GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
shunzou05
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
shunzou05
Model Tree
Qwen/Qwen3.5-27B
Input Modalities
Output Modalities
Supported Functionality
| Resource | Description |
|---|---|
| TerminalHorizon-Environment | Executable terminal-task environments. |
| TerminalHorizon-3K | Long-horizon training trajectories. |
| TerminalHorizon-27B | Fine-tuned Qwen3.5-27B. |
| TerminalHorizon-35B-A3B | Fine-tuned Qwen3.5-35B-A3B. |
| TerminalHorizon-122B-A10B | Fine-tuned Qwen3.5-122B-A10B. |
| Property | Value |
|---|---|
| Base model | Qwen/Qwen3.5-27B |
| Training method | Supervised fine-tuning |
| Training dataset | TerminalHorizon-3K |
| Training trajectories | 2,852 |
| Weight precision | BF16 |
| Checkpoint format | Hugging Face Transformers / Safetensors |
The terminal is a shared interaction interface, not a restriction to software tasks. The training environments span 25 professional domains and 463 subdomains, including software engineering, machine learning, education, agriculture, healthcare, finance, and scientific research.
TerminalHorizon focuses on meaningful work and connected agent behavior, rather than task length alone.
Use a version of Transformers that supports Qwen3.5:
python
from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration model_id = "shunzou05/TerminalHorizon-27B" processor = AutoProcessor.from_pretrained(model_id)model = Qwen3_5ForConditionalGeneration.from_pretrained( model_id, dtype="auto", device_map="auto",)Refer to the base model documentation for inference and serving examples.
bibtex
@misc{zou2026scalingagenticdatalonghorizon, title={Scaling Agentic Data for Long-Horizon Terminal Intelligence}, author={Shun Zou and Ziyu Ma and Yi Zou and Yong Wang and Lin Chen and Zehui Chen and Guanhua Chen and Xiangxiang Chu and Feng Zhao}, year={2026}, eprint={2610.13166}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2610.13166},}from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration model_id = "shunzou05/TerminalHorizon-27B" processor = AutoProcessor.from_pretrained(model_id)model = Qwen3_5ForConditionalGeneration.from_pretrained( model_id, dtype="auto", device_map="auto",)@misc{zou2026scalingagenticdatalonghorizon, title={Scaling Agentic Data for Long-Horizon Terminal Intelligence}, author={Shun Zou and Ziyu Ma and Yi Zou and Yong Wang and Lin Chen and Zehui Chen and Guanhua Chen and Xiangxiang Chu and Feng Zhao}, year={2026}, eprint={2610.13166}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2610.13166},}