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
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Download tools/export-lora/README.md from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.03 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/export-lora/README.md
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/export-lora/README.md
-
curl -L -o README.md https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/export-lora/README.md
1.03 kB
| # export-lora | |
| Apply LORA adapters to base model and export the resulting model. | |
| ``` | |
| usage: llama-export-lora [options] | |
| options: | |
| -m, --model FNAME model path from which to load base model | |
| --lora FNAME path to LoRA adapter (use comma-separated values to load multiple adapters) | |
| --lora-scaled FNAME:SCALE,... path to LoRA adapter with user defined scaling (format: FNAME:SCALE,...) | |
| -o, --output, --output-file FNAME output file (default: 'ggml-lora-merged-f16.gguf') | |
| ``` | |
| For example: | |
| ```bash | |
| ./bin/llama-export-lora \ | |
| -m open-llama-3b-v2.gguf \ | |
| -o open-llama-3b-v2-english2tokipona-chat.gguf \ | |
| --lora lora-open-llama-3b-v2-english2tokipona-chat-LATEST.gguf | |
| ``` | |
| Multiple LORA adapters can be applied by passing comma-separated values to `--lora FNAME` or `--lora-scaled FNAME:SCALE,...`: | |
| ```bash | |
| ./bin/llama-export-lora \ | |
| -m your_base_model.gguf \ | |
| -o your_merged_model.gguf \ | |
| --lora-scaled lora_task_A.gguf:0.5,lora_task_B.gguf:0.5 | |
| ``` | |