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wisdompan/qwen35-2b-mopd-mixsft
qwen35-2b-mopd-mixsft is a image-text-to-text model from wisdompan. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
This repository contains the full-parameter MixSFT initialization used for an Open-MOPD reproduction based on Qwen3.5-2B. It is intended as the common starting point for the Math, Code, and Instruction-Following teach…
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From the Hugging Face model README
This repository contains the full-parameter MixSFT initialization used for an Open-MOPD reproduction based on Qwen3.5-2B. It is intended as the common starting point for the Math, Code, and Instruction-Following teacher policies.
Status: MixSFT checkpoint, not the final MOPD student. Teacher RL and student distillation checkpoints will be published separately after they pass stability checks and evaluation.
基于 Qwen3.5-2B 的 MOPD 复现实验 MixSFT 初始化权重,覆盖数学、 代码和指令遵循数据。它不是最终 MOPD student;后续稳定的 teacher 和 student 权重会分阶段发布。
| Item | Value |
|---|---|
| Base model | Qwen/Qwen3.5-2B |
| Parameters | 1,881,825,088 |
| Fine-tuning | Full parameter supervised fine-tuning |
| Domains | Math, code, instruction following |
| Precision | bfloat16 |
| Context length used for SFT | 1,024 tokens |
| Checkpoint | 1 epoch / step 375 |
| Weight format | SafeTensors |
The checkpoint was trained with Adafactor, a cosine learning-rate schedule,
peak learning rate 1e-5, weight decay 0.1, gradient accumulation 8,
gradient checkpointing, and seed 20260902. The final recorded training loss
was 0.6251 and token accuracy was 0.8485.
This model is not presented as a production-ready assistant or as the final result of the MOPD experiment.
Qwen3.5 requires a recent Transformers build. The serving flow follows the upstream Qwen3.5 recommendation:
pip install "transformers[serving] @ git+https://github.com/huggingface/transformers.git@main"
transformers serve \
--force-model wisdompan/qwen35-2b-mopd-mixsft \
--port 8000 \
--continuous-batching
Then call the OpenAI-compatible endpoint:
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="unused")
response = client.chat.completions.create(
model="wisdompan/qwen35-2b-mopd-mixsft",
messages=[{"role": "user", "content": "Solve: 2x + 3 = 11"}],
)
print(response.choices[0].message.content)
Only a six-example pipeline sanity check was bundled with this checkpoint. It is useful for confirming that generation and reward routing work, but it is too small to report as a benchmark. Broader evaluation will accompany the final teacher and student releases.
Qwen/Qwen3.5-2B
-> MixSFT (this repository)
-> Math / Code / IF teacher RL
-> MOPD student distillation
This derivative is released under the Apache License 2.0, following the base model license. Users must also comply with the terms and usage requirements of the upstream Qwen3.5-2B model.