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 tests/peg-parser/test-json-serialization.cpp from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.02 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tests/peg-parser/test-json-serialization.cpp
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tests/peg-parser/test-json-serialization.cpp
-
curl -L -o test-json-serialization.cpp https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tests/peg-parser/test-json-serialization.cpp
1.02 kB
| void test_json_serialization(testing &t) { | |
| auto original = build_peg_parser([](common_peg_parser_builder & p) { | |
| return "<tool_call>" + p.json() + "</tool_call>"; | |
| }); | |
| auto json_serialized = original.to_json().dump(); | |
| t.test("compare before/after", [&](testing &t) { | |
| auto deserialized = common_peg_arena::from_json(common_json::parse(json_serialized)); | |
| // Test complex JSON | |
| std::string input = R"({"name": "test", "values": [1, 2, 3], "nested": {"a": true}})"; | |
| common_peg_parse_context ctx1(input); | |
| common_peg_parse_context ctx2(input); | |
| auto result1 = original.parse(ctx1); | |
| auto result2 = deserialized.parse(ctx2); | |
| t.assert_equal("both_succeed", result1.success(), result2.success()); | |
| t.assert_equal("same_end_pos", result1.end, result2.end); | |
| }); | |
| t.bench("deserialize", [&]() { | |
| auto deserialized = common_peg_arena::from_json(common_json::parse(json_serialized)); | |
| }, 100); | |
| } | |