refactor scripts/finetune.py into new cli modules (#550)
* refactor scripts/finetune.py into new cli modules * continue to support scripts/finetune.py * update readme with updated cli commands * Update scripts/finetune.py Co-authored-by: NanoCode012 <kevinvong@rocketmail.com> --------- Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
This commit is contained in:
Wing Lian
GitHub
22
README.md
22
README.md
@@ -76,11 +76,11 @@ pip3 install -e .[flash-attn]
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pip3 install -U git+https://github.com/huggingface/peft.git
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# finetune lora
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accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml
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accelerate launch -m axolotl.cli.train examples/openllama-3b/lora.yml
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# inference
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accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml \
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--inference --lora_model_dir="./lora-out"
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accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
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--lora_model_dir="./lora-out"
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```
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## Installation
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@@ -674,14 +674,14 @@ strict:
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Run
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```bash
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accelerate launch scripts/finetune.py your_config.yml
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accelerate launch -m axolotl.cli.train your_config.yml
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```
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#### Multi-GPU
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You can optionally pre-tokenize dataset with the following before finetuning:
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```bash
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CUDA_VISIBLE_DEVICES="" accelerate ... --prepare_ds_only
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CUDA_VISIBLE_DEVICES="" accelerate launch -m axolotl.cli.train your_config.yml --prepare_ds_only
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```
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##### Config
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@@ -720,16 +720,16 @@ Pass the appropriate flag to the train command:
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- Pretrained LORA:
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```bash
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--inference --lora_model_dir="./lora-output-dir"
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python -m axolotl.cli.inference examples/your_config.yml --lora_model_dir="./lora-output-dir"
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```
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- Full weights finetune:
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```bash
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--inference --base_model="./completed-model"
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python -m axolotl.cli.inference examples/your_config.yml --base_model="./completed-model"
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```
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- Full weights finetune w/ a prompt from a text file:
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```bash
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cat /tmp/prompt.txt | python scripts/finetune.py configs/your_config.yml \
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--base_model="./completed-model" --inference --prompter=None --load_in_8bit=True
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cat /tmp/prompt.txt | python -m axolotl.cli.inference examples/your_config.yml \
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--base_model="./completed-model" --prompter=None --load_in_8bit=True
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```
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### Merge LORA to base
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@@ -737,13 +737,13 @@ Pass the appropriate flag to the train command:
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Add below flag to train command above
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```bash
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--merge_lora --lora_model_dir="./completed-model" --load_in_8bit=False --load_in_4bit=False
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python3 -m axolotl.cli.merge_lora examples/your_config.yml --lora_model_dir="./completed-model" --load_in_8bit=False --load_in_4bit=False
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```
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If you run out of CUDA memory, you can try to merge in system RAM with
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```bash
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CUDA_VISIBLE_DEVICES="" python3 scripts/finetune.py ...
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CUDA_VISIBLE_DEVICES="" python3 -m axolotl.cli.merge_lora ...
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```
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## Common Errors 🧰
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@@ -1,269 +1,34 @@
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"""Prepare and train a model on a dataset. Can also infer from a model or merge lora"""
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import importlib
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import logging
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import os
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import random
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import sys
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Union
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import fire
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import torch
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import transformers
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import yaml
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# add src to the pythonpath so we don't need to pip install this
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from accelerate.commands.config import config_args
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from art import text2art
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from transformers import GenerationConfig, TextStreamer
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from axolotl.cli import (
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check_accelerate_default_config,
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do_inference,
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do_merge_lora,
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load_cfg,
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load_datasets,
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print_axolotl_text_art,
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)
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from axolotl.cli.shard import shard
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from axolotl.common.cli import TrainerCliArgs
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from axolotl.train import train
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from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
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from axolotl.logging_config import configure_logging
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from axolotl.train import TrainDatasetMeta, train
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from axolotl.utils.config import normalize_config, validate_config
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from axolotl.utils.data import prepare_dataset
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from axolotl.utils.dict import DictDefault
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from axolotl.utils.distributed import is_main_process
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from axolotl.utils.models import load_tokenizer
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from axolotl.utils.tokenization import check_dataset_labels
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from axolotl.utils.wandb_ import setup_wandb_env_vars
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project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
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src_dir = os.path.join(project_root, "src")
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sys.path.insert(0, src_dir)
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configure_logging()
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LOG = logging.getLogger("axolotl.scripts")
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os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
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def print_axolotl_text_art(suffix=None):
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font = "nancyj"
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ascii_text = " axolotl"
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if suffix:
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ascii_text += f" x {suffix}"
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ascii_art = text2art(" axolotl", font=font)
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if is_main_process():
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print(ascii_art)
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def get_multi_line_input() -> Optional[str]:
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print("Give me an instruction (Ctrl + D to finish): ")
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instruction = ""
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for line in sys.stdin:
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instruction += line # pylint: disable=consider-using-join
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# instruction = pathlib.Path("/proc/self/fd/0").read_text()
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return instruction
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def do_merge_lora(
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*,
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cfg: DictDefault,
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cli_args: TrainerCliArgs,
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):
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model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
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safe_serialization = cfg.save_safetensors is True
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LOG.info("running merge of LoRA with base model")
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model = model.merge_and_unload()
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model.to(dtype=torch.float16)
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if cfg.local_rank == 0:
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LOG.info("saving merged model")
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model.save_pretrained(
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str(Path(cfg.output_dir) / "merged"),
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safe_serialization=safe_serialization,
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)
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tokenizer.save_pretrained(str(Path(cfg.output_dir) / "merged"))
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def shard(
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*,
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cfg: DictDefault,
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cli_args: TrainerCliArgs,
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):
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model, _ = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
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safe_serialization = cfg.save_safetensors is True
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LOG.debug("Re-saving model w/ sharding")
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model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
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def do_inference(
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*,
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cfg: DictDefault,
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cli_args: TrainerCliArgs,
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):
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model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
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prompter = cli_args.prompter
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default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
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for token, symbol in default_tokens.items():
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# If the token isn't already specified in the config, add it
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if not (cfg.special_tokens and token in cfg.special_tokens):
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tokenizer.add_special_tokens({token: symbol})
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prompter_module = None
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if prompter:
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prompter_module = getattr(
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importlib.import_module("axolotl.prompters"), prompter
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)
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if cfg.landmark_attention:
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from axolotl.monkeypatch.llama_landmark_attn import set_model_mem_id
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set_model_mem_id(model, tokenizer)
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model.set_mem_cache_args(
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max_seq_len=255, mem_freq=50, top_k=5, max_cache_size=None
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)
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model = model.to(cfg.device)
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while True:
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print("=" * 80)
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# support for multiline inputs
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instruction = get_multi_line_input()
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if not instruction:
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return
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if prompter_module:
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prompt: str = next(
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prompter_module().build_prompt(instruction=instruction.strip("\n"))
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)
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else:
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prompt = instruction.strip()
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batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
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print("=" * 40)
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model.eval()
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with torch.no_grad():
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generation_config = GenerationConfig(
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repetition_penalty=1.1,
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max_new_tokens=1024,
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temperature=0.9,
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top_p=0.95,
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top_k=40,
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bos_token_id=tokenizer.bos_token_id,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.pad_token_id,
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do_sample=True,
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use_cache=True,
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return_dict_in_generate=True,
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output_attentions=False,
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output_hidden_states=False,
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output_scores=False,
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)
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streamer = TextStreamer(tokenizer)
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generated = model.generate(
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inputs=batch["input_ids"].to(cfg.device),
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generation_config=generation_config,
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streamer=streamer,
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)
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print("=" * 40)
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print(tokenizer.decode(generated["sequences"].cpu().tolist()[0]))
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def choose_config(path: Path):
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yaml_files = list(path.glob("*.yml"))
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if not yaml_files:
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raise ValueError(
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"No YAML config files found in the specified directory. Are you using a .yml extension?"
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)
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if len(yaml_files) == 1:
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print(f"Using default YAML file '{yaml_files[0]}'")
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return yaml_files[0]
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print("Choose a YAML file:")
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for idx, file in enumerate(yaml_files):
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print(f"{idx + 1}. {file}")
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chosen_file = None
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while chosen_file is None:
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try:
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choice = int(input("Enter the number of your choice: "))
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if 1 <= choice <= len(yaml_files):
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chosen_file = yaml_files[choice - 1]
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else:
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print("Invalid choice. Please choose a number from the list.")
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except ValueError:
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print("Invalid input. Please enter a number.")
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return chosen_file
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def check_not_in(list1: List[str], list2: Union[Dict[str, Any], List[str]]) -> bool:
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return not any(el in list2 for el in list1)
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def load_cfg(config: Path = Path("examples/"), **kwargs):
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if Path(config).is_dir():
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config = choose_config(config)
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# load the config from the yaml file
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with open(config, encoding="utf-8") as file:
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cfg: DictDefault = DictDefault(yaml.safe_load(file))
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# if there are any options passed in the cli, if it is something that seems valid from the yaml,
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# then overwrite the value
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cfg_keys = cfg.keys()
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for k, _ in kwargs.items():
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# if not strict, allow writing to cfg even if it's not in the yml already
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if k in cfg_keys or not cfg.strict:
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# handle booleans
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if isinstance(cfg[k], bool):
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cfg[k] = bool(kwargs[k])
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else:
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cfg[k] = kwargs[k]
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validate_config(cfg)
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normalize_config(cfg)
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setup_wandb_env_vars(cfg)
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return cfg
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def load_datasets(
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*,
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cfg: DictDefault,
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cli_args: TrainerCliArgs,
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) -> TrainDatasetMeta:
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tokenizer = load_tokenizer(cfg)
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train_dataset, eval_dataset, total_num_steps = prepare_dataset(cfg, tokenizer)
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if cli_args.debug or cfg.debug:
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LOG.info("check_dataset_labels...")
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check_dataset_labels(
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train_dataset.select(
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[
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random.randrange(0, len(train_dataset) - 1) # nosec
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for _ in range(cli_args.debug_num_examples)
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]
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),
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tokenizer,
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num_examples=cli_args.debug_num_examples,
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text_only=cli_args.debug_text_only,
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)
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return TrainDatasetMeta(
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train_dataset=train_dataset,
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eval_dataset=eval_dataset,
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total_num_steps=total_num_steps,
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)
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def check_accelerate_default_config():
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if Path(config_args.default_yaml_config_file).exists():
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LOG.warning(
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f"accelerate config file found at {config_args.default_yaml_config_file}. This can lead to unexpected errors"
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)
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LOG = logging.getLogger("axolotl.scripts.finetune")
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def do_cli(config: Path = Path("examples/"), **kwargs):
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print_axolotl_text_art()
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LOG.warning(
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str(
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PendingDeprecationWarning(
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"scripts/finetune.py will be replaced with calling axolotl.cli.train"
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)
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)
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)
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parsed_cfg = load_cfg(config, **kwargs)
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check_accelerate_default_config()
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parser = transformers.HfArgumentParser((TrainerCliArgs))
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249
src/axolotl/cli/__init__.py
Normal file
249
src/axolotl/cli/__init__.py
Normal file
@@ -0,0 +1,249 @@
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"""Prepare and train a model on a dataset. Can also infer from a model or merge lora"""
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import importlib
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import logging
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import os
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import random
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import sys
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Union
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import torch
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import yaml
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# add src to the pythonpath so we don't need to pip install this
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from accelerate.commands.config import config_args
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from art import text2art
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from transformers import GenerationConfig, TextStreamer
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from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
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from axolotl.logging_config import configure_logging
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from axolotl.train import TrainDatasetMeta
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from axolotl.utils.config import normalize_config, validate_config
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from axolotl.utils.data import prepare_dataset
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from axolotl.utils.dict import DictDefault
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from axolotl.utils.distributed import is_main_process
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from axolotl.utils.models import load_tokenizer
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from axolotl.utils.tokenization import check_dataset_labels
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from axolotl.utils.wandb_ import setup_wandb_env_vars
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project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
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src_dir = os.path.join(project_root, "src")
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sys.path.insert(0, src_dir)
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configure_logging()
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LOG = logging.getLogger("axolotl.scripts")
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os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
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def print_axolotl_text_art(suffix=None):
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font = "nancyj"
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ascii_text = " axolotl"
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if suffix:
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ascii_text += f" x {suffix}"
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ascii_art = text2art(" axolotl", font=font)
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if is_main_process():
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print(ascii_art)
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|
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|
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def get_multi_line_input() -> Optional[str]:
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print("Give me an instruction (Ctrl + D to finish): ")
|
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instruction = ""
|
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for line in sys.stdin:
|
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instruction += line # pylint: disable=consider-using-join
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# instruction = pathlib.Path("/proc/self/fd/0").read_text()
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return instruction
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def do_merge_lora(
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*,
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cfg: DictDefault,
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cli_args: TrainerCliArgs,
|
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):
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model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
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safe_serialization = cfg.save_safetensors is True
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LOG.info("running merge of LoRA with base model")
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model = model.merge_and_unload()
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model.to(dtype=torch.float16)
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if cfg.local_rank == 0:
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LOG.info("saving merged model")
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model.save_pretrained(
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str(Path(cfg.output_dir) / "merged"),
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safe_serialization=safe_serialization,
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)
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tokenizer.save_pretrained(str(Path(cfg.output_dir) / "merged"))
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|
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def do_inference(
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*,
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cfg: DictDefault,
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cli_args: TrainerCliArgs,
|
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):
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model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
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prompter = cli_args.prompter
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default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
|
||||
|
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for token, symbol in default_tokens.items():
|
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# If the token isn't already specified in the config, add it
|
||||
if not (cfg.special_tokens and token in cfg.special_tokens):
|
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tokenizer.add_special_tokens({token: symbol})
|
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|
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prompter_module = None
|
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if prompter:
|
||||
prompter_module = getattr(
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importlib.import_module("axolotl.prompters"), prompter
|
||||
)
|
||||
|
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if cfg.landmark_attention:
|
||||
from axolotl.monkeypatch.llama_landmark_attn import set_model_mem_id
|
||||
|
||||
set_model_mem_id(model, tokenizer)
|
||||
model.set_mem_cache_args(
|
||||
max_seq_len=255, mem_freq=50, top_k=5, max_cache_size=None
|
||||
)
|
||||
|
||||
model = model.to(cfg.device)
|
||||
|
||||
while True:
|
||||
print("=" * 80)
|
||||
# support for multiline inputs
|
||||
instruction = get_multi_line_input()
|
||||
if not instruction:
|
||||
return
|
||||
if prompter_module:
|
||||
prompt: str = next(
|
||||
prompter_module().build_prompt(instruction=instruction.strip("\n"))
|
||||
)
|
||||
else:
|
||||
prompt = instruction.strip()
|
||||
batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
|
||||
|
||||
print("=" * 40)
|
||||
model.eval()
|
||||
with torch.no_grad():
|
||||
generation_config = GenerationConfig(
|
||||
repetition_penalty=1.1,
|
||||
max_new_tokens=1024,
|
||||
temperature=0.9,
|
||||
top_p=0.95,
|
||||
top_k=40,
|
||||
bos_token_id=tokenizer.bos_token_id,
|
||||
eos_token_id=tokenizer.eos_token_id,
|
||||
pad_token_id=tokenizer.pad_token_id,
|
||||
do_sample=True,
|
||||
use_cache=True,
|
||||
return_dict_in_generate=True,
|
||||
output_attentions=False,
|
||||
output_hidden_states=False,
|
||||
output_scores=False,
|
||||
)
|
||||
streamer = TextStreamer(tokenizer)
|
||||
generated = model.generate(
|
||||
inputs=batch["input_ids"].to(cfg.device),
|
||||
generation_config=generation_config,
|
||||
streamer=streamer,
|
||||
)
|
||||
print("=" * 40)
|
||||
print(tokenizer.decode(generated["sequences"].cpu().tolist()[0]))
|
||||
|
||||
|
||||
def choose_config(path: Path):
|
||||
yaml_files = list(path.glob("*.yml"))
|
||||
|
||||
if not yaml_files:
|
||||
raise ValueError(
|
||||
"No YAML config files found in the specified directory. Are you using a .yml extension?"
|
||||
)
|
||||
|
||||
if len(yaml_files) == 1:
|
||||
print(f"Using default YAML file '{yaml_files[0]}'")
|
||||
return yaml_files[0]
|
||||
|
||||
print("Choose a YAML file:")
|
||||
for idx, file in enumerate(yaml_files):
|
||||
print(f"{idx + 1}. {file}")
|
||||
|
||||
chosen_file = None
|
||||
while chosen_file is None:
|
||||
try:
|
||||
choice = int(input("Enter the number of your choice: "))
|
||||
if 1 <= choice <= len(yaml_files):
|
||||
chosen_file = yaml_files[choice - 1]
|
||||
else:
|
||||
print("Invalid choice. Please choose a number from the list.")
|
||||
except ValueError:
|
||||
print("Invalid input. Please enter a number.")
|
||||
|
||||
return chosen_file
|
||||
|
||||
|
||||
def check_not_in(list1: List[str], list2: Union[Dict[str, Any], List[str]]) -> bool:
|
||||
return not any(el in list2 for el in list1)
|
||||
|
||||
|
||||
def load_cfg(config: Path = Path("examples/"), **kwargs):
|
||||
if Path(config).is_dir():
|
||||
config = choose_config(config)
|
||||
|
||||
# load the config from the yaml file
|
||||
with open(config, encoding="utf-8") as file:
|
||||
cfg: DictDefault = DictDefault(yaml.safe_load(file))
|
||||
# if there are any options passed in the cli, if it is something that seems valid from the yaml,
|
||||
# then overwrite the value
|
||||
cfg_keys = cfg.keys()
|
||||
for k, _ in kwargs.items():
|
||||
# if not strict, allow writing to cfg even if it's not in the yml already
|
||||
if k in cfg_keys or not cfg.strict:
|
||||
# handle booleans
|
||||
if isinstance(cfg[k], bool):
|
||||
cfg[k] = bool(kwargs[k])
|
||||
else:
|
||||
cfg[k] = kwargs[k]
|
||||
|
||||
validate_config(cfg)
|
||||
|
||||
normalize_config(cfg)
|
||||
|
||||
setup_wandb_env_vars(cfg)
|
||||
return cfg
|
||||
|
||||
|
||||
def load_datasets(
|
||||
*,
|
||||
cfg: DictDefault,
|
||||
cli_args: TrainerCliArgs,
|
||||
) -> TrainDatasetMeta:
|
||||
tokenizer = load_tokenizer(cfg)
|
||||
|
||||
train_dataset, eval_dataset, total_num_steps = prepare_dataset(cfg, tokenizer)
|
||||
|
||||
if cli_args.debug or cfg.debug:
|
||||
LOG.info("check_dataset_labels...")
|
||||
check_dataset_labels(
|
||||
train_dataset.select(
|
||||
[
|
||||
random.randrange(0, len(train_dataset) - 1) # nosec
|
||||
for _ in range(cli_args.debug_num_examples)
|
||||
]
|
||||
),
|
||||
tokenizer,
|
||||
num_examples=cli_args.debug_num_examples,
|
||||
text_only=cli_args.debug_text_only,
|
||||
)
|
||||
|
||||
return TrainDatasetMeta(
|
||||
train_dataset=train_dataset,
|
||||
eval_dataset=eval_dataset,
|
||||
total_num_steps=total_num_steps,
|
||||
)
|
||||
|
||||
|
||||
def check_accelerate_default_config():
|
||||
if Path(config_args.default_yaml_config_file).exists():
|
||||
LOG.warning(
|
||||
f"accelerate config file found at {config_args.default_yaml_config_file}. This can lead to unexpected errors"
|
||||
)
|
||||
26
src/axolotl/cli/inference.py
Normal file
26
src/axolotl/cli/inference.py
Normal file
@@ -0,0 +1,26 @@
|
||||
"""
|
||||
CLI to run inference on a trained model
|
||||
"""
|
||||
from pathlib import Path
|
||||
|
||||
import fire
|
||||
import transformers
|
||||
|
||||
from axolotl.cli import do_inference, load_cfg, print_axolotl_text_art
|
||||
from axolotl.common.cli import TrainerCliArgs
|
||||
|
||||
|
||||
def do_cli(config: Path = Path("examples/"), **kwargs):
|
||||
# pylint: disable=duplicate-code
|
||||
print_axolotl_text_art()
|
||||
parsed_cfg = load_cfg(config, **kwargs)
|
||||
parser = transformers.HfArgumentParser((TrainerCliArgs))
|
||||
parsed_cli_args, _ = parser.parse_args_into_dataclasses(
|
||||
return_remaining_strings=True
|
||||
)
|
||||
parsed_cli_args.inference = True
|
||||
|
||||
do_inference(cfg=parsed_cfg, cli_args=parsed_cli_args)
|
||||
|
||||
|
||||
fire.Fire(do_cli)
|
||||
26
src/axolotl/cli/merge_lora.py
Normal file
26
src/axolotl/cli/merge_lora.py
Normal file
@@ -0,0 +1,26 @@
|
||||
"""
|
||||
CLI to run merge a trained LoRA into a base model
|
||||
"""
|
||||
from pathlib import Path
|
||||
|
||||
import fire
|
||||
import transformers
|
||||
|
||||
from axolotl.cli import do_merge_lora, load_cfg, print_axolotl_text_art
|
||||
from axolotl.common.cli import TrainerCliArgs
|
||||
|
||||
|
||||
def do_cli(config: Path = Path("examples/"), **kwargs):
|
||||
# pylint: disable=duplicate-code
|
||||
print_axolotl_text_art()
|
||||
parsed_cfg = load_cfg(config, **kwargs)
|
||||
parser = transformers.HfArgumentParser((TrainerCliArgs))
|
||||
parsed_cli_args, _ = parser.parse_args_into_dataclasses(
|
||||
return_remaining_strings=True
|
||||
)
|
||||
parsed_cli_args.merge_lora = True
|
||||
|
||||
do_merge_lora(cfg=parsed_cfg, cli_args=parsed_cli_args)
|
||||
|
||||
|
||||
fire.Fire(do_cli)
|
||||
41
src/axolotl/cli/shard.py
Normal file
41
src/axolotl/cli/shard.py
Normal file
@@ -0,0 +1,41 @@
|
||||
"""
|
||||
CLI to shard a trained model into 10GiB chunks
|
||||
"""
|
||||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
import fire
|
||||
import transformers
|
||||
|
||||
from axolotl.cli import load_cfg, print_axolotl_text_art
|
||||
from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
|
||||
from axolotl.utils.dict import DictDefault
|
||||
|
||||
LOG = logging.getLogger("axolotl.scripts")
|
||||
|
||||
|
||||
def shard(
|
||||
*,
|
||||
cfg: DictDefault,
|
||||
cli_args: TrainerCliArgs,
|
||||
):
|
||||
model, _ = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
|
||||
safe_serialization = cfg.save_safetensors is True
|
||||
LOG.debug("Re-saving model w/ sharding")
|
||||
model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
|
||||
|
||||
|
||||
def do_cli(config: Path = Path("examples/"), **kwargs):
|
||||
# pylint: disable=duplicate-code
|
||||
print_axolotl_text_art()
|
||||
parsed_cfg = load_cfg(config, **kwargs)
|
||||
parser = transformers.HfArgumentParser((TrainerCliArgs))
|
||||
parsed_cli_args, _ = parser.parse_args_into_dataclasses(
|
||||
return_remaining_strings=True
|
||||
)
|
||||
parsed_cli_args.shard = True
|
||||
|
||||
shard(cfg=parsed_cfg, cli_args=parsed_cli_args)
|
||||
|
||||
|
||||
fire.Fire(do_cli)
|
||||
35
src/axolotl/cli/train.py
Normal file
35
src/axolotl/cli/train.py
Normal file
@@ -0,0 +1,35 @@
|
||||
"""
|
||||
CLI to run training on a model
|
||||
"""
|
||||
from pathlib import Path
|
||||
|
||||
import fire
|
||||
import transformers
|
||||
|
||||
from axolotl.cli import (
|
||||
check_accelerate_default_config,
|
||||
load_cfg,
|
||||
load_datasets,
|
||||
print_axolotl_text_art,
|
||||
)
|
||||
from axolotl.common.cli import TrainerCliArgs
|
||||
from axolotl.train import train
|
||||
|
||||
|
||||
def do_cli(config: Path = Path("examples/"), **kwargs):
|
||||
# pylint: disable=duplicate-code
|
||||
print_axolotl_text_art()
|
||||
parsed_cfg = load_cfg(config, **kwargs)
|
||||
check_accelerate_default_config()
|
||||
parser = transformers.HfArgumentParser((TrainerCliArgs))
|
||||
parsed_cli_args, _ = parser.parse_args_into_dataclasses(
|
||||
return_remaining_strings=True
|
||||
)
|
||||
|
||||
dataset_meta = load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
|
||||
if parsed_cli_args.prepare_ds_only:
|
||||
return
|
||||
train(cfg=parsed_cfg, cli_args=parsed_cli_args, dataset_meta=dataset_meta)
|
||||
|
||||
|
||||
fire.Fire(do_cli)
|
||||
Reference in New Issue
Block a user