Instructions to use 1bit-MONSTER/Zamba2-1.2B-instruct-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/Zamba2-1.2B-instruct-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/Zamba2-1.2B-instruct-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf 1bit-MONSTER/Zamba2-1.2B-instruct-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf 1bit-MONSTER/Zamba2-1.2B-instruct-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf 1bit-MONSTER/Zamba2-1.2B-instruct-GGUF:Q8_0
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/Zamba2-1.2B-instruct-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf 1bit-MONSTER/Zamba2-1.2B-instruct-GGUF:Q8_0
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/Zamba2-1.2B-instruct-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf 1bit-MONSTER/Zamba2-1.2B-instruct-GGUF:Q8_0
Use Docker
docker model run hf.co/1bit-MONSTER/Zamba2-1.2B-instruct-GGUF:Q8_0
- LM Studio
- Jan
- Ollama
How to use 1bit-MONSTER/Zamba2-1.2B-instruct-GGUF with Ollama:
ollama run hf.co/1bit-MONSTER/Zamba2-1.2B-instruct-GGUF:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use 1bit-MONSTER/Zamba2-1.2B-instruct-GGUF with Docker Model Runner:
docker model run hf.co/1bit-MONSTER/Zamba2-1.2B-instruct-GGUF:Q8_0
- Lemonade
How to use 1bit-MONSTER/Zamba2-1.2B-instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull 1bit-MONSTER/Zamba2-1.2B-instruct-GGUF:Q8_0
Run and chat with the model
lemonade run user.Zamba2-1.2B-instruct-GGUF-Q8_0
List all available models
lemonade list
- Atomic Chat
Zamba2-1.2B-instruct โ GGUF
GGUF conversion of Zyphra's Zamba2-1.2B-instruct
(a Mamba2/attention hybrid), served through the 1bit engine.
Zamba2 architecture support was carried from upstream llama.cpp (ggml-org#21412); we
added the Vulkan-side fix that makes it actually fast on this hardware.
Contents
Zamba2-1.2B-instruct-Q8_0.gguf
The Vulkan fix
Zamba2's Vulkan SSM_SCAN kernel didn't handle d_state = 64 correctly, which capped
decode speed badly across the whole model family โ we measured 4.3 tok/s at 2.7B and
2.1 tok/s at 7B before fixing it. After the fix, those became 45 and 17 tok/s
respectively (same hardware). This 1.2B checkpoint benefits from the same fix.
Measured performance (Strix Halo, Vulkan, Q8_0, this checkpoint)
pp512: 3302 tok/s ยท tg128: 83.4 tok/s
Running it
1bit serve -m Zamba2-1.2B-instruct-Q8_0.gguf --device vulkan
Attribution
- Base model: Zyphra/Zamba2-1.2B-instruct, Apache 2.0.
- Zamba2 llama.cpp support: carried from upstream
ggml-org#21412. - Vulkan
SSM_SCANd_state=64 fix: this project's engine team. - License: Apache 2.0, inherited from the base model.
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