Qwen3 4B Chemistry SDPO TR

--- license: apache-2.0 base_model: Qwen/Qwen3-4B library_name: transformers tags: - qwen3 - reinforcement-learning - trust-region - sdpo - srpo --- # Qwen3-4B-Chemistry-SDPO-TR This repository contains the Qwen3-4B `Chemistry` `SDPO-TR` run trained with batch size 32. The repository root contains the **best validation checkpoint** (`global_step_100`), selected by validation `mean@16`. The best validation checkpoint is also the final checkpoint, so the repository root is both best and last. ## Performance | Dataset | Method | Base model | Train batch size | Best val mean@16 | Best checkpoint | Final val mean@16 | Final checkpoint | |---|---|---|---:|---:|---:|---:|---:| | Chemistry | SDPO-TR | Qwen3-4B | 32 | 74.91% | 100 | 74.91% | 100 |  Raw result files: - `results/validation_mean16.csv` - `results/training_scores.csv` - `artifacts/config.yaml` - `artifacts/wandb-summary.json` ## Training Hyperparameters | Hyperparameter | Value | |---|---:| | Base model | `Qwen/Qwen3-4B` | | Method | `SDPO-TR` | | Train batch size | `32` | | Train max samples | `3200` | | Total training steps | `100` | | Save frequency | `10` | | Test frequency | `10` | | Teacher regularization | `trust-region` | | Teacher update rate | `0.1` | | Rollout samples per prompt | `8` | | Validation samples per prompt | `16` | | Learning rate | `1e-5` | | vLLM GPU memory utilization | `0.8` | ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer repo_id = "SeongryongJung/Qwen3-4B-Chemistry-SDPO-TR" tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( repo_id, torch_dtype="auto", device_map="auto", trust_remote_code=True, ) ```