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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 | # 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,quantization]",
# "transformers>=5.3.0",
# "trackio",
# "mamba_ssm==2.2.5",
# "causal_conv1d==1.5.2",
# ]
# ///
"""
Fine-tune NVIDIA Nemotron 3 models with SFT.
Prerequisites:
pip install "transformers>=5.3.0"
pip install --no-build-isolation mamba_ssm==2.2.5
pip install --no-build-isolation causal_conv1d==1.5.2
Example:
accelerate launch \
--config_file examples/accelerate_configs/deepspeed_zero3.yaml \
examples/scripts/sft_nemotron_3.py \
--dtype bfloat16 \
--model_name_or_path nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 \
--attn_implementation eager \
--dataset_name HuggingFaceH4/Multilingual-Thinking \
--max_length 128 \
--per_device_train_batch_size 1 \
--gradient_accumulation_steps 4 \
--num_train_epochs 1 \
--learning_rate 2e-4 \
--optim paged_adamw_8bit \
--logging_steps 10 \
--output_dir nemotron-3-sft \
--report_to trackio \
--use_peft \
--lora_r 8 \
--lora_alpha 16 \
--lora_target_modules q_proj k_proj v_proj o_proj gate_proj up_proj down_proj
"""
from datasets import load_dataset
from transformers import AutoModelForCausalLM
from trl import ModelConfig, ScriptArguments, SFTConfig, SFTTrainer, TrlParser, get_peft_config
def main(script_args, training_args, model_args):
# NemotronH does not support gradient checkpointing
training_args.gradient_checkpointing = False
# Load model
model_kwargs = dict(
revision=model_args.model_revision,
attn_implementation=model_args.attn_implementation,
dtype=model_args.dtype,
)
model = AutoModelForCausalLM.from_pretrained(model_args.model_name_or_path, **model_kwargs)
# Load dataset
dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
# Merge thinking into message content using <think> tags and remove extra columns
def merge_thinking_and_remove_key(example):
new_messages = []
for msg in example["messages"]:
content = msg["content"]
thinking = msg.get("thinking")
if thinking and isinstance(thinking, str) and thinking.strip():
content = f"<think>\n{thinking}\n</think>\n{content}"
new_messages.append({"role": msg["role"], "content": content})
example["messages"] = new_messages
return example
dataset = dataset.map(merge_thinking_and_remove_key)
# Prepare eval dataset if needed
eval_dataset = None
if training_args.eval_strategy != "no" and script_args.dataset_test_split in dataset:
eval_dataset = dataset[script_args.dataset_test_split]
# Train model
trainer = SFTTrainer(
model=model,
args=training_args,
train_dataset=dataset[script_args.dataset_train_split],
eval_dataset=eval_dataset,
peft_config=get_peft_config(model_args),
)
trainer.train()
trainer.save_model(training_args.output_dir)
if training_args.push_to_hub:
trainer.push_to_hub(dataset_name=script_args.dataset_name)
if __name__ == "__main__":
parser = TrlParser((ScriptArguments, SFTConfig, ModelConfig))
script_args, training_args, model_args, _ = parser.parse_args_and_config(return_remaining_strings=True)
main(script_args, training_args, model_args)
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