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 tools/ui/src/lib/constants/model-id.constants.ts from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.6 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/constants/model-id.constants.ts
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/src/lib/constants/model-id.constants.ts
-
curl -L -o model-id.constants.ts https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/constants/model-id.constants.ts
1.6 kB
| /** | |
| * Parsing of `org/ModelName[-tag][:quant]` style model IDs. | |
| */ | |
| export const MODEL_ID = { | |
| /** | |
| * Matches an activated-parameter-count segment, e.g. `A10B`, `a2.4b`. | |
| * The leading `A`/`a` distinguishes it from a regular params segment. | |
| */ | |
| ACTIVATED_PARAMS_RE: /^[Aa]\d+(\.\d+)?[BbMmKkTt]$/, | |
| /** Matches prefix for custom quantization types, e.g. `UD-Q8_K_XL`. */ | |
| CUSTOM_QUANTIZATION_PREFIX_RE: /^UD$/i, | |
| /** Container format segments to exclude from tags (every model uses these). */ | |
| IGNORED_SEGMENTS: new Set(['GGUF', 'GGML']), | |
| /** Sentinel value returned by `indexOf` when a substring is not found. */ | |
| NOT_FOUND: -1, | |
| /** Separates `<org>` from `<model>` in a model ID, e.g. `org/ModelName`. */ | |
| ORG_SEPARATOR: '/', | |
| /** | |
| * Matches a parameter-count segment, e.g. `7B`, `1.5b`, `120M`. | |
| * The optional leading `E` covers effective-parameter sizes, e.g. Gemma's | |
| * `E2B`/`E4B` (MatFormer models sized by resident params). | |
| */ | |
| PARAMS_RE: /^[Ee]?\d+(\.\d+)?[BbMmKkTt]$/, | |
| /** | |
| * Matches a quantization/precision segment, e.g. `Q4_K_M`, `IQ4_XS`, `F16`, `BF16`, `MXFP4`. | |
| * Case-insensitive to handle both uppercase and lowercase inputs. | |
| */ | |
| QUANTIZATION_SEGMENT_RE: /^(I?Q\d+(_[A-Z0-9]+)*|F\d+|BF\d+|MXFP\d+(_[A-Z0-9]+)*)$/i, | |
| /** Separates the model path from the quantization tag, e.g. `model:Q4_K_M`. */ | |
| QUANTIZATION_SEPARATOR: ':', | |
| /** Separates named segments within the model path, e.g. `ModelName-7B-GGUF`. */ | |
| SEGMENT_SEPARATOR: '-', | |
| /** Matches a trailing weight file extension, e.g. `model.gguf` -> `model`. */ | |
| WEIGHT_EXTENSION_RE: /\.(gguf|ggml)$/i | |
| }; | |