初始化项目，由ModelHub XC社区提供模型

Model: SeongryongJung/Qwen3-4B-Chemistry-SRPO-TR
Source: Original Platform

README.md

@@ -0,0 +1,66 @@
+---
+license: apache-2.0
+base_model: Qwen/Qwen3-4B
+library_name: transformers
+tags:
+- qwen3
+- reinforcement-learning
+- trust-region
+- sdpo
+- srpo
+---
+
+# Qwen3-4B-Chemistry-SRPO-TR
+
+This repository contains the Qwen3-4B `Chemistry` `SRPO-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 | SRPO-TR | Qwen3-4B | 32 | 76.61% | 100 | 76.61% | 100 |
+
+![Training and validation scores](results/training_score.png)
+
+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 | `SRPO-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 | `5e-6` |
+| vLLM GPU memory utilization | `0.8` |
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+repo_id = "SeongryongJung/Qwen3-4B-Chemistry-SRPO-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,
+)
+```