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 examples/embedding/README.md from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 2.31 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/examples/embedding/README.md
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/examples/embedding/README.md
-
curl -L -o README.md https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/examples/embedding/README.md
2.31 kB
| # llama.cpp/example/embedding | |
| This example demonstrates generate high-dimensional embedding vector of a given text with llama.cpp. | |
| ## Quick Start | |
| To get started right away, run the following command, making sure to use the correct path for the model you have: | |
| ### Unix-based systems (Linux, macOS, etc.): | |
| ```bash | |
| ./llama-embedding -m ./path/to/model --pooling mean --log-disable -p "Hello World!" 2>/dev/null | |
| ``` | |
| ### Windows: | |
| ```powershell | |
| llama-embedding.exe -m ./path/to/model --pooling mean --log-disable -p "Hello World!" 2>$null | |
| ``` | |
| The above command will output space-separated float values. | |
| ## extra parameters | |
| ### --embd-normalize $integer$ | |
| | $integer$ | description | formula | | |
| |-----------|---------------------|---------| | |
| | $-1$ | none | | |
| | $0$ | max absolute int16 | $\Large{{32760 * x_i} \over\max \lvert x_i\rvert}$ | |
| | $1$ | taxicab | $\Large{x_i \over\sum \lvert x_i\rvert}$ | |
| | $2$ | euclidean (default) | $\Large{x_i \over\sqrt{\sum x_i^2}}$ | |
| | $>2$ | p-norm | $\Large{x_i \over\sqrt[p]{\sum \lvert x_i\rvert^p}}$ | |
| ### --embd-output-format $'string'$ | |
| | $'string'$ | description | | | |
| |------------|------------------------------|--| | |
| | '' | same as before | (default) | |
| | 'array' | single embeddings | $[[x_1,...,x_n]]$ | |
| | | multiple embeddings | $[[x_1,...,x_n],[x_1,...,x_n],...,[x_1,...,x_n]]$ | |
| | 'json' | openai style | | |
| | 'json+' | add cosine similarity matrix | | |
| | 'raw' | plain text output | | |
| ### --embd-separator $"string"$ | |
| | $"string"$ | | | |
| |--------------|-| | |
| | "\n" | (default) | |
| | "<#embSep#>" | for example | |
| | "<#sep#>" | other example | |
| ## examples | |
| ### Unix-based systems (Linux, macOS, etc.): | |
| ```bash | |
| ./llama-embedding -p 'Castle<#sep#>Stronghold<#sep#>Dog<#sep#>Cat' --pooling mean --embd-separator '<#sep#>' --embd-normalize 2 --embd-output-format '' -m './path/to/model.gguf' --n-gpu-layers 99 --log-disable 2>/dev/null | |
| ``` | |
| ### Windows: | |
| ```powershell | |
| llama-embedding.exe -p 'Castle<#sep#>Stronghold<#sep#>Dog<#sep#>Cat' --pooling mean --embd-separator '<#sep#>' --embd-normalize 2 --embd-output-format '' -m './path/to/model.gguf' --n-gpu-layers 99 --log-disable 2>/dev/null | |
| ``` | |