---
license: apache-2.0
base_model: BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-MixSFT
datasets:
- BytedTsinghua-SIA/Open-MOPD-Data
library_name: transformers
pipeline_tag: text-generation
language:
- en
tags:
- smollm3
- open-mopd
- reinforcement-learning
- code
---

# Open-MOPD-SmolLM3-3B-RL-Code

This is the code-domain teacher in the Open-MOPD pipeline. It starts from
`BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-MixSFT` and is trained only on code
prompts with verifiable rewards using GRPO. This release corresponds to
training step 180.

Training uses global batch size 128, mini-batch size 32, learning rate `1e-6`,
rollout group size 16, a 30,000-token response limit, no KL penalty, and
accuracy-based group filtering.

## Results

| Model | LiveCodeBench v5 | LiveCodeBench v6 | Code average |
|---|---:|---:|---:|
| **RL-Code teacher** | **22.16** | **21.31** | **21.73** |
| MixSFT starting point | 15.99 | 19.20 | 17.60 |

Results use avg@10 rather than best@10, with temperature 1.0,
`max_model_len=32768`, `top_p=0.95`, `top_k=-1`, and
`stop_token_ids=[128012]`.

The code portion of the RL prompt mixture explicitly excludes LiveCodeBench.
The decontamination record is available in
`BytedTsinghua-SIA/Open-MOPD-Data` under `rl_prompt_mix/manifest.json`.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-Code"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype="bfloat16", device_map="auto")
```

## Intended use and limitations

This is a domain teacher intended for distillation, not a general-purpose
assistant. It was optimized only on code and can perform worse than MixSFT on
other domains.

## Model specifications

- Architecture: `SmolLM3ForCausalLM`
- Parameters: approximately 3B
- Layers: 36
- Vocabulary size: 128,256
- Weights: BF16, approximately 6.2 GB
- Includes tokenizer and chat template