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
Download examples/server-llama2-13B.sh from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 798 Bytes
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/examples/server-llama2-13B.sh
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/examples/server-llama2-13B.sh
-
curl -L -o server-llama2-13B.sh https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/examples/server-llama2-13B.sh
798 Bytes
| set -e | |
| cd "$(dirname "$0")/.." || exit | |
| # Specify the model you want to use here: | |
| MODEL="${MODEL:-./models/llama-2-13b-chat.ggmlv3.q5_K_M.bin}" | |
| PROMPT_TEMPLATE=${PROMPT_TEMPLATE:-./prompts/chat-system.txt} | |
| # Adjust to the number of CPU cores you want to use. | |
| N_THREAD="${N_THREAD:-12}" | |
| # Note: you can also override the generation options by specifying them on the command line: | |
| GEN_OPTIONS="${GEN_OPTIONS:---ctx_size 4096 --batch-size 1024}" | |
| # shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS | |
| ./llama-server $GEN_OPTIONS \ | |
| --model "$MODEL" \ | |
| --threads "$N_THREAD" \ | |
| --rope-freq-scale 1.0 \ | |
| "$@" | |
| # I used this to test the model with mps, but omitted it from the general purpose. If you want to use it, just specify it on the command line. | |
| # -ngl 1 \ | |