Instructions to use prithivMLmods/BAAI-AREX-Turbo-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/BAAI-AREX-Turbo-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="prithivMLmods/BAAI-AREX-Turbo-GGUF")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("prithivMLmods/BAAI-AREX-Turbo-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use prithivMLmods/BAAI-AREX-Turbo-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/BAAI-AREX-Turbo-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/BAAI-AREX-Turbo-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/BAAI-AREX-Turbo-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/BAAI-AREX-Turbo-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf prithivMLmods/BAAI-AREX-Turbo-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf prithivMLmods/BAAI-AREX-Turbo-GGUF:Q4_K_M
Use Docker
docker model run hf.co/prithivMLmods/BAAI-AREX-Turbo-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use prithivMLmods/BAAI-AREX-Turbo-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prithivMLmods/BAAI-AREX-Turbo-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/BAAI-AREX-Turbo-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/prithivMLmods/BAAI-AREX-Turbo-GGUF:Q4_K_M
- SGLang
How to use prithivMLmods/BAAI-AREX-Turbo-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/BAAI-AREX-Turbo-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/BAAI-AREX-Turbo-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 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/BAAI-AREX-Turbo-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/BAAI-AREX-Turbo-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" } } ] } ] }' - Ollama
How to use prithivMLmods/BAAI-AREX-Turbo-GGUF with Ollama:
ollama run hf.co/prithivMLmods/BAAI-AREX-Turbo-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use prithivMLmods/BAAI-AREX-Turbo-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/BAAI-AREX-Turbo-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/BAAI-AREX-Turbo-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use prithivMLmods/BAAI-AREX-Turbo-GGUF with Docker Model Runner:
docker model run hf.co/prithivMLmods/BAAI-AREX-Turbo-GGUF:Q4_K_M
- Lemonade
How to use prithivMLmods/BAAI-AREX-Turbo-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prithivMLmods/BAAI-AREX-Turbo-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.BAAI-AREX-Turbo-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use prithivMLmods/BAAI-AREX-Turbo-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/BAAI-AREX-Turbo-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/BAAI-AREX-Turbo-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use prithivMLmods/BAAI-AREX-Turbo-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/BAAI-AREX-Turbo-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/BAAI-AREX-Turbo-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"
BAAI-AREX-Turbo-GGUF
AREX-Turbo (
BAAI/AREX-Turbo) is an Apache-2.0 open-weight 4B dense deep-research agent from BAAI, built onQwen/Qwen3.5-4Bas a compact alternative to the 122B AREX-Base, with a 256K context window. Its recursive self-improvement framework combines iterative research, evidence verification, targeted follow-up searches, and autonomous context updates to improve long-horizon information seeking. Across research benchmarks, it scores 70.7 on BrowseComp, 81.6 on GAIA, 57.0 on xbench-2510, 78.5 on DeepSearchQA, 68.5 on WideSearch-en, and 40.6 on text-only HLE with tools. Despite its smaller size, it outperforms several 30Bβ235B models, matches Qwen3.5-122B on GAIA, surpasses it on BrowseComp (70.7 vs. 63.8) and WideSearch-en (68.5 vs. 60.5), and exceeds Gemini-3.1-Pro on GAIA, xbench-2510, and WideSearch-en. It usessearch,visit,update_context, andfinishtools for evidence-driven research.
MTP β Speculative Decoding: No
Model Files
| File Name | Quant Type | File Size | File Link | Description |
|---|---|---|---|---|
| AREX-Turbo.BF16.gguf | BF16 | 8.42 GB | Link | Full BF16 weights. Highest quality, largest file size. |
| AREX-Turbo.Q3_K_L.gguf | Q3_K_L | 2.42 GB | Link | Lower quality but usable, good for low RAM availability. |
| AREX-Turbo.Q3_K_M.gguf | Q3_K_M | 2.26 GB | Link | Low quality. |
| AREX-Turbo.Q4_K_M.gguf | Q4_K_M | 2.71 GB | Link | Good quality, default size for most use cases, recommended. |
| AREX-Turbo.Q4_K_S.gguf | Q4_K_S | 2.56 GB | Link | Slightly lower quality with more space savings, recommended. |
| AREX-Turbo.Q5_K_M.gguf | Q5_K_M | 3.07 GB | Link | High quality, recommended. |
| AREX-Turbo.Q5_K_S.gguf | Q5_K_S | 2.99 GB | Link | High quality, recommended. |
| AREX-Turbo.Q6_K.gguf | Q6_K | 3.46 GB | Link | Very high quality, near perfect, recommended. |
| AREX-Turbo.Q8_0.gguf | Q8_0 | 4.48 GB | Link | Extremely high quality, generally unneeded but max available quant. |
| AREX-Turbo.mmproj-bf16.gguf | mmproj-bf16 | 676 MB | Link | Multimodal projection file in BF16 format. Used for vision/language models. |
llama.cpp
LLM inference in C/C++ β https://github.com/ggml-org/llama.cpp
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