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_ignore_eos.py from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.27 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/server/tests/unit/test_ignore_eos.py
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/server/tests/unit/test_ignore_eos.py
-
curl -L -o test_ignore_eos.py https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/server/tests/unit/test_ignore_eos.py
1.27 kB
| import pytest | |
| from utils import * | |
| server = ServerPreset.tinyllama2() | |
| def create_server(): | |
| global server | |
| server = ServerPreset.tinyllama2() | |
| def test_ignore_eos_populates_logit_bias(): | |
| """ignore_eos=true must add EOG logit biases to generation_settings.""" | |
| global server | |
| server.start() | |
| res = server.make_request("POST", "/completion", data={ | |
| "n_predict": 8, | |
| "prompt": "Once upon a time", | |
| "ignore_eos": True, | |
| "temperature": 0.0, | |
| }) | |
| assert res.status_code == 200 | |
| # EOG token biases must be present with -inf bias | |
| logit_bias = res.body["generation_settings"]["logit_bias"] | |
| assert len(logit_bias) > 0 | |
| for entry in logit_bias: | |
| assert entry["bias"] is None # null in JSON represents -inf | |
| def test_ignore_eos_false_no_logit_bias(): | |
| """ignore_eos=false (default) must NOT add EOG logit biases.""" | |
| global server | |
| server.start() | |
| res = server.make_request("POST", "/completion", data={ | |
| "n_predict": 8, | |
| "prompt": "Once upon a time", | |
| "ignore_eos": False, | |
| "temperature": 0.0, | |
| }) | |
| assert res.status_code == 200 | |
| logit_bias = res.body["generation_settings"]["logit_bias"] | |
| assert len(logit_bias) == 0 | |