ""
# eos_token: ""
# unk_token: ""
# add extra tokens
tokens:
# FSDP
fsdp:
fsdp_config:
# Deepspeed
deepspeed:
# TODO
torchdistx_path:
# Debug mode
debug:
```
### Accelerate
Configure accelerate
```bash
accelerate config
# nano ~/.cache/huggingface/accelerate/default_config.yaml
```
### Train
Run
```bash
accelerate launch scripts/finetune.py configs/your_config.yml
```
### Inference
Add `--inference` flag to train command above
If you are inferencing a pretrained LORA, pass
```bash
--lora_model_dir ./completed-model
```
### Merge LORA to base (Dev branch 🔧 )
Add `--merge_lora --lora_model_dir="path/to/lora"` flag to train command above
