Implementation:Ggml org Llama cpp Llama Model Load For Chat

Aspect	Detail
Implementation Name	Llama Model Load For Chat
Doc Type	Pattern Doc
Category	Model Loading
Workflow	Interactive_Chat
Applies To	llama.cpp
Status	Active

Overview

Description

This pattern documents the complete sequence of loading a model and creating a context specifically for multi-turn chat in llama.cpp. The pattern covers three steps: configuring and loading the model from a GGUF file, obtaining the vocabulary handle, and initializing a context with chat-appropriate parameters. This is the foundational setup required before any chat interaction can begin.

Usage

This pattern is used at application startup in any llama.cpp-based chat application. It is a one-time initialization that produces three essential objects: a llama_model for the loaded weights, a llama_vocab for tokenization, and a llama_context for inference. All three must remain alive for the duration of the chat session and must be freed in reverse order upon shutdown.

Code Reference

Attribute	Value
Source Location	`examples/simple-chat/simple-chat.cpp:69-89`
Key Functions	`llama_model_default_params()`, `llama_model_load_from_file()`, `llama_model_get_vocab()`, `llama_context_default_params()`, `llama_init_from_model()`
Import	`#include "llama.h"`

Key Signatures:

// Model parameter defaults and loading
struct llama_model_params llama_model_default_params(void);
struct llama_model * llama_model_load_from_file(const char * path_model, struct llama_model_params params);

// Vocabulary access
const struct llama_vocab * llama_model_get_vocab(const struct llama_model * model);

// Context parameter defaults and creation
struct llama_context_params llama_context_default_params(void);
struct llama_context * llama_init_from_model(struct llama_model * model, struct llama_context_params params);

I/O Contract

Direction	Name	Type	Description
Input	path_model	`const char *`	File system path to the GGUF model file
Input	n_gpu_layers	`int32_t`	Number of layers to offload to GPU (e.g., 99 for all)
Input	n_ctx	`uint32_t`	Context window size in tokens (e.g., 2048)
Output	model	`llama_model *`	Loaded model handle, or NULL on failure
Output	vocab	`const llama_vocab *`	Vocabulary handle for tokenization
Output	ctx	`llama_context *`	Inference context handle, or NULL on failure

Preconditions:

ggml_backend_load_all() must be called before model loading to initialize dynamic backends
The GGUF file at path_model must exist and be a valid model file

Postconditions:

On success, all three output handles are valid and ready for use
On failure, the caller must check for NULL and handle the error

Usage Examples

// Step 1: Load dynamic backends
ggml_backend_load_all();

// Step 2: Initialize the model
llama_model_params model_params = llama_model_default_params();
model_params.n_gpu_layers = 99;  // offload all layers to GPU

llama_model * model = llama_model_load_from_file("model.gguf", model_params);
if (!model) {
    fprintf(stderr, "error: unable to load model\n");
    return 1;
}

// Step 3: Get the vocabulary
const llama_vocab * vocab = llama_model_get_vocab(model);

// Step 4: Initialize the context
llama_context_params ctx_params = llama_context_default_params();
ctx_params.n_ctx   = 2048;  // context window for chat
ctx_params.n_batch = 2048;  // match batch size to context size

llama_context * ctx = llama_init_from_model(model, ctx_params);
if (!ctx) {
    fprintf(stderr, "error: failed to create the llama_context\n");
    return 1;
}

// ... use model, vocab, ctx for chat ...

// Cleanup (reverse order)
llama_free(ctx);
llama_model_free(model);

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