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import os
import subprocess
import yaml
# Base config
config_template = {
"task": "llm-sft",
"base_model": "mistralai/Mistral-7B-Instruct-v0.3",
"project_name": "",
"log": "tensorboard",
"backend": "spaces-l4x1",
"data": {
"path": "derek-thomas/labeled-multiple-choice-explained-mistral-tokenized",
"train_split": "train",
"valid_split": None,
"chat_template": "none",
"column_mapping": {
"text_column": ""
},
},
"params": {
"block_size": 1024,
"model_max_length": 1024,
"epochs": 2,
"batch_size": 1,
"lr": 3e-5,
"peft": True,
"quantization": "int4",
"target_modules": "all-linear",
"padding": "left",
"optimizer": "adamw_torch",
"scheduler": "linear",
"gradient_accumulation": 8,
"mixed_precision": "bf16",
},
"hub": {
"username": "derek-thomas",
"token": os.getenv('HF_TOKEN'),
"push_to_hub": True,
},
}
# Suffix options
project_suffixes = ["RFA-gpt3-5", "RFA-mistral", "FAR-gpt3-5", "FAR-mistral", "FA"]
text_columns = ["conversation_RFA_gpt3_5", "conversation_RFA_mistral", "conversation_FAR_gpt3_5",
"conversation_FAR_mistral", "conversation_FA"]
# Directory to store generated configs
output_dir = "./autotrain_configs"
os.makedirs(output_dir, exist_ok=True)
# Generate configs and run commands
for project_suffix, text_column in zip(project_suffixes, text_columns):
# Modify the config
config = config_template.copy()
config["project_name"] = f"mistral-v03-poe-{project_suffix}"
config["data"]["column_mapping"]["text_column"] = text_column
# Save the config to a YAML file
config_path = os.path.join(output_dir, f"{text_column}.yml")
with open(config_path, "w") as f:
yaml.dump(config, f)
# Run the command
print(f"Running autotrain with config: {config_path}")
subprocess.run(["autotrain", "--config", config_path])
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