# Axolotl
axolotl

One repo to finetune them all!

Go ahead and axolotl questions!!

pre-commit PyTest Status
## Axolotl supports | | fp16/fp32 | lora | qlora | gptq | gptq w/ lora | gptq w/flash attn | flash attn | xformers attn | |----------|:----------|:-----|-------|------|:-------------|-------------------|------------|---------------| | llama | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | Pythia | ✅ | ✅ | ✅ | ❌ | ❓ | ❌ | ❌ | ❓ | | cerebras | ✅ | ✅ | ✅ | ❌ | ❓ | ❌ | ❌ | ✅ | | mpt | ✅ | ❌ | ❓ | ❌ | ❓ | ❌ | ❌ | ❓ | | falcon | ✅ | ✅ | ✅ | ❌ | ❓ | ❌ | ❌ | ✅ | | gpt-j | ✅ | ✅ | ✅ | ❌ | ❓ | ❌ | ❓ | ✅ | ## Quickstart ⚡ **Requirements**: Python 3.9 and Pytorch 2.0. ```bash git clone https://github.com/OpenAccess-AI-Collective/axolotl pip3 install -e . pip3 install -U git+https://github.com/huggingface/peft.git # 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" ``` ## Installation ### Environment - Docker ```bash 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 runpod - `winglian/axolotl-runpod:main-py3.9-cu118-2.0.0-gptq`: for gptq - `winglian/axolotl:dev`: dev branch (not usually up to date) Or run on the current files for development: ```sh docker compose up -d ``` - Conda/Pip venv 1. Install python **3.9** 2. Install pytorch stable https://pytorch.org/get-started/locally/ 3. Install python dependencies with ONE of the following: - Recommended, supports QLoRA, NO gptq/int4 support ```bash pip3 install -e . pip3 install -U git+https://github.com/huggingface/peft.git ``` - gptq/int4 support, NO QLoRA ```bash pip3 install -e .[gptq] ``` - same as above but not recommended ```bash pip3 install -e .[gptq_triton] ``` - LambdaLabs
Click to Expand 1. Install python ```bash 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 ``` 2. Install pip ```bash wget https://bootstrap.pypa.io/get-pip.py python get-pip.py ``` 3. Install torch ```bash pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 ``` 4. Axolotl ```bash 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 ``` 5. Set path ```bash export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH ```
### Dataset Have dataset(s) in one of the following format (JSONL recommended): - `alpaca`: instruction; input(optional) ```json {"instruction": "...", "input": "...", "output": "..."} ``` - `sharegpt:chat`: 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": "..."} ``` - `explainchoice`: question, choices, (solution OR explanation) ```json {"question": "...", "choices": ["..."], "solution": "...", "explanation": "..."} ``` - `concisechoice`: question, choices, (solution OR explanation) ```json {"question": "...", "choices": ["..."], "solution": "...", "explanation": "..."} ``` - `summarizetldr`: article and summary ```json {"article": "...", "summary": "..."} ``` - `alpaca_chat`: basic instruct for alpaca chat ```json {"instruction": "...", "input": "...", "response": "..."} ``` - `alpaca_chat.load_qa`: question and answer for alpaca chat ```json {"question": "...", "answer": "..."} ``` - `alpaca_chat.load_concise`: question and answer for alpaca chat, for concise answers ```json {"instruction": "...", "input": "...", "response": "..."} ``` - `alpaca_chat.load_camel_ai`: question and answer for alpaca chat, for load_camel_ai ```json {"message_1": "...", "message_2": "..."} ``` - `alpaca_w_system.load_open_orca`: support for open orca datasets with included system prompts, instruct ```json {"system_prompt": "...", "question": "...", "response": "..."} ``` - `context_qa`: in context question answering from an article ```json {"article": "...", "question": "...", "answer": "..."} ``` - `context_qa.load_404`: in context question answering from an article, with default response for no answer from context ```json {"article": "...", "unanswerable_question": "..."} ``` - `creative_acr.load_answer`: instruction and revision ```json {"instruction": "...", "revision": "..."} ``` - `creative_acr.load_critique`: critique ```json {"scores": "...", "critiques": "...", "instruction": "...", "answer": "..."} ``` - `creative_acr.load_revise`: critique and revise ```json {"scores": "...", "critiques": "...", "instruction": "...", "answer": "...", "revision": "..."} ``` - `pygmalion`: pygmalion ```json {"conversations": [{"role": "...", "value": "..."}]} ``` - `sharegpt_simple.load_role`: conversations where `role` is used instead of `from` ```json {"conversations": [{"role": "...", "value": "..."}]} ``` - `sharegpt_jokes`: creates a chat where bot is asked to tell a joke, then explain why the joke is funny ```json {"conversations": [{"title": "...", "text": "...", "explanation": "..."}]} ```
#### How to add custom prompts 1. Add your method to a file in [prompt_strategies](src/axolotl/prompt_strategies). Please see other files as example. 2. Use your custom file name as the dataset type `.load_`. 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 sequence_len: 2048 # max token length for prompt # huggingface repo datasets: - path: vicgalle/alpaca-gpt4 type: alpaca # format from earlier # huggingface repo with specific configuration/subset datasets: - path: EleutherAI/pile name: enron_emails type: completion # format from earlier # local datasets: - path: json data_files: data.jsonl # or json type: alpaca # format from earlier ``` - loading ```yaml 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 ```yaml 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 ```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 # 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 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: # hf 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: (chat/instruct) | .load_ data_files: # path to source data files shards: # number of shards to split data into name: # name of dataset configuration to load # 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 # push checkpoints to hub hub_model_id: # repo path to push finetuned model # 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: # 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 # 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: "" # eos_token: "" # unk_token: "" # 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: ```
### Train Run ```bash accelerate launch scripts/finetune.py configs/your_config.yml ``` #### Multi-GPU Config - llama FSDP ```yaml 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 ``` - llama Deepspeed: append `ACCELERATE_USE_DEEPSPEED=true` in front of finetune command ### Inference Pass the appropriate flag to the train command: - Pretrained LORA: ```bash --inference --lora_model_dir="./lora-output-dir" ``` - Full weights finetune: ```bash --inference --base_model="./completed-model" ``` - Full weights finetune w/ a prompt from a text file: ```bash cat /tmp/prompt.txt | python scripts/finetune.py configs/your_config.yml \ --base_model="./completed-model" --inference --prompter=None --load_in_8bit=True ``` ### Merge LORA to base Add below flag to train command above ```bash --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 ```bash CUDA_VISIBLE_DEVICES="" python3 scripts/finetune.py ... ``` ## Common Errors 🧰 > 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. > accelerate config missing It's safe to ignore it. ## Need help? 🙋♂️ Join our [Discord server](https://discord.gg/HhrNrHJPRb) where we can help you ## Badge ❤🏷️ Building something cool with Axolotl? Consider adding a badge to your model card. ```markdown [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) ``` [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) ## Community Showcase Open Access AI Collective - [Minotaur 13b](https://huggingface.co/openaccess-ai-collective/minotaur-13b) - [Manticore 13b](https://huggingface.co/openaccess-ai-collective/manticore-13b) - [Hippogriff 30b](https://huggingface.co/openaccess-ai-collective/hippogriff-30b-chat) PocketDoc Labs - [Dan's PersonalityEngine 13b LoRA](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-13b-LoRA) ## 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**! Please run below to setup env ```bash pip3 install -r requirements-dev.txt -r requirements-tests.txt pre-commit install # test pytest tests/ ```