Instructions to use prithivMLmods/drex-v1.5-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/drex-v1.5-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="prithivMLmods/drex-v1.5-GGUF")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("prithivMLmods/drex-v1.5-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use prithivMLmods/drex-v1.5-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/drex-v1.5-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/drex-v1.5-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/drex-v1.5-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/drex-v1.5-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/drex-v1.5-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf prithivMLmods/drex-v1.5-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/drex-v1.5-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf prithivMLmods/drex-v1.5-GGUF:Q4_K_M
Use Docker
docker model run hf.co/prithivMLmods/drex-v1.5-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use prithivMLmods/drex-v1.5-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prithivMLmods/drex-v1.5-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/drex-v1.5-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/prithivMLmods/drex-v1.5-GGUF:Q4_K_M
- SGLang
How to use prithivMLmods/drex-v1.5-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/drex-v1.5-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/drex-v1.5-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/drex-v1.5-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/drex-v1.5-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use prithivMLmods/drex-v1.5-GGUF with Ollama:
ollama run hf.co/prithivMLmods/drex-v1.5-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use prithivMLmods/drex-v1.5-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/drex-v1.5-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/drex-v1.5-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use prithivMLmods/drex-v1.5-GGUF with Docker Model Runner:
docker model run hf.co/prithivMLmods/drex-v1.5-GGUF:Q4_K_M
- Lemonade
How to use prithivMLmods/drex-v1.5-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prithivMLmods/drex-v1.5-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.drex-v1.5-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use prithivMLmods/drex-v1.5-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/drex-v1.5-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/drex-v1.5-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use prithivMLmods/drex-v1.5-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/drex-v1.5-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/drex-v1.5-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"
drex-v1.5-GGUF
drex v1.5 is a ~9B-parameter decision model from Nace.AI, built on a
Qwen3_5ForCausalLMhybrid-attention backbone (32 layers, with a 3:1 ratio of linear-attention to full-attention layers) distilled from XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B, paired with a dedicated pointer head that scores options directly from hidden states rather than generating text. Instead of producing free-form output, it takes a state (text or JSON) plus typed questions—choice,noul(yes/no), or ordinalscore—and returns calibrated probabilities over the possible answers in a single forward pass per question, supporting context lengths from 16,384 up to 131,072 tokens. It communicates via the TypeSafe-compatible System One API (POST /v1/systemone), making it a drop-in replacement for Kev- or Jev-format decision servers, and can be run locally through the bundled Python inference/serving scripts, llama.cpp, or Ollama (with bf16 or Q8_0 GGUF weights), or accessed via Nace's hosted Drex API. Despite its relatively small size, it ranks as the top model under 10B parameters on the public Decision Index 0.3.1 (58.08), performs competitively with the larger/undisclosed Jev 1.13.0 on JevBench accuracy (86.2% vs 87.0%) and game-arena win rate (56.8%), and maintains strong accuracy (89–93%) even on long-context documents up to 128k tokens.
Model Files
| File Name | Quant Type | File Size | File Link | Description |
|---|---|---|---|---|
| drex-v1.5.BF16.gguf | BF16 | 17.9 GB | Link | Full BF16 weights. Highest quality, largest file size. |
| drex-v1.5.Q3_K_L.gguf | Q3_K_L | 4.93 GB | Link | Lower quality but usable, good for low RAM availability. |
| drex-v1.5.Q3_K_M.gguf | Q3_K_M | 4.62 GB | Link | Low quality. |
| drex-v1.5.Q4_K_M.gguf | Q4_K_M | 5.63 GB | Link | Good quality, default size for most use cases, recommended. |
| drex-v1.5.Q4_K_S.gguf | Q4_K_S | 5.35 GB | Link | Slightly lower quality with more space savings, recommended. |
| drex-v1.5.Q5_K_M.gguf | Q5_K_M | 6.47 GB | Link | High quality, recommended. |
| drex-v1.5.Q5_K_S.gguf | Q5_K_S | 6.31 GB | Link | High quality, recommended. |
| drex-v1.5.Q6_K.gguf | Q6_K | 7.36 GB | Link | Very high quality, near perfect, recommended. |
| drex-v1.5.Q8_0.gguf | Q8_0 | 9.53 GB | Link | Extremely high quality, generally unneeded but max available quant. |
llama.cpp
LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp
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Model tree for prithivMLmods/drex-v1.5-GGUF
Base model
Qwen/Qwen3.5-9B-Base