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1fa3c6c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 | # 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)
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