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: 1,132 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 62 63 | #include "ngram-mod.h"
#include <algorithm>
//
// common_ngram_mod
//
common_ngram_mod::common_ngram_mod(uint16_t n, size_t size) : n(n), used(0) {
entries.resize(size);
reset();
}
size_t common_ngram_mod::idx(const entry_t * tokens) const {
size_t res = 0;
for (size_t i = 0; i < n; ++i) {
res = res*6364136223846793005ULL + tokens[i];
}
res = res % entries.size();
return res;
}
void common_ngram_mod::add(const entry_t * tokens) {
const size_t i = idx(tokens);
if (entries[i] == EMPTY) {
used++;
}
entries[i] = tokens[n];
}
common_ngram_mod::entry_t common_ngram_mod::get(const entry_t * tokens) const {
const size_t i = idx(tokens);
return entries[i];
}
void common_ngram_mod::reset() {
std::fill(entries.begin(), entries.end(), EMPTY);
used = 0;
}
size_t common_ngram_mod::get_n() const {
return n;
}
size_t common_ngram_mod::get_used() const {
return used;
}
size_t common_ngram_mod::size() const {
return entries.size();
}
size_t common_ngram_mod::size_bytes() const {
return entries.size() * sizeof(entries[0]);
}
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