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47 lines
2.0 KiB
Markdown
47 lines
2.0 KiB
Markdown
<!--Copyright 2026 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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# TRL
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[TRL](https://huggingface.co/docs/trl/index) is a post-training framework for foundation models. It includes methods like SFT, GRPO, and DPO. Each method has a dedicated trainer that builds on the [`Trainer`] class and scales from a single GPU to multi-node clusters.
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```py
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from datasets import load_dataset
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from trl import GRPOTrainer
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from trl.rewards import accuracy_reward
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dataset = load_dataset("trl-lib/DeepMath-103K", split="train")
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trainer = GRPOTrainer(
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model="Qwen/Qwen2-0.5B-Instruct",
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reward_funcs=accuracy_reward,
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train_dataset=dataset,
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)
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trainer.train()
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```
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## Transformers integration
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TRL extends Transformers APIs and adds method-specific settings.
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- TRL trainers build on [`Trainer`]. Method-specific trainers like [`~trl.GRPOTrainer`] add generation, reward scoring, and loss computation. Config classes extend [`TrainingArguments`] with method-specific fields.
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- Model loading uses [`AutoConfig.from_pretrained`], then instantiates the model class from the config with that class' `from_pretrained`.
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## Resources
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- [TRL](https://huggingface.co/docs/trl/index) docs
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- [Fine Tuning with TRL](https://huggingface.co/datasets/trl-lib/documentation-images/resolve/main/Fine%20tuning%20with%20TRL%20(Oct%2025).pdf) talk |