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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -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 serve -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 Desktop

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 serve -hf OpenResearcher/OpenResearcher-30B-A3B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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

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

Hermes Agent

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 serve -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

Atomic Chat

OpenClaw

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

Start the llama.cpp server

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

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "OpenResearcher/OpenResearcher-30B-A3B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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

Core Contributors

Advisors

_{Wenhu Chen}

_{Yu Zhang}

Acknowledgements

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: 741

GGUF

Model size

32B params

Architecture

nemotron_h_moe

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

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

Base model

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

Quantized

(4)

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 • 102