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/model-conversion/scripts/embedding/modelcard.template from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.33 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/examples/model-conversion/scripts/embedding/modelcard.template
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/examples/model-conversion/scripts/embedding/modelcard.template
-
curl -L -o modelcard.template https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/examples/model-conversion/scripts/embedding/modelcard.template
1.33 kB
| --- | |
| base_model: | |
| - {base_model} | |
| --- | |
| # {model_name} GGUF | |
| Recommended way to run this model: | |
| ```sh | |
| llama-server -hf {namespace}/{model_name}-GGUF --embeddings | |
| ``` | |
| Then the endpoint can be accessed at http://localhost:8080/embedding, for | |
| example using `curl`: | |
| ```console | |
| curl --request POST \ | |
| --url http://localhost:8080/embedding \ | |
| --header "Content-Type: application/json" \ | |
| --data '{{"input": "Hello embeddings"}}' \ | |
| --silent | |
| ``` | |
| Alternatively, the `llama-embedding` command line tool can be used: | |
| ```sh | |
| llama-embedding -hf {namespace}/{model_name}-GGUF --verbose-prompt -p "Hello embeddings" | |
| ``` | |
| #### embd_normalize | |
| When a model uses pooling, or the pooling method is specified using `--pooling`, | |
| the normalization can be controlled by the `embd_normalize` parameter. | |
| The default value is `2` which means that the embeddings are normalized using | |
| the Euclidean norm (L2). Other options are: | |
| * -1 No normalization | |
| * 0 Max absolute | |
| * 1 Taxicab | |
| * 2 Euclidean/L2 | |
| * \>2 P-Norm | |
| This can be passed in the request body to `llama-server`, for example: | |
| ```sh | |
| --data '{{"input": "Hello embeddings", "embd_normalize": -1}}' \ | |
| ``` | |
| And for `llama-embedding`, by passing `--embd-normalize <value>`, for example: | |
| ```sh | |
| llama-embedding -hf {namespace}/{model_name}-GGUF --embd-normalize -1 -p "Hello embeddings" | |
| ``` | |