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
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Download README.md from Xkev/deepresearch_single: direct link, hf CLI and curl.
- Browser
- Download file 1.05 kB
-
https://huggingface.co/Xkev/deepresearch_single/resolve/main/README.md
- Command line
-
hf download hf://Xkev/deepresearch_single/README.md
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curl -L -o README.md https://huggingface.co/Xkev/deepresearch_single/resolve/main/README.md
1.05 kB
metadata
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
Code: https://github.com/Embodied-Minds-Lab/MPT
For the three-agent counterpart, see Xkev/deepresearch_society.
Training
- Initialization: single-agent SFT cold start of
google/gemma-4-E2B-it - Dataset: OpenResearcher deep-research tasks
- Framework: 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.