File size: 1,468 Bytes
63ffea5 0f9c531 63ffea5 0f9c531 e1cd058 0f9c531 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | ---
license: apache-2.0
base_model: Qwen/Qwen3-14B
language:
- en
library_name: transformers
tags:
- terminal-agents
- code-agent
- task-synthesis
- qwen3
---
# Meta-Task-14B
[Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) fine-tuned on 3,221 trajectories synthesized
by the Meta-Task framework, for solving terminal tasks.
This model accompanies the paper **"Meta-Task: Turning Terminal Task Synthesis into a Terminal
Task for Scalable Agent Training"** ([arXiv:2607.27929](https://arxiv.org/abs/2607.27929)).
Official code: https://github.com/goodmanpzh/meta-task
## Results
Terminal-Bench 2.0, Terminus-2 scaffold, non-thinking mode: **22.5 %** Avg Pass@1
(base Qwen3-14B: 5.2 %).
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "saulpzh/Meta-Task-14B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto")
```
## Training
LoRA SFT with the SWIFT framework (rank/alpha 64/128, dropout 0.05, lr 1e-4, cosine schedule,
5 epochs, sequence length 40,960, BF16, 8×A100 80GB with DeepSpeed ZeRO-3). Training data:
[saulpzh/Meta-Task-Data](https://huggingface.co/datasets/saulpzh/Meta-Task-Data).
## Links
- Code: https://github.com/goodmanpzh/meta-task
- Dataset: https://huggingface.co/datasets/saulpzh/Meta-Task-Data
- Meta-Task-32B: https://huggingface.co/saulpzh/Meta-Task-32B
## License
Apache-2.0.
|