---
license: apache-2.0
language:
- en
datasets:
- AweAI-Team/CalibForge
base_model:
- Qwen/Qwen3-30B-A3B-Instruct-2507
---
# CalibForge-30B-A3B
📄 Paper ·
💻 Repository ·
🤗 Dataset ·
🤖 35B Model
CalibForge-30B-A3B is a terminal-agent model fine-tuned from [Qwen3-30B-A3B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507) using full-parameter, multi-turn supervised fine-tuning. Its training trajectories were distilled from **5,431** calibrated terminal tasks spanning 16 domains. **CalibForge** uses verified solver outcomes and full trajectories to revise executable terminal tasks toward a solver-relative learnable zone. See the [paper](https://arxiv.org/abs/2608.06352) for the task-construction pipeline.
## Evaluation Results
| Model | Terminal-Bench 2.0 Acc. (%) ↑ | SWE-bench Pro Resolved (%) ↑ | Doc2Repo Pass Rate (%) ↑ |
|---|---:|---:|---:|
| Qwen3-30B-A3B-Instruct-2507 (base) | 7.87 ± 0.00 | 3.26 | 5.94 ± 0.88 |
| **CalibForge-30B-A3B (this model)** | **32.58 ± 1.12** | **30.94** | **35.98 ± 1.82** |
Terminal-Bench 2.0 and Doc2Repo results are reported as mean ± SEM over three runs; SWE-bench Pro is evaluated once. Terminal-Bench 2.0 uses [CalibForge-Eval](https://github.com/AweAI-Team/AweAgent/tree/main/recipes/terminal_bench_v2) with a 500-step limit, a one-hour per-task timeout, and sandboxes capped at 16 CPUs and 32 GB RAM. SWE-bench Pro and Doc2Repo use their official evaluation scaffolds; corresponding AweAgent recipes are available for [SWE-bench Pro](https://github.com/AweAI-Team/AweAgent/tree/main/recipes/swe_bench_pro) and [Doc2Repo through BeyondSWE](https://github.com/AweAI-Team/AweAgent/tree/main/recipes/beyond_swe).
## Usage with SGLang
Install a recent SGLang release with Qwen3 MoE support, then launch an OpenAI-compatible server:
```bash
uv venv --python 3.12 .venv
source .venv/bin/activate
uv pip install "sglang[all]"
python -m sglang.launch_server \
--model-path AweAI-Team/CalibForge-30B-A3B \
--tp 8 \
--dp 1 \
--trust-remote-code \
--enable-metrics \
--max-running-requests 40 \
--tool-call-parser qwen \
--mem-fraction-static 0.95 \
--host :: \
--port 40003
```
## Model Details
| Property | Value |
|---|---|
| Backbone | Qwen3-30B-A3B-Instruct-2507 |
| Precision | BF16 |
| Configured maximum positions | 262,144 |
| Training method | Full-parameter, multi-turn SFT |
| Training context length | 131,072 tokens |
| Training epochs | 10 |
| Optimizer | AdamW (`β₁=0.9`, `β₂=0.999`) |
| Learning rate | `1.0e-5`, cosine schedule, 0.05 warmup ratio |
| Global batch size | 128 |
| Training hardware | 64 × NVIDIA H20 GPUs |
## License
The model is released under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
## Citation
If you use this model, please cite the CalibForge paper:
```bibtex
@misc{meng2026calibforge,
title = {CalibForge: Adversarial Solver Calibration for Scaling Learnable Terminal Tasks},
author = {Fanzhe Meng and Guoxin Chen and Jiale Zhao and Shuang Sun and Zhiyu Lin and Wayne Xin Zhao and Ruihua Song and Ji-Rong Wen and Kai Jia},
year = {2026},
eprint = {2608.06352},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2608.06352}
}
```