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/mention-token.ts from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.65 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/utils/mention-token.ts
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/src/lib/utils/mention-token.ts
-
curl -L -o mention-token.ts https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/utils/mention-token.ts
1.65 kB
| // An `@` starts a mention only when preceded by start-of-string or one of | |
| // these; identifier chars are not delimiters, so a mid-word `@` does not. | |
| const TOKEN_BOUNDARY_CHARS = new Set([ | |
| ' ', | |
| '\t', | |
| '\n', | |
| '\r', | |
| '(', | |
| ')', | |
| '[', | |
| ']', | |
| ',', | |
| ';', | |
| ':', | |
| '"', | |
| "'" | |
| ]); | |
| /** | |
| * Find the most-recent `@`-mention token whose extent includes `cursor`; | |
| * the query covers the whole `@...` token regardless of caret position. | |
| */ | |
| export function findMentionToken( | |
| value: string, | |
| cursor: number | |
| ): { start: number; end: number; query: string } | null { | |
| if (cursor <= 0 || cursor > value.length) return null; | |
| let atIndex = -1; | |
| for (let i = cursor - 1; i >= 0; i--) { | |
| const ch = value[i]; | |
| if (ch === '@') { | |
| const prev = i > 0 ? value[i - 1] : ''; | |
| if (i === 0 || TOKEN_BOUNDARY_CHARS.has(prev)) { | |
| atIndex = i; | |
| } | |
| break; | |
| } | |
| if (TOKEN_BOUNDARY_CHARS.has(ch)) break; | |
| } | |
| if (atIndex === -1) return null; | |
| let end = atIndex + 1; | |
| while (end < value.length && !TOKEN_BOUNDARY_CHARS.has(value[end])) { | |
| end++; | |
| } | |
| return { | |
| end, | |
| query: value.slice(atIndex + 1, end), | |
| start: atIndex | |
| }; | |
| } | |
| /** | |
| * Stable signature of a mention token for use as a "dismissed" marker: | |
| * while the picker is closed and this exact token is still intact, the | |
| * picker does not silently re-open on in-token edits. | |
| */ | |
| export interface MentionDismissSnapshot { | |
| start: number; | |
| query: string; | |
| } | |
| export function takeMentionDismissSnapshot( | |
| value: string, | |
| cursor: number | |
| ): MentionDismissSnapshot | null { | |
| const token = findMentionToken(value, cursor); | |
| if (!token) return null; | |
| return { query: token.query, start: token.start }; | |
| } | |