Feat: Rewrite Readme

README.md

@@ -1,6 +1,8 @@
 # Axolotl
 
-#### Go ahead and axolotl questions
+A centralized repo to train multiple architectures with different dataset types using a simple yaml file.
+
+Go ahead and axolotl questions!!
 
 ## Support Matrix
 
@@ -9,41 +11,111 @@
 | llama    | ✅         | ✅                 | ✅          | ✅                            | ✅               | ✅                  |
 | Pythia   | ✅         | ✅                 | ❌          | ❌                            | ❌               | ❓                  |
 | cerebras | ✅         | ✅                 | ❌          | ❌                            | ❌               | ❓                  |
+| mpt      | ✅         | ❌                 | ❌          | ❌                            | ❌               | ❓                  |
 
 
 ## Getting Started
-- install python 3.9.  3.10 and above are not supported.
 
-- Point the config you are using to a huggingface hub dataset (see [configs/llama_7B_4bit.yml](https://github.com/winglian/axolotl/blob/main/configs/llama_7B_4bit.yml#L6-L8))
-
-```yaml
-datasets:
-  - path: vicgalle/alpaca-gpt4
-    type: alpaca
-```
-
-- Optionally Download some datasets, see [data/README.md](data/README.md)
-
-
-- Create a new or update the existing YAML config [config/sample.yml](config/sample.yml)
+### Environment
+
+- Docker 
+  ```bash
+  docker pull winglian/axolotl
+  ```
+
+- 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 a dataset in one of the following format:
+
+- alpaca: instruction
+  ```json
+  {"instruction": "...", "input": "...", "output": "..."}
+  ```
+- #TODO add others
+- completion: raw corpus
+  ```json
+  {"text": "..."}
+  ```
+
+Optionally Download some datasets, see [data/README.md](data/README.md)
+
+### Config
+
+See sample configs in [configs](configs) folder. 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
+  ```
+
+- dataset
+  ```yaml
+  datasets:
+    - path: vicgalle/alpaca-gpt4 # local or huggingface repo
+      type: alpaca # format from above
+  ```
+
+- loading
+  ```yaml
+  load_4bit: true
+  load_in_8bit: true
+  bf16: true
+  fp16: true
+  tf32: true
+  ```
+
+- 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
+  ```
+
+<details>
+
+<summary>All yaml options</summary>
 
 ```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: decapoda-research/llama-7b-hf-int4
+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: decapoda-research/llama-7b-hf
+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
@@ -55,17 +127,19 @@ datasets:
 dataset_prepared_path: data/last_run_prepared
 # How much of the dataset to set aside as evaluation. 1 = 100%, 0.50 = 50%, etc
 val_set_size: 0.04
-# 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_model_dir:
+
 # 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
@@ -74,14 +148,24 @@ lora_target_modules:
   - v_proj
 #  - k_proj
 #  - o_proj
+#  - gate_proj
+#  - down_proj
+#  - up_proj
+lora_modules_to_save:
+#  - embed_tokens
+#  - lm_head
+lora_out_dir: # TODO: explain
 lora_fan_in_fan_out: false
-# wandb configuration if your're using it
+
+# wandb configuration if you're using it
 wandb_project:
 wandb_watch:
 wandb_run_id:
-wandb_log_model: checkpoint
+wandb_log_model: # 'checkpoint'
+
 # where to save the finsihed model to
 output_dir: ./completed-model
+
 # training hyperparameters
 batch_size: 8
 micro_batch_size: 2
@@ -89,87 +173,83 @@ 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
-# Use CUDA bf16
-bf16: true
-# Use CUDA tf32
-tf32: true
+
 # 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
 
-- Install python dependencies with ONE of the following:
+special_tokens:
+  # bos_token: "<s>"
+  # eos_token: "</s>"
+  # unk_token: "<unk>"
 
-    - `pip3 install -e .[int4]` (recommended)
-    - `pip3 install -e .[int4_triton]`
-    - `pip3 install -e .`
--
-- If not using `int4` or `int4_triton`, run `pip install "peft @ git+https://github.com/huggingface/peft.git"`
-- Configure accelerate `accelerate config` or update `~/.cache/huggingface/accelerate/default_config.yaml`
+# FSDP
+fsdp:
+fsdp_config:
 
-```yaml
-compute_environment: LOCAL_MACHINE
-distributed_type: MULTI_GPU
-downcast_bf16: 'no'
-gpu_ids: all
-machine_rank: 0
-main_training_function: main
-mixed_precision: bf16
-num_machines: 1
-num_processes: 4
-rdzv_backend: static
-same_network: true
-tpu_env: []
-tpu_use_cluster: false
-tpu_use_sudo: false
-use_cpu: false
+# Deepspeed
+deepspeed:
+
+# TODO
+torchdistx_path:
+
+# Debug mode
+debug:
 ```
 
-- Train! `accelerate launch scripts/finetune.py`, make sure to choose the correct YAML config file
-- Alternatively you can pass in the config file like: `accelerate launch scripts/finetune.py configs/llama_7B_alpaca.yml`~~
+</details>
 
+### Accelerate
 
-## How to start training on Runpod in under 10 minutes
+Configure accelerate using `accelerate config` or update `~/.cache/huggingface/accelerate/default_config.yaml`
 
-- Choose your Docker container wisely.
-- I recommend `huggingface:transformers-pytorch-deepspeed-latest-gpu` see https://hub.docker.com/r/huggingface/transformers-pytorch-deepspeed-latest-gpu/
-- Once you start your runpod, and SSH into it:
-```shell
-export TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
-source <(curl -s https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/dev/scripts/setup-runpod.sh)
-```
+### Train
 
-- Once the setup script completes
-```shell
-accelerate launch scripts/finetune.py configs/quickstart.yml
+Run
+```bash
+accelerate launch scripts/finetune.py configs/your_config.yml
 ```
 
-- Here are some helpful environment variables you'll want to manually set if you open a new shell
-```shell
-export WANDB_MODE=offline
-export WANDB_CACHE_DIR=/workspace/data/wandb-cache
-export HF_DATASETS_CACHE="/workspace/data/huggingface-cache/datasets"
-export HUGGINGFACE_HUB_CACHE="/workspace/data/huggingface-cache/hub"
-export TRANSFORMERS_CACHE="/workspace/data/huggingface-cache/hub"
-export NCCL_P2P_DISABLE=1
+### Inference
+
+Add `--inference` flag to train command above
+
+If you are inferencing a pretrained LORA, pass 
+```bash
+--lora_model_dir path/to/lora
 ```
 
+### Merge LORA to base
+
+Add `--merge_lora --lora_model_dir="path/to/lora"` flag to train command above
+