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: 1,800 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 | import pytest
from utils import *
server: ServerProcess
@pytest.fixture(autouse=True)
def create_server():
global server
server = ServerPreset.tinyllama2()
server.gcp_compat = True
def test_gcp_predict_camel_case():
global server
server.start()
res = server.make_request("POST", "/predict", data={
"instances": [
{
"@requestFormat": "chatCompletions",
"max_tokens": 8,
"messages": [
{"role": "user", "content": "What is the meaning of life?"},
],
}
],
})
assert res.status_code == 200
assert "predictions" in res.body
assert len(res.body["predictions"]) == 1
prediction = res.body["predictions"][0]
assert "choices" in prediction
assert len(prediction["choices"]) == 1
assert prediction["choices"][0]["message"]["role"] == "assistant"
assert len(prediction["choices"][0]["message"]["content"]) > 0
def test_gcp_predict_multiple_instances():
global server
server.n_slots = 2
server.start()
res = server.make_request("POST", "/predict", data={
"instances": [
{
"@requestFormat": "chatCompletions",
"max_tokens": 8,
"messages": [{"role": "user", "content": "Say hello"}],
},
{
"@requestFormat": "chatCompletions",
"max_tokens": 8,
"messages": [{"role": "user", "content": "Say world"}],
},
],
})
assert res.status_code == 200
assert len(res.body["predictions"]) == 2
for prediction in res.body["predictions"]:
assert "choices" in prediction
assert len(prediction["choices"][0]["message"]["content"]) > 0
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