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: 1,878 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 | import pytest
from utils import *
import threading
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
server = ServerPreset.tinyllama2()
@pytest.fixture(autouse=True)
def create_server():
global server
server = ServerPreset.tinyllama2()
def test_mcp_no_proxy():
global server
server.ui_mcp_proxy = False
server.start()
res = server.make_request("GET", "/cors-proxy")
assert res.status_code == 403
def test_mcp_proxy():
global server
server.ui_mcp_proxy = True
server.start()
url = f"http://{server.server_host}:{server.server_port}/cors-proxy?url=http://example.com"
res = requests.get(url)
assert res.status_code == 200
assert "Example Domain" in res.text
def test_mcp_proxy_custom_port():
global server
server.ui_mcp_proxy = True
server.start()
# try getting the server's models API via the proxy
res = server.make_request("GET", f"/cors-proxy?url=http://{server.server_host}:{server.server_port}/models")
assert res.status_code == 200
assert "data" in res.body
def test_mcp_proxy_no_content():
# note: see issue #26598
class NoContentHandler(BaseHTTPRequestHandler):
def do_POST(self):
self.send_response(204)
self.end_headers()
def log_message(self, format, *args):
pass
target = ThreadingHTTPServer(("127.0.0.1", 0), NoContentHandler)
target_thread = threading.Thread(target=target.serve_forever, daemon=True)
target_thread.start()
try:
global server
server.ui_mcp_proxy = True
server.start()
res = server.make_request("POST", f"/cors-proxy?url=http://127.0.0.1:{target.server_port}/", data={})
assert res.status_code == 204
assert res.body in (None, b"", "")
finally:
target.shutdown()
target.server_close()
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