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: 2,239 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 | # llama.cpp/examples/speculative-simple
Demonstration of basic greedy speculative decoding
```bash
# spec-type draft-simple
./bin/llama-speculative-simple \
-hf ggml-org/Qwen3-8B-Base-GGUF:Q8_0 \
-hfd ggml-org/Qwen3-0.6B-Base-GGUF \
-p "Here is a quick sort implementation in C++. Just code, no comments:\n\n#include" \
--spec-type draft-simple --spec-draft-n-max 7 -ngld 99 --color on \
-n 256 --temp 0 --top-k 1 --seed 42 -ngl 99 -lv 4
# spec-type draft-mtp
./bin/llama-speculative-simple \
-hf ggml-org/Qwen3.6-27B-GGUF:Q8_0 \
-p "Here is a quick sort implementation in C++. Just code, no comments:\n\n#include" \
--spec-type draft-mtp --spec-draft-n-max 3 -ngld 99 --color on \
-n 256 --temp 0 --top-k 1 --seed 42 -ngl 99 -lv 4
# spec-type draft-mtp (with shared KV cache)
# note: this model needs a <s> token at the start to somewhat work without the chat template
./bin/llama-speculative-simple \
-hf ggml-org/Gemma-4-31B-it-GGUF:Q8_0 \
-p "<s>Here is a quick sort implementation in C++. Just code, no comments:\n\n#include" \
--spec-type draft-mtp --spec-draft-n-max 3 -ngld 99 --color on \
-n 256 --temp 0 --top-k 1 --seed 42 -ngl 99 -lv 4
# spec-type draft-eagle3
./bin/llama-speculative-simple \
-hf ggml-org/gpt-oss-20b-GGUF \
-p "Here is a quick sort implementation in C++. Just code, no comments:\n\n#include" \
--spec-type draft-eagle3 --spec-draft-n-max 3 -ngld 99 --color on \
-n 256 --temp 0 --top-k 1 --seed 42 -ngl 99 -lv 4
# spec-type draft-dflash
./bin/llama-speculative-simple \
-hf ggml-org/Qwen3-8B-GGUF \
-p "Here is a quick sort implementation in C++. Just code, no comments:\n\n#include" \
--spec-type draft-dflash --spec-draft-n-max 7 -ngld 99 --color on \
-n 256 --temp 0 --top-k 1 --seed 42 -ngl 99 -lv 4
# spec-type draft-dspark
./bin/llama-speculative-simple \
-hf ggml-org/Qwen3-8B-GGUF \
-p "Here is a quick sort implementation in C++. Just code, no comments:\n\n#include" \
--spec-type draft-dspark --spec-draft-n-max 7 -ngld 99 --color on \
-n 256 --temp 0 --top-k 1 --seed 42 -ngl 99 -lv 4
```
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