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303ed94 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 | # 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",
# "trackio",
# "kernels",
# ]
# ///
"""
Simple Self-Distillation (SSD) training for code generation.
Implements the method from "Embarrassingly Simple Self-Distillation Improves Code Generation" (Zhang et al., 2026):
sample completions from the model at a training-time temperature and truncation, then fine-tune on those raw,
unverified samples with standard cross-entropy loss. No reward model, verifier, teacher, or RL needed.
The dataset only requires a ``prompt`` column containing coding problem prompts.
Example:
```bash
python examples/scripts/ssd.py \
--model_name_or_path Qwen/Qwen3-4B-Instruct-2507 \
--dataset_name microsoft/rStar-Coder \
--dataset_config seed_sft \
--prompt_column question \
--output_dir outputs/ssd-qwen3-4b \
--per_device_train_batch_size 1 \
--gradient_accumulation_steps 32 \
--learning_rate 5e-6 \
--lr_scheduler_type cosine \
--max_prompt_length 1024 \
--max_completion_length 65536 \
--temperature 1.6 \
--top_k 20 \
--top_p 0.8 \
--num_train_epochs 1 \
--bf16 \
--report_to trackio
```
"""
from dataclasses import dataclass, field
import torch
from datasets import DatasetDict, load_dataset, load_from_disk
from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import (
ModelConfig,
ScriptArguments,
TrlParser,
get_kbit_device_map,
get_peft_config,
get_quantization_config,
)
from trl.experimental.ssd import SSDConfig, SSDTrainer
@dataclass
class SSDScriptArguments(ScriptArguments):
dataset_path: str | None = field(
default=None,
metadata={"help": "Optional local dataset path to load with `load_from_disk`. Overrides `dataset_name`."},
)
prompt_column: str = field(
default="prompt",
metadata={"help": "Column containing the problem prompts."},
)
if __name__ == "__main__":
parser = TrlParser((SSDScriptArguments, SSDConfig, ModelConfig))
script_args, training_args, model_args = parser.parse_args_and_config()
if model_args.model_name_or_path is None:
raise ValueError("`model_name_or_path` is required.")
if model_args.dtype in ["auto", None]:
if training_args.bf16:
dtype = torch.bfloat16
elif training_args.fp16:
dtype = torch.float16
else:
dtype = "auto"
else:
dtype = 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:
model_kwargs["device_map"] = get_kbit_device_map()
model_kwargs["quantization_config"] = quantization_config
training_args.model_init_kwargs = model_kwargs
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path, revision=model_args.model_revision, padding_side="left"
)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
# Load dataset
if script_args.dataset_path is not None:
dataset = load_from_disk(script_args.dataset_path)
else:
dataset = load_dataset(
script_args.dataset_name,
name=script_args.dataset_config,
streaming=script_args.dataset_streaming,
)
# Ensure the dataset has a `prompt` column
def _prepare_split(ds):
if script_args.prompt_column != "prompt" and script_args.prompt_column in ds.column_names:
ds = ds.rename_column(script_args.prompt_column, "prompt")
return ds.select_columns(["prompt"])
if isinstance(dataset, DatasetDict):
train_dataset = _prepare_split(dataset[script_args.dataset_train_split])
eval_dataset = None
if script_args.dataset_test_split in dataset:
eval_dataset = _prepare_split(dataset[script_args.dataset_test_split])
else:
train_dataset = _prepare_split(dataset)
eval_dataset = None
model = AutoModelForCausalLM.from_pretrained(model_args.model_name_or_path, **model_kwargs)
model.config.use_cache = False if training_args.gradient_checkpointing else True
trainer = SSDTrainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
processing_class=tokenizer,
peft_config=get_peft_config(model_args),
)
trainer.train()
trainer.save_model(training_args.output_dir)
if eval_dataset is not None:
metrics = trainer.evaluate()
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
if training_args.push_to_hub:
trainer.push_to_hub(dataset_name=script_args.dataset_name or script_args.dataset_path)
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