Instructions to use 1bit-MONSTER/ZAYA1-reasoning-base-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use 1bit-MONSTER/ZAYA1-reasoning-base-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 1bit-MONSTER/ZAYA1-reasoning-base-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf 1bit-MONSTER/ZAYA1-reasoning-base-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 1bit-MONSTER/ZAYA1-reasoning-base-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf 1bit-MONSTER/ZAYA1-reasoning-base-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 1bit-MONSTER/ZAYA1-reasoning-base-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf 1bit-MONSTER/ZAYA1-reasoning-base-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 1bit-MONSTER/ZAYA1-reasoning-base-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf 1bit-MONSTER/ZAYA1-reasoning-base-GGUF:Q4_K_M
Use Docker
docker model run hf.co/1bit-MONSTER/ZAYA1-reasoning-base-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use 1bit-MONSTER/ZAYA1-reasoning-base-GGUF with Ollama:
ollama run hf.co/1bit-MONSTER/ZAYA1-reasoning-base-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use 1bit-MONSTER/ZAYA1-reasoning-base-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf 1bit-MONSTER/ZAYA1-reasoning-base-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": "1bit-MONSTER/ZAYA1-reasoning-base-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use 1bit-MONSTER/ZAYA1-reasoning-base-GGUF with Docker Model Runner:
docker model run hf.co/1bit-MONSTER/ZAYA1-reasoning-base-GGUF:Q4_K_M
- Lemonade
How to use 1bit-MONSTER/ZAYA1-reasoning-base-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull 1bit-MONSTER/ZAYA1-reasoning-base-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.ZAYA1-reasoning-base-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use 1bit-MONSTER/ZAYA1-reasoning-base-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 1bit-MONSTER/ZAYA1-reasoning-base-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 1bit-MONSTER/ZAYA1-reasoning-base-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use 1bit-MONSTER/ZAYA1-reasoning-base-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf 1bit-MONSTER/ZAYA1-reasoning-base-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 "1bit-MONSTER/ZAYA1-reasoning-base-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"
ZAYA1-reasoning-base β GGUF
Our own GGUF conversion of Zyphra's ZAYA1-reasoning-base. It keeps Zyphra's original Megatron-style checkpoint layout, which transformers cannot load; our converter maps it to ZAYA's standard layout (llama.cpp #14). Converting Zyphra/ZAYA1-8B-legacy this way gives tensors byte-identical to the ones from Zyphra/ZAYA1-8B.
Contents
ZAYA1-reasoning-base-Q4_K_M.gguf(8B shape: 40 layers, 16 experts; rope theta 1e6; 32,768-token context).
Validation
- Wikitext-2 test perplexity, 60 chunks of 512 tokens, Q4_K_M on Vulkan (Radeon 8060S): 10.15 Β± 0.22. Decode 89-91 tok/s (chat, Vulkan). It answers chat prompts with its own template, thinking first.
- Raw-text perplexity favours base models; the post-trained ZAYA1-8B scores 32.13 on the same test.
Running it
With the 1bit engine:
1bit serve -m ZAYA1-reasoning-base-Q4_K_M.gguf --device vulkan
ZAYA runs from our llama.cpp fork (branch 1bit/hrx-vulkan-patched); upstream llama.cpp has no ZAYA
model. The GGUFs must come from our converter: it writes the grouped convolution's weights
tap-major, which the graph expects.
Attribution
- Base model: Zyphra/ZAYA1-reasoning-base, Apache 2.0.
- GGUF conversion and validation: the 1bit engine project, with the converter and model code in our llama.cpp fork.
- License: Apache 2.0, inherited from the base model.
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