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: 1,028 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 | # 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
```
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