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_kv_keep_only_active.py from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/server/tests/unit/test_kv_keep_only_active.py
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/server/tests/unit/test_kv_keep_only_active.py
-
curl -L -o test_kv_keep_only_active.py https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/server/tests/unit/test_kv_keep_only_active.py
3.52 kB
| import os | |
| import tempfile | |
| import pytest | |
| from utils import * | |
| server = ServerPreset.tinyllama2() | |
| class LogReader: | |
| def __init__(self, path): | |
| self.path = path | |
| self.pos = 0 | |
| def drain(self): | |
| with open(self.path) as f: | |
| f.seek(self.pos) | |
| content = f.read() | |
| self.pos = f.tell() | |
| return content | |
| def create_server(): | |
| global server | |
| server = ServerPreset.tinyllama2() | |
| server.n_slots = 2 | |
| server.n_predict = 4 | |
| server.temperature = 0.0 | |
| server.server_slots = True | |
| server.cache_ram = 100 | |
| server.kv_unified = True | |
| server.debug = True | |
| fd, server.log_path = tempfile.mkstemp(suffix='.log') | |
| os.close(fd) | |
| yield | |
| LONG_PROMPT = ( | |
| "Once upon a time in a land far away, there lived a brave knight " | |
| "who traveled across mountains and rivers to find the legendary " | |
| "golden sword hidden deep within the enchanted forest of whispers. " | |
| "He met many creatures along the way including dragons and fairies " | |
| "and wizards who helped him on his noble quest to save the kingdom." | |
| ) | |
| # idle slot cleared on launch should restore from cache-ram | |
| def test_clear_and_restore(): | |
| global server | |
| server.start() | |
| log = LogReader(server.log_path) | |
| # verify feature is enabled | |
| assert "__TEST_TAG_CACHE_IDLE_SLOTS_ENABLED__" in log.drain() | |
| res = server.make_request("POST", "/completion", data={ | |
| "prompt": LONG_PROMPT, | |
| "id_slot": 0, | |
| "cache_prompt": True, | |
| }) | |
| assert res.status_code == 200 | |
| original_prompt_n = res.body["timings"]["prompt_n"] | |
| # Slot 0 is the only slot with KV — should NOT be cleared | |
| assert "__TEST_TAG_CACHE_IDLE_SLOT__" not in log.drain() | |
| # Launching slot 1 clears idle slot 0 | |
| res = server.make_request("POST", "/completion", data={ | |
| "prompt": "The quick brown fox", | |
| "id_slot": 1, | |
| "cache_prompt": True, | |
| }) | |
| assert res.status_code == 200 | |
| assert "__TEST_TAG_CACHE_IDLE_SLOT__" in log.drain() | |
| # Re-send same prompt — should restore from cache-ram | |
| res = server.make_request("POST", "/completion", data={ | |
| "prompt": LONG_PROMPT, | |
| "cache_prompt": True, | |
| }) | |
| assert res.status_code == 200 | |
| assert "updating prompt cache" in log.drain() | |
| assert res.body["timings"]["cache_n"] > 0 | |
| assert res.body["timings"]["prompt_n"] < original_prompt_n | |
| # Follow-up — slot 0 kept its KV, no clearing needed | |
| res = server.make_request("POST", "/completion", data={ | |
| "prompt": LONG_PROMPT + " The knight finally reached the castle gates.", | |
| "cache_prompt": True, | |
| }) | |
| assert res.status_code == 200 | |
| assert "__TEST_TAG_CACHE_IDLE_SLOT__" not in log.drain() | |
| def test_disabled_with_flag(): | |
| global server | |
| server.no_cache_idle_slots = True | |
| server.start() | |
| log = LogReader(server.log_path) | |
| # Feature should not be enabled | |
| assert "__TEST_TAG_CACHE_IDLE_SLOTS_ENABLED__" not in log.drain() | |
| res = server.make_request("POST", "/completion", data={ | |
| "prompt": LONG_PROMPT, | |
| "id_slot": 0, | |
| "cache_prompt": True, | |
| }) | |
| assert res.status_code == 200 | |
| # Request on different slot — should NOT trigger clearing | |
| res = server.make_request("POST", "/completion", data={ | |
| "prompt": "The quick brown fox", | |
| "id_slot": 1, | |
| "cache_prompt": True, | |
| }) | |
| assert res.status_code == 200 | |
| assert "__TEST_TAG_CACHE_IDLE_SLOT__" not in log.drain() | |