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
File size: 2,727 Bytes
c59fd93 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 | import { ModelModality } from '$lib/enums';
import type { ModelOption } from '$lib/types/models';
import { SvelteMap } from 'svelte/reactivity';
export interface ModelItem {
option: ModelOption;
flatIndex: number;
}
export interface OrgGroup {
orgName: string | null;
items: ModelItem[];
}
export interface GroupedModelOptions {
loaded: ModelItem[];
favorites: ModelItem[];
available: OrgGroup[];
}
function matchesModality(option: ModelOption, term: string): boolean {
const modalities = option.modalities;
if (!modalities) return false;
switch (term) {
case ModelModality.VISION.toLowerCase():
return modalities.vision;
case ModelModality.AUDIO.toLowerCase():
return modalities.audio;
case ModelModality.VIDEO.toLowerCase():
return modalities.video;
default:
return false;
}
}
export function filterModelOptions(options: ModelOption[], searchTerm: string): ModelOption[] {
const term = searchTerm.trim().toLowerCase();
if (!term) return options;
return options.filter(
(option) =>
option.model.toLowerCase().includes(term) ||
option.name?.toLowerCase().includes(term) ||
option.aliases?.some((alias: string) => alias.toLowerCase().includes(term)) ||
option.tags?.some((tag: string) => tag.toLowerCase().includes(term)) ||
matchesModality(option, term)
);
}
export function groupModelOptions(
filteredOptions: ModelOption[],
favoriteIds: Set<string>,
isModelLoaded: (model: string) => boolean
): GroupedModelOptions {
// Loaded models
const loaded: ModelItem[] = [];
for (let i = 0; i < filteredOptions.length; i++) {
if (isModelLoaded(filteredOptions[i].model)) {
loaded.push({ flatIndex: i, option: filteredOptions[i] });
}
}
// Favorites (excluding loaded)
const loadedModelIds = new Set(loaded.map((item) => item.option.model));
const favorites: ModelItem[] = [];
for (let i = 0; i < filteredOptions.length; i++) {
if (
favoriteIds.has(filteredOptions[i].model) &&
!loadedModelIds.has(filteredOptions[i].model)
) {
favorites.push({ flatIndex: i, option: filteredOptions[i] });
}
}
// Available models grouped by org (excluding loaded and favorites)
const available: OrgGroup[] = [];
const orgGroups = new SvelteMap<string, ModelItem[]>();
for (let i = 0; i < filteredOptions.length; i++) {
const option = filteredOptions[i];
if (loadedModelIds.has(option.model) || favoriteIds.has(option.model)) continue;
const key = option.parsedId?.orgName ?? '';
if (!orgGroups.has(key)) orgGroups.set(key, []);
orgGroups.get(key)!.push({ flatIndex: i, option });
}
for (const [orgName, items] of orgGroups) {
available.push({ items, orgName: orgName || null });
}
return { available, favorites, loaded };
}
|