Instructions to use OpenResearcher/OpenResearcher-30B-A3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenResearcher/OpenResearcher-30B-A3B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="OpenResearcher/OpenResearcher-30B-A3B-GGUF",
	filename="OpenResearcher-30B-A3B-BF16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use OpenResearcher/OpenResearcher-30B-A3B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf OpenResearcher/OpenResearcher-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf OpenResearcher/OpenResearcher-30B-A3B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf OpenResearcher/OpenResearcher-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf OpenResearcher/OpenResearcher-30B-A3B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf OpenResearcher/OpenResearcher-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf OpenResearcher/OpenResearcher-30B-A3B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf OpenResearcher/OpenResearcher-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf OpenResearcher/OpenResearcher-30B-A3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/OpenResearcher/OpenResearcher-30B-A3B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use OpenResearcher/OpenResearcher-30B-A3B-GGUF with Ollama:
```
ollama run hf.co/OpenResearcher/OpenResearcher-30B-A3B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use OpenResearcher/OpenResearcher-30B-A3B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for OpenResearcher/OpenResearcher-30B-A3B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for OpenResearcher/OpenResearcher-30B-A3B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for OpenResearcher/OpenResearcher-30B-A3B-GGUF to start chatting

Pi new

How to use OpenResearcher/OpenResearcher-30B-A3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf OpenResearcher/OpenResearcher-30B-A3B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "OpenResearcher/OpenResearcher-30B-A3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use OpenResearcher/OpenResearcher-30B-A3B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf OpenResearcher/OpenResearcher-30B-A3B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default OpenResearcher/OpenResearcher-30B-A3B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use OpenResearcher/OpenResearcher-30B-A3B-GGUF with Docker Model Runner:
```
docker model run hf.co/OpenResearcher/OpenResearcher-30B-A3B-GGUF:Q4_K_M
```

Lemonade

How to use OpenResearcher/OpenResearcher-30B-A3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OpenResearcher/OpenResearcher-30B-A3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.OpenResearcher-30B-A3B-GGUF-Q4_K_M

List all available models

lemonade list

Adopted by NVIDIA's Nemotron family of models!

🤗 HuggingFace ｜ Blog ｜ Slack | WeChat

Overview

OpenResearcher is a fully open agentic large language model (30B-A3B) designed for long-horizon deep research scenarios. It achieves an impressive 54.8% accuracy on BrowseComp-Plus, surpassing performance of GPT-4.1, Claude-Opus-4, Gemini-2.5-Pro, DeepSeek-R1 and Tongyi-DeepResearch. It also demonstrates leading performance across a range of deep research benchmarks, including BrowseComp, GAIA, WebWalkerQA, and xbench-DeepSearch. We fully open-source the training and evaluation recipe—including data, model, training methodology, and evaluation framework for everyone to progress deep research.

OpenResearcher-30B-A3B-GGUF

Note: For the best performance, we recommend using OpenResearcher-30B-A3B.

To support efficient deployment, we release several quantized versions of OpenResearcher-30B-A3B, including Q4_K_M, Q5_0, Q5_K_M, Q6_K, and Q8_0.

Quantization	File Size	BPW	PPL	+/-	Tokens/sec
BF16	58.84 GiB	16.00	8.4522	0.06489	4,117.90
Q8_0	31.27 GiB	8.51	8.4654	0.06499	7,490.81
Q6_K	31.20 GiB	8.49	8.4784	0.06510	7,389.76
Q5_0	20.37 GiB	5.54	8.5462	0.06558	7,534.66
Q4_K_M	22.82 GiB	6.21	8.5970	0.06610	7,046.96
Q5_K_M	24.24 GiB	6.60	8.6074	0.06625	6,661.48

Citation

@article{li2026openresearcher,
  title={{OpenResearcher: A Fully Open Pipeline for Long-Horizon Deep Research Trajectory Synthesis}},
  author={Li, Zhuofeng and Jiang, Dongfu and Ma, Xueguang and Zhang, Haoxiang and Nie, Ping and Zhang, Yuyu and Zou, Kai and Xie, Jianwen and Zhang, Yu and Chen, Wenhu},
  journal={arXiv preprint arXiv:2603.20278},
  year={2026}
}

Downloads last month: 99

GGUF

Model size

32B params

Architecture

nemotron_h_moe

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

Inference Providers NEW

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Model tree for OpenResearcher/OpenResearcher-30B-A3B-GGUF

Base model

nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16

Quantized

(2)

this model

Dataset used to train OpenResearcher/OpenResearcher-30B-A3B-GGUF

Paper for OpenResearcher/OpenResearcher-30B-A3B-GGUF

OpenResearcher: A Fully Open Pipeline for Long-Horizon Deep Research Trajectory Synthesis

Paper • 2603.20278 • Published Mar 17 • 98