This repository demonstrates an *advanced pipeline for training a ChatGPT-like or Claude-like model. The pipeline includes:
** Pre-Training** (optional) ** Supervised Fine-Tuning (3FT)* ** Reward Modeling (pairwise preference data) ** RLHF (pconceptual PPO script)
=== Setup ===
- Install dependencies
pip install -r requirements.txt- Prepare data
"- Place raw text in data/raw/ for pre-training.
"- Place instruction’response data in data/sft/ for SFT.
"- Place pairwise preference data in data/reward/ for reward modeling.
- Edit Configs
- Adjust hyperparameters in
configs/.
- Run
- Pre-train:
bash scripts/run_pretrain.sh- SFT:
bash scripts/run_sft.sh- Reward model:
bash scripts/run_reward_model.sh- RLHF (placeholder):
bash scripts/run_rlhf.sh- Inference
- Load a checkpoint and generate text:
python src/inference.py=== Notes ===
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For large models, configure Accelerate and possibly use DeepSpeed or FSDP.
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SFT, Reward Modeling, and RLHF require carefully curated datasets.
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The code here is a demonstration scaffold; modify to your needs and environment.