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Model: SeongryongJung/qwen3-4b-tooluse-srpo-ema005
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-4B
+library_name: transformers
+tags:
+- qwen3
+- reinforcement-learning
+- sdpo
+- srpo
+- ema
+---
+
+# qwen3-4b-tooluse-srpo-ema005
+
+This repository contains the **last checkpoint** (`global_step_100`) for `qwen3gen-tooluse-SRPO-Qwen-Qwen3-4B-mbs32-ema0.05-dwtrue-train64-rollout8-lr5e-6-vllm0.8`,
+converted to Hugging Face Transformers format.
+
+## Evaluation
+
+The reported headline score is the **best validation `mean@16` observed during training**.
+It is not necessarily the score of the uploaded last checkpoint.
+
+| Dataset | Method | Model | Uploaded checkpoint | Best val mean@16 | Best step | Final val mean@16 |
+|---|---|---|---|---:|---:|---:|
+| tooluse | SRPO | Qwen3-4B | global_step_100 | 62.59% | 40 | 57.26% |
+
+![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 Setup
+
+- Base model: `Qwen/Qwen3-4B`
+- Dataset: `tooluse`
+- Method: `SRPO`
+- EMA teacher update rate: `0.05`
+- Uploaded weights: last checkpoint, `global_step_100`
+- Validation metric used for headline score: `val-aux/*/mean@16`
+- Validation sampling: `n=16`
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+repo_id = "SeongryongJung/qwen3-4b-tooluse-srpo-ema005"
+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,
+)
+```
+
+## Notes
+
+Most best-validation intermediate checkpoints were not retained as full actor checkpoints because
+training kept only the latest actor checkpoint. Therefore, this repository publishes the last
+checkpoint and records the best validation score separately.