---
license: mit
language:
- en
library_name: transformers
pipeline_tag: text-generation
base_model: Qwen/Qwen3-4B
tags:
- qwen3
- ssd
- self-distillation
- rlve
---

# Qwen3-4B SSD (RLVE Eval20, N=20) — global step 500

Weights merged from VERL FSDP SFT checkpoint **`global_step_500`** (500 optimizer steps, 1 epoch schedule) of
**Simple Self-Distillation (SSD)** applied to **Qwen/Qwen3-4B**:
sample N=20 self-generated responses from the frozen base model, then SFT on those samples.

## Training data

Parquet SFT corpus (16k rows, `messages` column):
[CL-From-Nothing/RLVE-Eval20-Qwen3-4B-SSD-N20-SFT-Train](https://huggingface.co/datasets/CL-From-Nothing/RLVE-Eval20-Qwen3-4B-SSD-N20-SFT-Train).

Companion 1.7B model: [CL-From-Nothing/Qwen3-1-7B-SSD-RLVE-Eval20-N20-global-step-500](https://huggingface.co/CL-From-Nothing/Qwen3-1-7B-SSD-RLVE-Eval20-N20-global-step-500).

## Load

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "CL-From-Nothing/Qwen3-4B-SSD-RLVE-Eval20-N20-global-step-500"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
```