How to use from
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 keyvan-ai/Mankei-1B-Chat-GGUF:
# Run inference directly in the terminal:
llama cli -hf keyvan-ai/Mankei-1B-Chat-GGUF:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf keyvan-ai/Mankei-1B-Chat-GGUF:
# Run inference directly in the terminal:
llama cli -hf keyvan-ai/Mankei-1B-Chat-GGUF:
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 keyvan-ai/Mankei-1B-Chat-GGUF:
# Run inference directly in the terminal:
./llama-cli -hf keyvan-ai/Mankei-1B-Chat-GGUF:
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 keyvan-ai/Mankei-1B-Chat-GGUF:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf keyvan-ai/Mankei-1B-Chat-GGUF:
Use Docker
docker model run hf.co/keyvan-ai/Mankei-1B-Chat-GGUF:
Quick Links

Mankei

Mankei-1B-Chat — GGUF

GGUF-Quantisierungen von keyvan-ai/Mankei-1B-Chat für llama.cpp und Ollama — läuft auf der CPU.

Quantisierung Größe Download
f16 1,9 GB Mankei-1B-Chat-f16.gguf
Q8_0 1,0 GB Mankei-1B-Chat-q8_0.gguf
Q6_K 854 MB Mankei-1B-Chat-q6_k.gguf
Q5_K_M 727 MB Mankei-1B-Chat-q5_k_m.gguf
Q4_K_M 637 MB Mankei-1B-Chat-q4_k_m.gguf
Q3_K_M 512 MB Mankei-1B-Chat-q3_k_m.gguf
# Ollama
ollama run hf.co/keyvan-ai/Mankei-1B-Chat-GGUF:Q4_K_M

# llama.cpp
llama-cli -hf keyvan-ai/Mankei-1B-Chat-GGUF:Q4_K_M

Empfohlenes Sampling: temperature 0.7 · top_p 0.9 · repetition_penalty 1.15.

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Model size
1B params
Architecture
llama
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