CalibForge-35B-A3B

CalibForge-35B-A3B

<p align="center"> <img src="assets/calibforge-overview.png" alt="Overview of CalibForge and its evaluation results" width="100%"> </p> <p align="center"> 📄 <a href="https://arxiv.org/abs/2608.06352">Paper</a> · 💻 <a href="https://github.com/AweAI-Team/CalibForge">Repository</a> · 🤗 <a href="https://huggingface.co/datasets/AweAI-Team/CalibForge">Dataset</a> · 🤖 <a href="https://huggingface.co/AweAI-Team/CalibForge-30B-A3B">30B Model</a> </p>

CalibForge-35B-A3B is a terminal-agent model fine-tuned from Qwen3.5-35B-A3B 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 for the task-construction pipeline.

Evaluation Results

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 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 and Doc2Repo through BeyondSWE. See the paper for comparisons with other training-data sources and for the complete evaluation and decontamination protocols.

<p align="center"> <img src="assets/tb2-category-results.png" alt="Terminal-Bench 2.0 tasks solved by category for CalibForge-35B-A3B and its base model" width="400"> </p>

Usage with SGLang

Install a recent SGLang release with Qwen3.5 MoE support, then launch an OpenAI-compatible server:

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-35B-A3B \
  --tp 8 \
  --dp 1 \
  --trust-remote-code \
  --enable-metrics \
  --max-running-requests 40 \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder \
  --mem-fraction-static 0.85 \
  --host :: \
  --port 40003

Model Details

License

The model is released under the Apache License 2.0.

Citation

If you use this model, please cite the CalibForge paper:

@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}
}