# Axolotl
axolotl

One repo to finetune them all!

Go ahead and axolotl questions!!

## Axolotl supports | | fp16/fp32 | fp16/fp32 w/ lora | 4bit-quant | 4bit-quant w/flash attention | flash attention | xformers attention | |----------|:----------|:------------------|------------|------------------------------|-----------------|--------------------| | llama | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | Pythia | ✅ | ✅ | ❌ | ❌ | ❌ | ❓ | | cerebras | ✅ | ✅ | ❌ | ❌ | ❌ | ❓ | | mpt | ✅ | ❌ | ❌ | ❌ | ❌ | ❓ | ## Quickstart ⚡ **Requirements**: Python 3.9. ```bash git clone https://github.com/OpenAccess-AI-Collective/axolotl pip3 install -e .[int4] accelerate config # finetune accelerate launch scripts/finetune.py examples/4bit-lora-7b/config.yml # inference accelerate launch scripts/finetune.py examples/4bit-lora-7b/config.yml \ --inference --lora_model_dir="./llama-7b-lora-int4" ``` ## Installation ### Environment - Docker ```bash docker run --gpus '"all"' --rm -it winglian/axolotl:main ``` - `winglian/axolotl:dev`: dev branch - `winglian/axolotl-runpod:main`: for runpod - 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 other formats - `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": "..."} ``` > Have some new format to propose? Check if it's already defined in [data.py](src/axolotl/utils/data.py) in `dev` branch!
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 sequence_len: 2048 # max token length / prompt ``` - 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 # Trust remote code for untrusted source trust_remote_code: # 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 # Edit manually # 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 below flag to train command above ```bash --merge_lora --lora_model_dir="./completed-model" ``` ## Common Errors 🧰 > Cuda out of memory Please reduce any below - `micro_batch_size` - `eval_batch_size` - `sequence_len` ## Contributing 🤝 Bugs? Please check for open issue else create a new [Issue](https://github.com/OpenAccess-AI-Collective/axolotl/issues/new). PRs are **greatly welcome**!