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/utils/model-names.ts from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 2.17 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/utils/model-names.ts
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/src/lib/utils/model-names.ts
-
curl -L -o model-names.ts https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/utils/model-names.ts
2.17 kB
| import { FILE_PATH_SEPARATOR_REGEX } from '$lib/constants'; | |
| /** | |
| * Normalizes a model name by extracting the filename from a path, but preserves Hugging Face repository format. | |
| * | |
| * Handles both forward slashes (/) and backslashes (\) as path separators. | |
| * - If the model name has exactly one slash (org/model format), preserves the full "org/model" name | |
| * - If the model name has no slash or multiple slashes, extracts just the filename | |
| * - If the model name is just a filename (no path), returns it as-is. | |
| * | |
| * @param modelName - The model name or path to normalize | |
| * @returns The normalized model name | |
| * | |
| * @example | |
| * normalizeModelName('models/llama-3.1-8b') // Returns: 'llama-3.1-8b' (multiple slashes -> filename) | |
| * normalizeModelName('C:\\Models\\gpt-4') // Returns: 'gpt-4' (multiple slashes -> filename) | |
| * normalizeModelName('meta-llama/Llama-3.1-8B') // Returns: 'meta-llama/Llama-3.1-8B' (Hugging Face format) | |
| * normalizeModelName('simple-model') // Returns: 'simple-model' (no slash) | |
| * normalizeModelName(' spaced ') // Returns: 'spaced' | |
| * normalizeModelName('') // Returns: '' | |
| */ | |
| export function normalizeModelName(modelName: string): string { | |
| const trimmed = modelName.trim(); | |
| if (!trimmed) { | |
| return ''; | |
| } | |
| const segments = trimmed.split(FILE_PATH_SEPARATOR_REGEX); | |
| // If we have exactly 2 segments (one slash), treat it as Hugging Face repo format | |
| // and preserve the full "org/model" format | |
| if (segments.length === 2) { | |
| const [org, model] = segments; | |
| const trimmedOrg = org?.trim(); | |
| const trimmedModel = model?.trim(); | |
| if (trimmedOrg && trimmedModel) { | |
| return `${trimmedOrg}/${trimmedModel}`; | |
| } | |
| } | |
| // For other cases (no slash, or multiple slashes), extract just the filename | |
| const candidate = segments.pop(); | |
| const normalized = candidate?.trim(); | |
| return normalized && normalized.length > 0 ? normalized : trimmed; | |
| } | |
| /** | |
| * Validates if a model name is valid (non-empty after normalization). | |
| * | |
| * @param modelName - The model name to validate | |
| * @returns true if valid, false otherwise | |
| */ | |
| export function isValidModelName(modelName: string): boolean { | |
| return normalizeModelName(modelName).length > 0; | |
| } | |