Instructions to use AI4SGI/ExoMind-F16-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 AI4SGI/ExoMind-F16-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 AI4SGI/ExoMind-F16-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf AI4SGI/ExoMind-F16-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AI4SGI/ExoMind-F16-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf AI4SGI/ExoMind-F16-GGUF:F16

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 AI4SGI/ExoMind-F16-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf AI4SGI/ExoMind-F16-GGUF:F16

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 AI4SGI/ExoMind-F16-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AI4SGI/ExoMind-F16-GGUF:F16

Use Docker

docker model run hf.co/AI4SGI/ExoMind-F16-GGUF:F16

LM Studio
Jan

vLLM

How to use AI4SGI/ExoMind-F16-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AI4SGI/ExoMind-F16-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AI4SGI/ExoMind-F16-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/AI4SGI/ExoMind-F16-GGUF:F16

Ollama
How to use AI4SGI/ExoMind-F16-GGUF with Ollama:
```
ollama run hf.co/AI4SGI/ExoMind-F16-GGUF:F16
```

Unsloth Studio

How to use AI4SGI/ExoMind-F16-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 AI4SGI/ExoMind-F16-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 AI4SGI/ExoMind-F16-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AI4SGI/ExoMind-F16-GGUF to start chatting

How to use AI4SGI/ExoMind-F16-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AI4SGI/ExoMind-F16-GGUF:F16

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": "AI4SGI/ExoMind-F16-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AI4SGI/ExoMind-F16-GGUF with Docker Model Runner:
```
docker model run hf.co/AI4SGI/ExoMind-F16-GGUF:F16
```

Lemonade

How to use AI4SGI/ExoMind-F16-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AI4SGI/ExoMind-F16-GGUF:F16

Run and chat with the model

lemonade run user.ExoMind-F16-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use AI4SGI/ExoMind-F16-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 AI4SGI/ExoMind-F16-GGUF:F16

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 AI4SGI/ExoMind-F16-GGUF:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AI4SGI/ExoMind-F16-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AI4SGI/ExoMind-F16-GGUF:F16

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 "AI4SGI/ExoMind-F16-GGUF:F16" \
  --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"

ExoMind: Democratizing Scientific Intelligence via Extended-Mind-Inspired Agentic System

ExoMind Team · Shanghai Artificial Intelligence Laboratory

Overview

Reference-precision GGUF release of ExoMind. Download all four numbered shards and pass the first shard to llama.cpp; the remaining shards are discovered automatically.

This repository intentionally contains only the F16 model and the matching multimodal projector. Keeping each precision in its own repository makes downloads, local disk requirements, and deployment commands explicit.

Files

File	Role	Download size
`qwen3_5_35b_a3b-F16-00001-of-00004.gguf`	F16 shard 1/4	18.15 GiB
`qwen3_5_35b_a3b-F16-00002-of-00004.gguf`	F16 shard 2/4	18.31 GiB
`qwen3_5_35b_a3b-F16-00003-of-00004.gguf`	F16 shard 3/4	18.18 GiB
`qwen3_5_35b_a3b-F16-00004-of-00004.gguf`	F16 shard 4/4	11.55 GiB
`mmproj-qwen3_5_35b_a3b-F16.gguf`	F16 multimodal projector	857.62 MiB

Quick Start with llama.cpp

Text-only serving:

llama-server \
  -m qwen3_5_35b_a3b-F16-00001-of-00004.gguf \
  --ctx-size 32768 \
  --host 0.0.0.0 \
  --port 8080

For image input, load the projector shipped in this repository:

llama-server \
  -m qwen3_5_35b_a3b-F16-00001-of-00004.gguf \
  --mmproj mmproj-qwen3_5_35b_a3b-F16.gguf \
  --ctx-size 32768 \
  --host 0.0.0.0 \
  --port 8080

Conversion Provenance

The original 71,066,994,432-byte F16 GGUF was split with llama-gguf-split at a 20 GB maximum shard size using llama.cpp revision 7584430. The four shards contain all 753 tensors, add 128 bytes of split metadata, and passed the pinned tool's merge dry run. The original unsplit file remains intact. These GGUF files were supplied as existing release artifacts. Their exact filenames, byte sizes, and GGUF v3 headers were validated before publication, but the original HF-to-GGUF conversion and quantization commands were not retained with the files. The repository therefore does not claim bit-for-bit reproducibility of the original conversion pipeline.

Evaluation Boundary

Published benchmark results use the original Transformers BF16 checkpoint. The F16 GGUF packaging has not been assigned separate scores.

Complete settings and comparisons are available in the evaluation explorer.

License and Attribution

The model files and upstream Qwen3.5 materials are distributed under the Apache License 2.0 included with the model. Technical-report text, scientific figures, results, and ExoMind brand assets are governed by the ExoMind Research Content and Brand Terms. See NOTICE.md for third-party notices.

Citation

@misc{exomind2026,
  title  = {ExoMind: Democratizing Scientific Intelligence via Extended-Mind-Inspired Agentic System},
  author = {Peng Ye and Zhuo Liu and Jingqi Ye and Fangchen Yu and Shengji Tang and Yichen Jiang and Haonan He and Zongsheng Cao and Tao Chen and Bo Zhang and Wanli Ouyang and Bowen Zhou and Lei Bai},
  year   = {2026},
  note   = {Technical report},
  url    = {https://github.com/AI4SGI/ExoMind/blob/main/Paper.pdf}
}