Steps to call TinyLLaMA from C++ (using llama.cpp)

Sat, 19 Jul 2025 09:40:53 +0900

✅ TinyLLaMA × C++ Setup Steps (using `llama.cpp`)

🔧 Step 1: Prepare llama.cpp

1-1. Required Environment (Minimum)

OS: Windows / Linux / macOS
Development Environment: g++ / clang / MSVC
Git / CMake

1-2. Get and Build llama.cpp

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git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
mkdir build
cd build
cmake ..
cmake --build . --config Release

On Windows, it is easier to use cmake --build . --config Release in the Visual Studio Developer Command Prompt.

📦 Step 2: Download and Convert the TinyLLaMA Model

2-1. Get the Original Model from HuggingFace

Example: TinyLLaMA-1.1B

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# Download using transformers if necessary
pip install transformers huggingface_hub

python3 -m transformers.models.llama.convert_llama_weights_to_hf \
 --input_dir ./TinyLlama-1.1B-Chat \
 --model_size 1B \
 --output_dir ./hf_model

This is a step to convert into Hugging Face format.

2-2. Convert to GGUF format (for `llama.cpp`)

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cd llama.cpp
python3 convert.py ./hf_model --outfile tinyllama.gguf

2-3. Model Quantization (Size Reduction)

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./quantize ./tinyllama.gguf ./tinyllama-q4.gguf q4_0

q4_0 is 4-bit quantization. The model size will be reduced to around 350MB.

🧪 Step 3: Call the Model from C++ (Code Example)

3-1. Simple C++ Code (Inference)

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#include "llama.h"
#include <iostream>

int main() {
 llama_model_params model_params = llama_model_default_params();
 llama_context_params ctx_params = llama_context_default_params();

 llama_model *model = llama_load_model_from_file("tinyllama-q4.gguf", model_params);
 llama_context *ctx = llama_new_context_with_model(model, ctx_params);

 std::string prompt = "The user says they want to read Excel data, filter it, and save it. What is the node configuration?";
 llama_batch batch = llama_batch_init(512, 0, 1);
 llama_token BOS = llama_token_bos(model);
 batch.token[0] = BOS;

 // Tokenize
 std::vector<llama_token> tokens(prompt.size() + 8);
 int n = llama_tokenize(model, prompt.c_str(), tokens.data(), tokens.size(), true);
 tokens.resize(n);

 for (size_t i = 0; i < tokens.size(); ++i) {
 batch.token[i + 1] = tokens[i];
 }

 batch.n_tokens = tokens.size() + 1;
 llama_decode(ctx, batch);

 // Get Inference Result
 for (int i = 0; i < 50; ++i) {
 llama_token next = llama_sample_token(ctx, nullptr);
 std::cout << llama_token_to_str(model, next);
 llama_batch next_batch = llama_batch_init(1, 0, 1);
 next_batch.token[0] = next;
 next_batch.n_tokens = 1;
 llama_decode(ctx, next_batch);
 }

 llama_free(ctx);
 llama_free_model(model);
 return 0;
}

🧱 Step 4: Compilation Method (Example)

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g++ -I./llama.cpp main.cpp ./llama.cpp/build/libllama.a -o tiny_infer -pthread -std=c++11

libllama.a will be created in the build/ directory after building.

✅ Example Structure of Output (Organized)

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my_app/
├── tinyllama-q4.gguf # Quantized model (~350MB)
├── main.cpp # C++ code above
├── llama.cpp/ # llama.cpp core
└── build/
 └── libllama.a # Compiled library

🧠 Additional Notes for Applying to Use Cases

Include code in C++ to match and select node templates based on the output
Example: If “Excel”, “filter”, and “save” are included -> Generate corresponding nodes
A simple structure like if statements + JSON template loading is fine for this part

📌 Summary

Item	Content
Recommended Model	TinyLLaMA-1.1B-Chat v1.0 (GGUF + Quantization)
Size	~350-450MB (4-bit quantization)
C++ Integration	Possible using `llama.cpp`, almost no external dependencies
Processing Capability	Sufficient for basic intent understanding and output generation (Natural Language -> Structure)
Scalability	Can be made into a node generation AI by combining slot filling and template calling

TinyLLaMA on kenji.blog

Steps to call TinyLLaMA from C++ (using llama.cpp)

✅ TinyLLaMA × C++ Setup Steps (using llama.cpp)

🔧 Step 1: Prepare llama.cpp

1-1. Required Environment (Minimum)

1-2. Get and Build llama.cpp

📦 Step 2: Download and Convert the TinyLLaMA Model

2-1. Get the Original Model from HuggingFace

2-2. Convert to GGUF format (for llama.cpp)

2-3. Model Quantization (Size Reduction)

🧪 Step 3: Call the Model from C++ (Code Example)

3-1. Simple C++ Code (Inference)

🧱 Step 4: Compilation Method (Example)

✅ Example Structure of Output (Organized)

🧠 Additional Notes for Applying to Use Cases

📌 Summary

✅ TinyLLaMA × C++ Setup Steps (using `llama.cpp`)

2-2. Convert to GGUF format (for `llama.cpp`)