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: 1,765 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 | #pragma once
#include <optional>
#include <string>
#include <vector>
#include "utils.h"
namespace jinja {
// allow differentiate between user input strings and template strings
// transformations should handle this information as follows:
// - one-to-one (e.g., uppercase, lowercase): preserve is_input flag
// - one-to-many (e.g., strip): if input string is marked as is_input, all resulting parts should be marked as is_input
// - many-to-one (e.g., concat): if ALL input parts are marked as is_input, resulting part should be marked as is_input
struct string_part {
bool is_input = false; // may skip parsing special tokens if true
std::string val;
bool is_uppercase() const;
bool is_lowercase() const;
};
struct string {
std::vector<string_part> parts;
string() = default;
string(const std::string & v, bool user_input = false) {
parts.push_back({user_input, v});
}
string(int v) {
parts.push_back({false, std::to_string(v)});
}
string(double v) {
parts.push_back({false, std::to_string(v)});
}
// mark all parts as user input
void mark_input();
std::string str() const;
size_t length() const;
void hash_update(hasher & hash) const noexcept;
bool all_parts_are_input() const;
bool is_uppercase() const;
bool is_lowercase() const;
// mark this string as input if other has ALL parts as input
void mark_input_based_on(const string & other);
string & append(const string & other);
// in-place transformations
string uppercase();
string lowercase();
string capitalize();
string titlecase();
string strip(bool left, bool right, std::optional<const std::string_view> chars = std::nullopt);
};
} // namespace jinja
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