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: 2,400 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 | /// <reference types="@vitest/browser/matchers" />
/// <reference types="@vitest/browser/providers/playwright" />
import { beforeEach, vi } from 'vitest';
// Mock fetch for API calls during client tests.
// In test environment there is no backend server, so we intercept
// the specific endpoints the app uses and return valid mock data.
// The passthrough target is captured once at module load: capturing it
// inside beforeEach grabs the previous test's spy (vi.spyOn returns the
// existing spy), making the default branch recurse on itself as soon as
// a test fetches a URL outside the mocked set.
const originalFetch = globalThis.fetch.bind(globalThis);
beforeEach(() => {
vi.spyOn(globalThis, 'fetch').mockImplementation(
async (input: RequestInfo | URL, init?: RequestInit) => {
const url = typeof input === 'string' ? input : input instanceof URL ? input.href : input.url;
// Mock server props endpoint
if (url.includes('/server')) {
return new Response(
JSON.stringify({
git_branch: 'test',
git_commit: 'test',
mode: 'router',
version: 'test'
}),
{ headers: { 'Content-Type': 'application/json' }, status: 200 }
);
}
// Mock models list endpoint
if (/\/v1\/models|\/models\b/.test(url)) {
return new Response(
JSON.stringify({
data: [
{
created: 0,
id: 'test-model.gguf',
in_cache: false,
meta: {},
object: 'model',
owned_by: 'llamacpp',
path: 'models/test-model.gguf',
status: { value: 'unloaded' }
}
],
models: [
{
details: {},
model: 'test-model.gguf',
name: 'Test Model'
}
],
object: 'list'
}),
{ headers: { 'Content-Type': 'application/json' }, status: 200 }
);
}
// Mock /props endpoint (used for modalities)
if (url.includes('/props')) {
return new Response(
JSON.stringify({
default_generation_settings: { n_ctx: 2048 }
}),
{ headers: { 'Content-Type': 'application/json' }, status: 200 }
);
}
// Mock /tools endpoint (used for built-in tools list)
if (url.includes('/tools')) {
return new Response(JSON.stringify([]), {
headers: { 'Content-Type': 'application/json' },
status: 200
});
}
// Default: use real fetch
return originalFetch(input, init);
}
);
});
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