Instructions to use VladHong/llama-cpp-K2-FAST2 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 VladHong/llama-cpp-K2-FAST2 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 VladHong/llama-cpp-K2-FAST2 # Run inference directly in the terminal: llama cli -hf VladHong/llama-cpp-K2-FAST2
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf VladHong/llama-cpp-K2-FAST2 # Run inference directly in the terminal: llama cli -hf VladHong/llama-cpp-K2-FAST2
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf VladHong/llama-cpp-K2-FAST2 # Run inference directly in the terminal: ./build/bin/llama cli -hf VladHong/llama-cpp-K2-FAST2
Use Docker
docker model run hf.co/VladHong/llama-cpp-K2-FAST2
- Llama new
- LM Studio
- Jan
- Ollama
How to use VladHong/llama-cpp-K2-FAST2 with Ollama:
ollama run hf.co/VladHong/llama-cpp-K2-FAST2
- Unsloth Desktop
- Docker Model Runner
How to use VladHong/llama-cpp-K2-FAST2 with Docker Model Runner:
docker model run hf.co/VladHong/llama-cpp-K2-FAST2
- Lemonade
How to use VladHong/llama-cpp-K2-FAST2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull VladHong/llama-cpp-K2-FAST2
Run and chat with the model
lemonade run user.llama-cpp-K2-FAST2-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
File size: 1,396 Bytes
c59fd93 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 | #!/usr/bin/env bash
function usage {
echo "usage: <n>$0"
echo "note: n is the number of essays to download"
echo "for specific n, the resulting pg.txt file will have the following number of tokens:"
echo "n | tokens"
echo "--- | ---"
echo "1 | 6230"
echo "2 | 23619"
echo "5 | 25859"
echo "10 | 36888"
echo "15 | 50188"
echo "20 | 59094"
echo "25 | 88764"
echo "30 | 103121"
echo "32 | 108338"
echo "35 | 113403"
echo "40 | 127699"
echo "45 | 135896"
exit 1
}
function has_cmd {
if ! [ -x "$(command -v $1)" ]; then
echo "error: $1 is not available" >&2
exit 1
fi
}
# check for: curl, html2text, tail, sed, fmt
has_cmd curl
has_cmd html2text
has_cmd tail
has_cmd sed
if [ $# -ne 1 ]; then
usage
fi
n=$1
# get urls
urls="$(curl http://www.aaronsw.com/2002/feeds/pgessays.rss | grep html | sed -e "s/.*http/http/" | sed -e "s/html.*/html/" | head -n $n)"
printf "urls:\n%s\n" "$urls"
if [ -f pg.txt ]; then
rm pg.txt
fi
c=1
for url in $urls; do
echo "processing $url"
cc=$(printf "%03d" $c)
curl -L $url | html2text | tail -n +4 | sed -E "s/^[[:space:]]+//g" | fmt -w 80 >> pg-$cc-one.txt
cat pg-$cc-one.txt >> pg.txt
cp -v pg.txt pg-$cc-all.txt
c=$((c+1))
# don't flood the server
sleep 1
done
echo "done. data in pg.txt"
exit 0
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