license: apache-2.0
language:
- en
datasets:
- AweAI-Team/CalibForge
base_model:
- Qwen/Qwen3.5-35B-A3B
CalibForge-35B-A3B
📄 Paper · 💻 Repository · 🤗 Dataset · 🤖 30B Model
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
| Model | Terminal-Bench 2.0 Acc. (%) ↑ | SWE-bench Pro Resolved (%) ↑ | Doc2Repo Pass Rate (%) ↑ |
|---|---|---|---|
| Qwen3.5-35B-A3B (base) | 39.10 ± 1.09 | 41.29 | 44.92 ± 1.14 |
| CalibForge-35B-A3B (this model) | 47.57 ± 0.99 | 44.32 | 48.77 ± 0.90 |
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.
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
| Property | Value |
|---|---|
| Backbone | Qwen3.5-35B-A3B |
| 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.
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}
}