How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Subject-Emu-5259/NeuralAI-Mamba-K2:Q4_K_M# Run inference directly in the terminal:
llama cli -hf Subject-Emu-5259/NeuralAI-Mamba-K2:Q4_K_MUse 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 Subject-Emu-5259/NeuralAI-Mamba-K2:Q4_K_M# Run inference directly in the terminal:
./llama-cli -hf Subject-Emu-5259/NeuralAI-Mamba-K2:Q4_K_MBuild 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 Subject-Emu-5259/NeuralAI-Mamba-K2:Q4_K_M# Run inference directly in the terminal:
./build/bin/llama-cli -hf Subject-Emu-5259/NeuralAI-Mamba-K2:Q4_K_MUse Docker
docker model run hf.co/Subject-Emu-5259/NeuralAI-Mamba-K2:Q4_K_MQuick Links
🧠 NeuralAI · Mamba K2
NeuralAI's scaled Mamba SSM base, quantized to Q4_K_M GGUF (460MB) for fast local inference.
Model Details
- Architecture: Mamba SSM (
state-spaces/mamba-790m-hf) - Parameters: 793M
- Quantization: Q4_K_M via llama.cpp
- Status: Base pretrained weights — SFT queued to convert to instruction-tuned chat
- Creator: De'Andrew Preston Harris, NeuralAI
Status
- Mamba K2 is base-model only until SFT training is complete.
- Output will be base-model continuations, not aligned chat, until the SFT checkpoint is merged.
- Follow github.com/Subject-Emu-5259/NeuralAI for SFT releases.
Prompt Format (planned: neuralai-intel)
After SFT, K2 will use the same vocabulary-friendly format as K1:
### System:
You are NeuralAI, a helpful assistant.
### User:
{your question}
### Assistant:
Use with LM Studio / llama.cpp
python3 -m llama_cpp.server --model mamba-790m-hf.Q4_K_M.gguf --chat_format neuralai-intel
License
Apache 2.0
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Hardware compatibility
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4-bit
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Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf Subject-Emu-5259/NeuralAI-Mamba-K2:Q4_K_M# Run inference directly in the terminal: llama cli -hf Subject-Emu-5259/NeuralAI-Mamba-K2:Q4_K_M