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shunzou05
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Model Details
Model Provider
shunzou05
Model Tree
Qwen/Qwen3.5-122B-A10B
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/Qwen3.5-122B-A10B |
| 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 MoE:
python
from transformers import AutoProcessor, Qwen3_5MoeForConditionalGeneration model_id = "shunzou05/TerminalHorizon-122B-A10B" processor = AutoProcessor.from_pretrained(model_id)model = Qwen3_5MoeForConditionalGeneration.from_pretrained( model_id, dtype="auto", device_map="auto",)Refer to the base model documentation for inference and serving examples.
Paper links and the citation will be added with the publication details.
from transformers import AutoProcessor, Qwen3_5MoeForConditionalGeneration model_id = "shunzou05/TerminalHorizon-122B-A10B" processor = AutoProcessor.from_pretrained(model_id)model = Qwen3_5MoeForConditionalGeneration.from_pretrained( model_id, dtype="auto", device_map="auto",)