67 lines
1.8 KiB
Markdown
67 lines
1.8 KiB
Markdown
---
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license: apache-2.0
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base_model: Qwen/Qwen3-4B
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library_name: transformers
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tags:
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- qwen3
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- reinforcement-learning
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- trust-region
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- sdpo
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- srpo
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---
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# Qwen3-4B-Chemistry-SRPO-TR
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This repository contains the Qwen3-4B `Chemistry` `SRPO-TR` run trained with batch size 32.
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The repository root contains the **best validation checkpoint** (`global_step_100`), selected by validation `mean@16`.
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The best validation checkpoint is also the final checkpoint, so the repository root is both best and last.
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## Performance
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| Dataset | Method | Base model | Train batch size | Best val mean@16 | Best checkpoint | Final val mean@16 | Final checkpoint |
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|---|---|---|---:|---:|---:|---:|---:|
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| Chemistry | SRPO-TR | Qwen3-4B | 32 | 76.61% | 100 | 76.61% | 100 |
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Raw result files:
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- `results/validation_mean16.csv`
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- `results/training_scores.csv`
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- `artifacts/config.yaml`
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- `artifacts/wandb-summary.json`
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## Training Hyperparameters
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| Hyperparameter | Value |
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|---|---:|
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| Base model | `Qwen/Qwen3-4B` |
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| Method | `SRPO-TR` |
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| Train batch size | `32` |
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| Train max samples | `3200` |
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| Total training steps | `100` |
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| Save frequency | `10` |
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| Test frequency | `10` |
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| Teacher regularization | `trust-region` |
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| Teacher update rate | `0.1` |
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| Rollout samples per prompt | `8` |
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| Validation samples per prompt | `16` |
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| Learning rate | `5e-6` |
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| vLLM GPU memory utilization | `0.8` |
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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repo_id = "SeongryongJung/Qwen3-4B-Chemistry-SRPO-TR"
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tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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repo_id,
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torch_dtype="auto",
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device_map="auto",
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trust_remote_code=True,
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)
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```
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