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# Copyright 2020-2026 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# /// script
# dependencies = [
# "trl[peft]",
# "einops",
# "scikit-learn",
# "joblib",
# "trackio",
# "kernels",
# ]
# ///
"""
Run the BCO training script with the commands below. In general, the optimal configuration for BCO will be similar to that of KTO.
# Full training:
python examples/scripts/bco.py \
--model_name_or_path Qwen/Qwen2.5-0.5B-Instruct \
--trust_remote_code \
--dataset_name trl-lib/ultrafeedback-gpt-3.5-turbo-helpfulness \
--per_device_train_batch_size 16 \
--per_device_eval_batch_size 32 \
--num_train_epochs 1 \
--gradient_accumulation_steps 1 \
--eval_steps 0.2 \
--save_strategy no \
--output_dir bco-aligned-model \
--logging_first_step \
--max_length 2048 \
--max_completion_length 1024 \
--no_remove_unused_columns \
--warmup_steps 0.1
# QLoRA:
python examples/scripts/bco.py \
--model_name_or_path Qwen/Qwen2.5-0.5B-Instruct \
--trust_remote_code \
--dataset_name trl-lib/ultrafeedback-gpt-3.5-turbo-helpfulness \
--per_device_train_batch_size 16 \
--per_device_eval_batch_size 32 \
--num_train_epochs 1 \
--gradient_accumulation_steps 1 \
--eval_steps 0.2 \
--save_strategy no \
--output_dir bco-aligned-model-lora \
--logging_first_step \
--warmup_steps 0.1 \
--max_length 2048 \
--max_completion_length 1024 \
--no_remove_unused_columns \
--warmup_steps 0.1 \
--use_peft \
--load_in_4bit \
--lora_target_modules all-linear \
--lora_r 16 \
--lora_alpha 16
"""
from functools import partial
import torch
import torch.nn.functional as F
from accelerate import Accelerator
from datasets import load_dataset
from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, PreTrainedModel
from trl import ModelConfig, ScriptArguments, get_peft_config
from trl.experimental.bco import BCOConfig, BCOTrainer
def embed_prompt(input_ids: torch.LongTensor, attention_mask: torch.LongTensor, model: PreTrainedModel):
"""
Borrowed from https://huggingface.co/nomic-ai/nomic-embed-text-v1.5#transformers
"""
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
with torch.no_grad():
model_output = model(input_ids=input_ids, attention_mask=attention_mask)
embeddings = mean_pooling(model_output, attention_mask)
matryoshka_dim = 512
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
embeddings = F.layer_norm(embeddings, normalized_shape=(embeddings.shape[1],))
embeddings = embeddings[:, :matryoshka_dim]
return embeddings
if __name__ == "__main__":
parser = HfArgumentParser((ScriptArguments, BCOConfig, ModelConfig))
script_args, training_args, model_args = parser.parse_args_into_dataclasses()
training_args.gradient_checkpointing_kwargs = {"use_reentrant": True}
# Load a pretrained model
model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code
)
ref_model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code
)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
accelerator = Accelerator()
embedding_model = AutoModel.from_pretrained(
"nomic-ai/nomic-embed-text-v1.5",
trust_remote_code=model_args.trust_remote_code,
safe_serialization=True,
dtype=torch.bfloat16,
device_map="auto",
)
embedding_model = accelerator.prepare_model(embedding_model)
embedding_tokenizer = AutoTokenizer.from_pretrained(
"bert-base-uncased", trust_remote_code=model_args.trust_remote_code
)
embedding_func = partial(
embed_prompt,
model=embedding_model,
)
# Initialize the BCO trainer
trainer = BCOTrainer(
model,
ref_model,
args=training_args,
train_dataset=dataset[script_args.dataset_train_split],
eval_dataset=dataset[script_args.dataset_test_split] if training_args.eval_strategy != "no" else None,
processing_class=tokenizer,
peft_config=get_peft_config(model_args),
embedding_func=embedding_func,
embedding_tokenizer=embedding_tokenizer,
)
# Train and push the model to the Hub
trainer.train()
# Save and push to hub
trainer.save_model(training_args.output_dir)
if training_args.push_to_hub:
trainer.push_to_hub(dataset_name=script_args.dataset_name)