sft init save

cua_lite/train/sft.py

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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",
+#     "Pillow>=9.4.0",
+#     "peft",
+#     "trackio",
+#     "kernels",
+# ]
+# ///
+
+"""
+pip install pillow
+
+# Tested on 8x H100 GPUs
+accelerate launch \
+    --config_file examples/accelerate_configs/deepspeed_zero3.yaml \
+    examples/scripts/sft_vlm.py \
+    --dataset_name HuggingFaceH4/llava-instruct-mix-vsft \
+    --model_name_or_path llava-hf/llava-1.5-7b-hf \
+    --gradient_accumulation_steps 8 \
+    --output_dir LLaVA-1.5-7B-SFT \
+    --dtype bfloat16
+
+For LLaVA-NeXT, use:
+    --model_name_or_path llava-hf/llava-v1.6-mistral-7b-hf
+
+For meta-llama/Llama-3.2-11B-Vision-Instruct, use:
+    --model_name_or_path meta-llama/Llama-3.2-11B-Vision-Instruct
+
+accelerate launch \
+    --config_file examples/accelerate_configs/deepspeed_zero3.yaml \
+    examples/scripts/sft_vlm.py \
+    --dataset_name HuggingFaceH4/llava-instruct-mix-vsft \
+    --model_name_or_path HuggingFaceTB/SmolVLM-Instruct \
+    --per_device_train_batch_size 1 \
+    --gradient_accumulation_steps 1 \
+    --output_dir SmolVLM-SFT \
+    --dtype bfloat16 \
+    --use_peft \
+    --lora_target_modules down_proj, o_proj, k_proj, q_proj, gate_proj, up_proj, v_proj
+"""
+
+import os
+
+import torch
+from datasets import load_dataset
+from transformers import AutoModelForImageTextToText
+
+from trl import (
+    ModelConfig,
+    ScriptArguments,
+    SFTConfig,
+    SFTTrainer,
+    TrlParser,
+    get_kbit_device_map,
+    get_peft_config,
+    get_quantization_config,
+)
+
+
+# Enable logging in a Hugging Face Space
+os.environ.setdefault("TRACKIO_SPACE_ID", "trl-trackio")
+
+if __name__ == "__main__":
+    parser = TrlParser((ScriptArguments, SFTConfig, ModelConfig))
+    script_args, training_args, model_args = parser.parse_args_and_config()
+    training_args.max_length = None
+
+    ################
+    # Model
+    ################
+    dtype = model_args.dtype if model_args.dtype in ["auto", None] else getattr(torch, model_args.dtype)
+    model_kwargs = dict(
+        revision=model_args.model_revision,
+        attn_implementation=model_args.attn_implementation,
+        dtype=dtype,
+    )
+    quantization_config = get_quantization_config(model_args)
+    if quantization_config is not None:
+        # Passing None would not be treated the same as omitting the argument, so we include it only when valid.
+        model_kwargs["device_map"] = get_kbit_device_map()
+        model_kwargs["quantization_config"] = quantization_config
+
+    model = AutoModelForImageTextToText.from_pretrained(
+        model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code, **model_kwargs
+    )
+
+    ################
+    # Dataset
+    ################
+    dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
+
+    ################
+    # Training
+    ################
+    trainer = SFTTrainer(
+        model=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,
+        peft_config=get_peft_config(model_args),
+    )
+
+    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)