Instructions to use prithivMLmods/AI4SGI-ExoMind-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/AI4SGI-ExoMind-9B-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="prithivMLmods/AI4SGI-ExoMind-9B-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("prithivMLmods/AI4SGI-ExoMind-9B-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use prithivMLmods/AI4SGI-ExoMind-9B-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 prithivMLmods/AI4SGI-ExoMind-9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/AI4SGI-ExoMind-9B-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 prithivMLmods/AI4SGI-ExoMind-9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/AI4SGI-ExoMind-9B-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 prithivMLmods/AI4SGI-ExoMind-9B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf prithivMLmods/AI4SGI-ExoMind-9B-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 prithivMLmods/AI4SGI-ExoMind-9B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf prithivMLmods/AI4SGI-ExoMind-9B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/prithivMLmods/AI4SGI-ExoMind-9B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use prithivMLmods/AI4SGI-ExoMind-9B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prithivMLmods/AI4SGI-ExoMind-9B-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": "prithivMLmods/AI4SGI-ExoMind-9B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/prithivMLmods/AI4SGI-ExoMind-9B-GGUF:Q4_K_M
- SGLang
How to use prithivMLmods/AI4SGI-ExoMind-9B-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "prithivMLmods/AI4SGI-ExoMind-9B-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/AI4SGI-ExoMind-9B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "prithivMLmods/AI4SGI-ExoMind-9B-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/AI4SGI-ExoMind-9B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use prithivMLmods/AI4SGI-ExoMind-9B-GGUF with Ollama:
ollama run hf.co/prithivMLmods/AI4SGI-ExoMind-9B-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use prithivMLmods/AI4SGI-ExoMind-9B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf prithivMLmods/AI4SGI-ExoMind-9B-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": "prithivMLmods/AI4SGI-ExoMind-9B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use prithivMLmods/AI4SGI-ExoMind-9B-GGUF with Docker Model Runner:
docker model run hf.co/prithivMLmods/AI4SGI-ExoMind-9B-GGUF:Q4_K_M
- Lemonade
How to use prithivMLmods/AI4SGI-ExoMind-9B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prithivMLmods/AI4SGI-ExoMind-9B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.AI4SGI-ExoMind-9B-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use prithivMLmods/AI4SGI-ExoMind-9B-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 prithivMLmods/AI4SGI-ExoMind-9B-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 prithivMLmods/AI4SGI-ExoMind-9B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use prithivMLmods/AI4SGI-ExoMind-9B-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf prithivMLmods/AI4SGI-ExoMind-9B-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 "prithivMLmods/AI4SGI-ExoMind-9B-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"
AI4SGI-ExoMind-9B-GGUF
ExoMind-9B is the compact checkpoint in Shanghai AI Laboratory's ExoMind family, fine-tuned from Qwen3.5-9B for lower-resource experimentation in scientific reasoning and agentic research under the same "extended-mind-inspired" paradigm as the larger 35B-A3B flagship — organizing the model, specialized interaction objects, and autonomous interaction processes into one unified system. It's trained via progressive Chain-of-Interaction (CoI) training on selected pure-reasoning and interaction trajectories, enabling workflows around source discovery, evidence grounding, executable verification, and observation integration for scientific inquiry, while retaining the native image-text multimodal capabilities of its Qwen3.5 base and supporting a 262,144-token context window. Formal benchmark scores are reported only for the main ExoMind-35B-A3B system — which posts leading results among frontier models on tasks like FrontierScience-Research (70.0), CMT-Benchmark (84.0), AMO-Bench (78.0), and an eight-benchmark scientific reasoning average of 68.3, outperforming Claude-Opus-4.8 Thinking, GPT-5.5, and Gemini-3.1-Pro Preview — while ExoMind-9B itself has not been separately scored and is instead positioned as a resource-conscious checkpoint for scientific question answering, mathematical/computational reasoning, tool-use experiments, and agentic prototyping. It's servable via vLLM or SGLang with Qwen3-style reasoning and tool-call parsers, available in official and community GGUF quantizations, and released under the Apache License 2.0 (with the accompanying preprint, figures, and ExoMind branding separately governed by dedicated content and brand terms).
Model Files
| File Name | Quant Type | File Size | File Link |
|---|---|---|---|
| ExoMind-9B.BF16.gguf | BF16 | 17.9 GB | Download |
| ExoMind-9B.Q3_K_L.gguf | Q3_K_L | 4.93 GB | Download |
| ExoMind-9B.Q3_K_M.gguf | Q3_K_M | 4.62 GB | Download |
| ExoMind-9B.Q3_K_S.gguf | Q3_K_S | 4.26 GB | Download |
| ExoMind-9B.Q4_0.gguf | Q4_0 | 5.31 GB | Download |
| ExoMind-9B.Q4_K_M.gguf | Q4_K_M | 5.63 GB | Download |
| ExoMind-9B.Q4_K_S.gguf | Q4_K_S | 5.35 GB | Download |
| ExoMind-9B.Q5_0.gguf | Q5_0 | 6.31 GB | Download |
| ExoMind-9B.Q5_K_M.gguf | Q5_K_M | 6.47 GB | Download |
| ExoMind-9B.Q5_K_S.gguf | Q5_K_S | 6.31 GB | Download |
llama.cpp
LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp
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