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import gradio as gr
from alignment import (
DataArguments,
ModelArguments,
apply_chat_template,
get_datasets,
get_tokenizer,
)
def template(base_model, trained_adapter, token):
data_args = DataArguments(chat_template=None, dataset_mixer={'HuggingFaceH4/no_robots': 1.0}, dataset_splits=['train_sft', 'test_sft'], max_train_samples=None, max_eval_samples=None, preprocessing_num_workers=12, truncation_side=None)
model_args = ModelArguments(base_model_revision=None, model_name_or_path='mistralai/Mistral-7B-v0.1', model_revision='main', model_code_revision=None, torch_dtype='auto', trust_remote_code=True, use_flash_attention_2=True, use_peft=True, lora_r=64, lora_alpha=16, lora_dropout=0.1, lora_target_modules=['q_proj', 'k_proj', 'v_proj', 'o_proj'], lora_modules_to_save=None, load_in_8bit=False, load_in_4bit=True, bnb_4bit_quant_type='nf4', use_bnb_nested_quant=False)
###############
# Load datasets
###############
raw_datasets = get_datasets(data_args, splits=data_args.dataset_splits)
logger.info(
f"Training on the following datasets and their proportions: {[split + ' : ' + str(dset.num_rows) for split, dset in raw_datasets.items()]}"
)
################
# Load tokenizer
################
tokenizer = get_tokenizer(model_args, data_args)
#####################
# Apply chat template
#####################
raw_datasets = raw_datasets.map(apply_chat_template, fn_kwargs={"tokenizer": tokenizer, "task": "sft"})
train_dataset = raw_datasets["train"]
eval_dataset = raw_datasets["test"]
with gr.Blocks() as demo:
gr.Markdown("## AutoTrain Merge Adapter")
gr.Markdown("Please duplicate this space and attach a GPU in order to use it.")
token = gr.Textbox(
label="Hugging Face Write Token",
value="",
lines=1,
max_lines=1,
interactive=True,
type="password",
)
base_model = gr.Textbox(
label="Base Model (e.g. meta-llama/Llama-2-7b-chat-hf)",
value="",
lines=1,
max_lines=1,
interactive=True,
)
trained_adapter = gr.Textbox(
label="Trained Adapter Model (e.g. username/autotrain-my-llama)",
value="",
lines=1,
max_lines=1,
interactive=True,
)
submit = gr.Button(value="Merge & Push")
op = gr.Markdown(interactive=False)
submit.click(merge, inputs=[base_model, trained_adapter, token], outputs=[op])
if __name__ == "__main__":
demo.launch() |