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/server/tests/unit/test_stream.py from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 5.44 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/server/tests/unit/test_stream.py
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/server/tests/unit/test_stream.py
-
curl -L -o test_stream.py https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/server/tests/unit/test_stream.py
5.44 kB
| import json | |
| import socket | |
| import threading | |
| import time | |
| from urllib.parse import quote | |
| import pytest | |
| from utils import * | |
| server: ServerProcess | |
| # a model name with slashes exercises the query string routing of the stream routes: the id | |
| # cannot travel as a path param because the decoded slash would split it before capture | |
| MODEL = "ggml-org/tinygemma3-GGUF:Q8_0" | |
| STREAM_ID = f"conv-stream-test::{MODEL}" | |
| QS = "conv_id=" + quote(STREAM_ID, safe="") | |
| def create_server(): | |
| global server | |
| server = ServerPreset.router() | |
| def test_stream_resume_and_stop_with_slashed_model_name(): | |
| global server | |
| server.start() | |
| content = "" | |
| for data in server.make_stream_request("POST", "/chat/completions", data={ | |
| "model": MODEL, | |
| "stream": True, | |
| "max_tokens": 16, | |
| "messages": [{"role": "user", "content": "hello"}], | |
| }, headers={"X-Conversation-Id": STREAM_ID}): | |
| if data["choices"]: | |
| content += data["choices"][0]["delta"].get("content") or "" | |
| assert len(content) > 0 | |
| # the finished session replays from the beginning through the router | |
| res = server.make_request("GET", f"/v1/stream?{QS}&from=0") | |
| assert res.status_code == 200 | |
| assert "data: " in str(res.body) | |
| # the explicit stop reaches the owning child and evicts the session | |
| res = server.make_request("DELETE", f"/v1/stream?{QS}") | |
| assert res.status_code == 204 | |
| res = server.make_request("GET", f"/v1/stream?{QS}&from=0") | |
| assert res.status_code == 404 | |
| def test_stream_stop_during_model_load(): | |
| global server | |
| server.start() | |
| thread_error: list[ServerError] = [] | |
| thread_done = threading.Event() | |
| def fire_post(): | |
| try: | |
| for _ in server.make_stream_request("POST", "/chat/completions", data={ | |
| "model": MODEL, | |
| "stream": True, | |
| "max_tokens": 512, | |
| "messages": [{"role": "user", "content": "Count from 1 to 1000."}], | |
| }, headers={"X-Conversation-Id": STREAM_ID}): | |
| pass | |
| except ServerError as e: | |
| thread_error.append(e) | |
| finally: | |
| thread_done.set() | |
| t = threading.Thread(target=fire_post) | |
| t.start() | |
| # catch the autoload window, tiny models load fast so poll aggressively | |
| saw_loading = False | |
| deadline = time.time() + 5.0 | |
| while time.time() < deadline and not thread_done.is_set(): | |
| res = server.make_request("GET", "/models") | |
| status = next(m["status"]["value"] for m in res.body["data"] if m["id"] == MODEL) | |
| if status == "loading": | |
| saw_loading = True | |
| break | |
| time.sleep(0.002) | |
| if not saw_loading: | |
| t.join() | |
| pytest.skip("load window too short to be observed on this machine") # ty: ignore[too-many-positional-arguments] | |
| # a stop during the load cancels the parked request instead of leaving an orphan | |
| res = server.make_request("DELETE", f"/v1/stream?{QS}") | |
| assert res.status_code == 204 | |
| assert thread_done.wait(timeout=60) | |
| t.join() | |
| assert len(thread_error) == 1 | |
| assert thread_error[0].code == 400 | |
| assert "cancelled" in json.dumps(thread_error[0].body) | |
| res = server.make_request("GET", f"/v1/stream?{QS}&from=0") | |
| assert res.status_code == 404 | |
| def test_stream_resumes_after_reload_during_model_load(): | |
| global server | |
| server.start() | |
| # raw socket client so the connection can be dropped mid load like a page reload | |
| body = json.dumps({ | |
| "model": MODEL, | |
| "stream": True, | |
| "max_tokens": 16, | |
| "messages": [{"role": "user", "content": "hello"}], | |
| }) | |
| request = ( | |
| f"POST /v1/chat/completions HTTP/1.1\r\n" | |
| f"Host: {server.server_host}:{server.server_port}\r\n" | |
| f"Content-Type: application/json\r\n" | |
| f"X-Conversation-Id: {STREAM_ID}\r\n" | |
| f"Content-Length: {len(body)}\r\n" | |
| f"Connection: close\r\n\r\n{body}" | |
| ) | |
| sock = socket.create_connection((server.server_host, server.server_port)) | |
| sock.sendall(request.encode()) | |
| # drop the client while the model loads, poll aggressively to catch the window | |
| saw_loading = False | |
| saw_503 = False | |
| deadline = time.time() + 5.0 | |
| while time.time() < deadline: | |
| res = server.make_request("GET", "/models") | |
| status = next(m["status"]["value"] for m in res.body["data"] if m["id"] == MODEL) | |
| if status == "loading": | |
| saw_loading = True | |
| break | |
| if status == "loaded": | |
| break | |
| time.sleep(0.002) | |
| sock.close() | |
| if not saw_loading: | |
| pytest.skip("load window too short to be observed on this machine") # ty: ignore[too-many-positional-arguments] | |
| # while the model loads the resume route answers retry later, then the session appears, | |
| # receives the whole generation despite the dead client, and replays from the beginning | |
| deadline = time.time() + 60.0 | |
| replay = None | |
| while time.time() < deadline: | |
| res = server.make_request("GET", f"/v1/stream?{QS}&from=0") | |
| if res.status_code == 503: | |
| saw_503 = True | |
| elif res.status_code == 200 and "data: " in str(res.body): | |
| replay = res | |
| break | |
| time.sleep(0.1) | |
| assert saw_503, "resume during the load did not answer 503" | |
| assert replay is not None, "session never became resumable after the client disconnect" | |