Instructions to use Xkev/deepresearch_single with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Xkev/deepresearch_single with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Xkev/deepresearch_single") model = AutoModelForMultimodalLM.from_pretrained("Xkev/deepresearch_single", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,048 Bytes
cb68937 5e8711b cb68937 | 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 | ---
license: apache-2.0
base_model:
- google/gemma-4-E2B-it
language:
- en
pipeline_tag: any-to-any
library_name: transformers
tags:
- deep-research
- gemma4
---
# gemma-4-E2B-it — Deep Research Single-Agent RL
Paper: [Learning Self-Orchestration in Decentralized Agent Swarms](https://arxiv.org/abs/2610.22322)
Code: [https://github.com/Embodied-Minds-Lab/MPT](https://github.com/Embodied-Minds-Lab/MPT)
For the three-agent counterpart, see [`Xkev/deepresearch_society`](https://huggingface.co/Xkev/deepresearch_society).
## Training
- **Initialization**: single-agent SFT cold start of [`google/gemma-4-E2B-it`](https://huggingface.co/google/gemma-4-E2B-it)
- **Dataset**: OpenResearcher deep-research tasks
- **Framework**: [verl](https://github.com/volcengine/verl)
- **Algorithm**: monolithic REINFORCE
- **Hyperparameters**: `lr=1e-6`, `lr_warmup_ratio=0`, constant schedule
## Intended use
Research on multi-agent post-training and deep research agents. Not intended for general dialog or production.
## License
Apache-2.0.
|