Introduction
Large Language Models (LLMs) have revolutionized natural language processing, enabling applications to understand and generate human-like text. While many cloud-based solutions exist, there's growing interest in running these models locally for privacy, reduced latency, and offline usage. GoLlama is a tool designed to bring the power of LLMs to local environments using the Go programming language and the efficient llama.cpp library.
GoLlama aims to provide a flexible framework for inference, fine-tuning, and even training of LLMs, all with the performance benefits of Go and the optimized C++ backend of llama.cpp. This article explores the implementation details of GoLlama, covering everything from the core inferencing engine to advanced features like model quantization and fine-tuning.
Architecture Overview
GoLlama's architecture consists of several key components that work together to provide a complete LLM application. At its core is the inference engine, which leverages llama.cpp through CGO bindings to perform the actual text generation. This is surrounded by model management utilities, a tokenizer, and either a console or GUI interface.
The application follows a layered architecture:
- Interface Layer (Console/GUI)
- Application Layer (Session management, context handling)
- Model Layer (Loading, quantization, fine-tuning)
- Core Engine Layer (Bindings to llama.cpp)
This separation of concerns allows for flexibility in deployment and usage scenarios, from simple command-line applications to full-featured GUI tools.
Setting Up the Development Environment
Before diving into implementation, we need to set up our development environment. GoLlama requires both Go and C++ development tools, as it interfaces with llama.cpp through CGO.
First, we need to install Go (version 1.18 or higher recommended), a C++ compiler (GCC or Clang), and the necessary build tools. We'll also need to clone the llama.cpp repository and build it as a shared library.
For optimal performance, we should ensure that our build environment supports the appropriate CPU extensions (AVX2, AVX512, etc.) and potentially CUDA for GPU acceleration.
Implementing the Core Inferencing Engine
The heart of GoLlama is its inferencing engine, which interfaces with llama.cpp to perform text generation. We'll use CGO to create bindings to the C++ library.
Let's start by defining the core structures and functions needed for the inferencing engine:
package core
// #cgo CFLAGS: -I${SRCDIR}/llama.cpp/include
// #cgo LDFLAGS: -L${SRCDIR}/llama.cpp -lllama
// #include <llama.h>
// #include <stdlib.h>
import "C"
import (
"errors"
"runtime"
"sync"
"unsafe"
)
type LlamaModel struct {
model C.llama_model
context C.llama_context
params C.llama_context_params
mutex sync.Mutex
tokenizer *Tokenizer
}
func NewLlamaModel(modelPath string) (*LlamaModel, error) {
cModelPath := C.CString(modelPath)
defer C.free(unsafe.Pointer(cModelPath))
// Initialize llama.cpp
C.llama_backend_init(C.bool(true))
// Set default parameters
params := C.llama_context_default_params()
// Load the model
model := C.llama_load_model_from_file(cModelPath, params)
if model == nil {
return nil, errors.New("failed to load model")
}
// Create context
ctx := C.llama_new_context_with_model(model, params)
if ctx == nil {
C.llama_free_model(model)
return nil, errors.New("failed to create context")
}
// Create and return the model wrapper
llamaModel := &LlamaModel{
model: model,
context: ctx,
params: params,
}
// Set up finalizer to ensure resources are freed
runtime.SetFinalizer(llamaModel, freeLlamaModel)
return llamaModel, nil
}
func freeLlamaModel(lm *LlamaModel) {
C.llama_free_context(lm.context)
C.llama_free_model(lm.model)
}
The code above establishes the foundation for our inferencing engine. It creates a Go wrapper around the llama.cpp model and context, handling memory management through CGO. The NewLlamaModel function initializes llama.cpp, loads the model from a file, and creates a context for text generation. We also set up a finalizer to ensure that resources are properly freed when the Go garbage collector collects our model object.
Next, let's implement the core inference function that generates text:
func (lm *LlamaModel) Generate(prompt string, maxTokens int, temperature float32, topP float32) (string, error) {
lm.mutex.Lock()
defer lm.mutex.Unlock()
// Convert prompt to tokens
tokens, err := lm.tokenizer.Encode(prompt)
if err != nil {
return "", err
}
// Allocate memory for input tokens
cTokens := make([]C.llama_token, len(tokens))
for i, t := range tokens {
cTokens[i] = C.llama_token(t)
}
// Evaluate the prompt
if C.llama_eval(lm.context, (*C.llama_token)(&cTokens[0]), C.int(len(cTokens)), C.int(0), nil) != 0 {
return "", errors.New("failed to evaluate prompt")
}
// Generate new tokens
result := prompt
for i := 0; i < maxTokens; i++ {
// Get logits for the last token
logits := C.llama_get_logits(lm.context)
n_vocab := C.llama_n_vocab(lm.model)
// Apply temperature and top-p sampling
var nextToken C.llama_token
if temperature == 0 {
// Greedy sampling
nextToken = C.llama_sample_token_greedy(lm.context)
} else {
// Apply temperature
C.llama_sample_repetition_penalty(lm.context, nil, 0, 1.1)
C.llama_sample_temperature(lm.context, temperature)
// Apply top-p sampling
C.llama_sample_top_p(lm.context, topP)
// Sample a token
nextToken = C.llama_sample_token(lm.context)
}
// Check for end of generation
if nextToken == C.llama_token_eos() {
break
}
// Convert token to string and append to result
tokenStr := C.llama_token_to_str(lm.model, nextToken)
result += C.GoString(tokenStr)
// Evaluate the new token
if C.llama_eval(lm.context, &nextToken, C.int(1), C.int(i+len(tokens)), nil) != 0 {
return result, errors.New("failed to evaluate generated token")
}
}
return result, nil
}
This function takes a prompt string and generation parameters, then produces a completion. It first converts the prompt to tokens using our tokenizer, evaluates these tokens using the model, and then generates new tokens one by one. For each new token, we apply temperature and top-p sampling to control the randomness of the output. The generated tokens are converted back to strings and appended to the result.
Model Loading and Management
Managing models efficiently is crucial for GoLlama. We need to implement functions for loading, unloading, and switching between models. Let's create a model manager:
package models
import (
"fmt"
"os"
"path/filepath"
"sync"
"github.com/<GitHubUserName>/gollama/core"
)
type ModelManager struct {
modelsDir string
loadedModels map[string]*core.LlamaModel
mutex sync.RWMutex
}
func NewModelManager(modelsDir string) (*ModelManager, error) {
// Check if models directory exists
if _, err := os.Stat(modelsDir); os.IsNotExist(err) {
return nil, fmt.Errorf("models directory does not exist: %s", modelsDir)
}
return &ModelManager{
modelsDir: modelsDir,
loadedModels: make(map[string]*core.LlamaModel),
}, nil
}
func (mm *ModelManager) LoadModel(modelName string) (*core.LlamaModel, error) {
mm.mutex.Lock()
defer mm.mutex.Unlock()
// Check if model is already loaded
if model, exists := mm.loadedModels[modelName]; exists {
return model, nil
}
// Find the model file
modelPath := filepath.Join(mm.modelsDir, modelName)
if _, err := os.Stat(modelPath); os.IsNotExist(err) {
return nil, fmt.Errorf("model file not found: %s", modelPath)
}
// Load the model
model, err := core.NewLlamaModel(modelPath)
if err != nil {
return nil, fmt.Errorf("failed to load model %s: %v", modelName, err)
}
// Store in loaded models map
mm.loadedModels[modelName] = model
return model, nil
}
func (mm *ModelManager) UnloadModel(modelName string) error {
mm.mutex.Lock()
defer mm.mutex.Unlock()
if model, exists := mm.loadedModels[modelName]; exists {
// The model will be garbage collected, and the finalizer will free resources
delete(mm.loadedModels, modelName)
return nil
}
return fmt.Errorf("model not loaded: %s", modelName)
}
func (mm *ModelManager) ListAvailableModels() ([]string, error) {
var models []string
entries, err := os.ReadDir(mm.modelsDir)
if err != nil {
return nil, err
}
for _, entry := range entries {
if !entry.IsDir() && (filepath.Ext(entry.Name()) == ".bin" || filepath.Ext(entry.Name()) == ".gguf") {
models = append(models, entry.Name())
}
}
return models, nil
}
The model manager provides functions to load models from a specified directory, unload them when they're no longer needed, and list all available models. It uses a map to keep track of loaded models and a mutex to ensure thread safety when multiple goroutines access the manager.
Quantization Techniques
Quantization is a crucial technique for reducing the memory footprint and increasing the inference speed of LLMs. GoLlama should provide tools for quantizing models to different precision levels.
Let's implement a quantization module:
package quantization
// #cgo CFLAGS: -I${SRCDIR}/llama.cpp/include
// #cgo LDFLAGS: -L${SRCDIR}/llama.cpp -lllama
// #include <llama.h>
// #include <stdlib.h>
import "C"
import (
"errors"
"fmt"
"os"
"path/filepath"
"unsafe"
)
type QuantizationType int
const (
Q4_0 QuantizationType = iota
Q4_1
Q5_0
Q5_1
Q8_0
Q8_1
)
func (qt QuantizationType) String() string {
switch qt {
case Q4_0:
return "q4_0"
case Q4_1:
return "q4_1"
case Q5_0:
return "q5_0"
case Q5_1:
return "q5_1"
case Q8_0:
return "q8_0"
case Q8_1:
return "q8_1"
default:
return "unknown"
}
}
func QuantizeModel(inputPath, outputPath string, quantType QuantizationType) error {
// Check if input file exists
if _, err := os.Stat(inputPath); os.IsNotExist(err) {
return fmt.Errorf("input model file does not exist: %s", inputPath)
}
// Create output directory if it doesn't exist
outputDir := filepath.Dir(outputPath)
if _, err := os.Stat(outputDir); os.IsNotExist(err) {
if err := os.MkdirAll(outputDir, 0755); err != nil {
return fmt.Errorf("failed to create output directory: %v", err)
}
}
// Convert paths to C strings
cInputPath := C.CString(inputPath)
defer C.free(unsafe.Pointer(cInputPath))
cOutputPath := C.CString(outputPath)
defer C.free(unsafe.Pointer(cOutputPath))
// Set quantization parameters
params := C.llama_model_quantize_params{
nthread: C.int(8), // Use 8 threads for quantization
}
// Set quantization type
switch quantType {
case Q4_0:
params.ftype = C.LLAMA_FTYPE_MOSTLY_Q4_0
case Q4_1:
params.ftype = C.LLAMA_FTYPE_MOSTLY_Q4_1
case Q5_0:
params.ftype = C.LLAMA_FTYPE_MOSTLY_Q5_0
case Q5_1:
params.ftype = C.LLAMA_FTYPE_MOSTLY_Q5_1
case Q8_0:
params.ftype = C.LLAMA_FTYPE_MOSTLY_Q8_0
case Q8_1:
params.ftype = C.LLAMA_FTYPE_MOSTLY_Q8_1
default:
return errors.New("unsupported quantization type")
}
// Perform quantization
result := C.llama_model_quantize(cInputPath, cOutputPath, params)
if result != 0 {
return fmt.Errorf("quantization failed with error code: %d", result)
}
return nil
}
This module provides a function to quantize models to different precision levels, such as 4-bit, 5-bit, or 8-bit. It uses llama.cpp's quantization functionality through CGO bindings. The quantization process reduces the model size and memory requirements at the cost of some precision, making it possible to run larger models on hardware with limited resources.
Fine-Tuning Approach
Fine-tuning allows adapting pre-trained models to specific domains or tasks. Let's implement a fine-tuning module for GoLlama:
package finetune
// #cgo CFLAGS: -I${SRCDIR}/llama.cpp/include
// #cgo LDFLAGS: -L${SRCDIR}/llama.cpp -lllama
// #include <llama.h>
// #include <stdlib.h>
import "C"
import (
"encoding/json"
"errors"
"fmt"
"os"
"unsafe"
"github.com/<GitHubUserName>/gollama/core"
)
type FineTuningConfig struct {
LearningRate float32 `json:"learning_rate"`
BatchSize int `json:"batch_size"`
Epochs int `json:"epochs"`
WarmupSteps int `json:"warmup_steps"`
SaveCheckpoints bool `json:"save_checkpoints"`
CheckpointDir string `json:"checkpoint_dir"`
EvalInterval int `json:"eval_interval"`
}
type TrainingExample struct {
Prompt string `json:"prompt"`
Completion string `json:"completion"`
}
type FineTuner struct {
model *core.LlamaModel
config FineTuningConfig
}
func NewFineTuner(model *core.LlamaModel, config FineTuningConfig) *FineTuner {
return &FineTuner{
model: model,
config: config,
}
}
func (ft *FineTuner) LoadTrainingData(dataPath string) ([]TrainingExample, error) {
// Read the training data file
data, err := os.ReadFile(dataPath)
if err != nil {
return nil, fmt.Errorf("failed to read training data: %v", err)
}
// Parse the JSON data
var examples []TrainingExample
if err := json.Unmarshal(data, &examples); err != nil {
return nil, fmt.Errorf("failed to parse training data: %v", err)
}
return examples, nil
}
func (ft *FineTuner) FineTune(trainingData []TrainingExample) error {
// Create checkpoint directory if needed
if ft.config.SaveCheckpoints {
if err := os.MkdirAll(ft.config.CheckpointDir, 0755); err != nil {
return fmt.Errorf("failed to create checkpoint directory: %v", err)
}
}
// Prepare training parameters
params := C.llama_training_params{
lr : C.float(ft.config.LearningRate),
batch_size : C.int(ft.config.BatchSize),
epochs : C.int(ft.config.Epochs),
warmup_steps : C.int(ft.config.WarmupSteps),
}
// Prepare training data
for epoch := 0; epoch < ft.config.Epochs; epoch++ {
fmt.Printf("Starting epoch %d/%d\n", epoch+1, ft.config.Epochs)
// Process each training example
for i, example := range trainingData {
// Tokenize prompt and completion
promptTokens, err := ft.model.Tokenizer().Encode(example.Prompt)
if err != nil {
return fmt.Errorf("failed to tokenize prompt: %v", err)
}
completionTokens, err := ft.model.Tokenizer().Encode(example.Completion)
if err != nil {
return fmt.Errorf("failed to tokenize completion: %v", err)
}
// Combine tokens for training
allTokens := append(promptTokens, completionTokens...)
// Convert to C array
cTokens := make([]C.llama_token, len(allTokens))
for j, t := range allTokens {
cTokens[j] = C.llama_token(t)
}
// Train on this example
result := C.llama_train(
ft.model.GetContext(),
(*C.llama_token)(&cTokens[0]),
C.int(len(promptTokens)),
C.int(len(allTokens)),
params,
)
if result != 0 {
return fmt.Errorf("training failed on example %d with error code: %d", i, result)
}
// Print progress
if (i+1) % 10 == 0 {
fmt.Printf(" Processed %d/%d examples\n", i+1, len(trainingData))
}
// Evaluation
if ft.config.EvalInterval > 0 && (i+1) % ft.config.EvalInterval == 0 {
// Perform evaluation
fmt.Println(" Evaluating model...")
// Evaluation logic would go here
}
}
// Save checkpoint after each epoch
if ft.config.SaveCheckpoints {
checkpointPath := fmt.Sprintf("%s/checkpoint_epoch_%d.bin", ft.config.CheckpointDir, epoch+1)
if err := ft.SaveCheckpoint(checkpointPath); err != nil {
fmt.Printf("Warning: Failed to save checkpoint: %v\n", err)
} else {
fmt.Printf("Saved checkpoint to %s\n", checkpointPath)
}
}
}
return nil
}
func (ft *FineTuner) SaveCheckpoint(path string) error {
cPath := C.CString(path)
defer C.free(unsafe.Pointer(cPath))
result := C.llama_save_model(ft.model.GetModel(), cPath)
if result != 0 {
return errors.New("failed to save model checkpoint")
}
return nil
The fine-tuning module provides functionality to adapt pre-trained models to specific tasks or domains. It takes a configuration specifying parameters like learning rate and batch size, loads training examples from a JSON file, and then iteratively trains the model on these examples. The module also supports saving checkpoints during training and evaluating the model at regular intervals.
Training New LLMs
While fine-tuning adapts existing models, training new models from scratch requires more extensive functionality. Let's implement a module for training new LLMs:
package training
// #cgo CFLAGS: -I${SRCDIR}/llama.cpp/include
// #cgo LDFLAGS: -L${SRCDIR}/llama.cpp -lllama
// #include <llama.h>
// #include <stdlib.h>
import "C"
import (
"fmt"
"os"
"path/filepath"
"time"
"unsafe"
)
type TrainingConfig struct {
ModelSize string `json:"model_size"` // "7B", "13B", etc.
HiddenSize int `json:"hidden_size"`
IntermediateSize int `json:"intermediate_size"`
NumLayers int `json:"num_layers"`
NumHeads int `json:"num_heads"`
VocabSize int `json:"vocab_size"`
SequenceLength int `json:"sequence_length"`
BatchSize int `json:"batch_size"`
LearningRate float32 `json:"learning_rate"`
WarmupSteps int `json:"warmup_steps"`
TrainingSteps int `json:"training_steps"`
SaveInterval int `json:"save_interval"`
EvalInterval int `json:"eval_interval"`
OutputDir string `json:"output_dir"`
DataDir string `json:"data_dir"`
}
type Trainer struct {
config TrainingConfig
model C.llama_model
}
func NewTrainer(config TrainingConfig) (*Trainer, error) {
// Create output directory
if err := os.MkdirAll(config.OutputDir, 0755); err != nil {
return nil, fmt.Errorf("failed to create output directory: %v", err)
}
// Initialize llama.cpp
C.llama_backend_init(C.bool(true))
// Create model architecture
modelParams := C.llama_model_params{
n_vocab : C.int(config.VocabSize),
n_ctx : C.int(config.SequenceLength),
n_embd : C.int(config.HiddenSize),
n_mult : C.int(config.IntermediateSize),
n_head : C.int(config.NumHeads),
n_layer : C.int(config.NumLayers),
}
// Initialize new model
model := C.llama_init_model(modelParams)
if model == nil {
return nil, fmt.Errorf("failed to initialize model")
}
return &Trainer{
config: config,
model: model,
}, nil
}
func (t *Trainer) LoadTrainingData() ([]string, error) {
var files []string
entries, err := os.ReadDir(t.config.DataDir)
if err != nil {
return nil, fmt.Errorf("failed to read data directory: %v", err)
}
for _, entry := range entries {
if !entry.IsDir() && filepath.Ext(entry.Name()) == ".txt" {
files = append(files, filepath.Join(t.config.DataDir, entry.Name()))
}
}
if len(files) == 0 {
return nil, fmt.Errorf("no training data files found in %s", t.config.DataDir)
}
return files, nil
}
func (t *Trainer) Train() error {
// Load training data files
dataFiles, err := t.LoadTrainingData()
if err != nil {
return err
}
fmt.Printf("Starting training with %d data files\n", len(dataFiles))
// Create training parameters
trainParams := C.llama_training_params{
lr : C.float(t.config.LearningRate),
batch_size : C.int(t.config.BatchSize),
warmup_steps : C.int(t.config.WarmupSteps),
}
// Create tokenizer for processing text
tokenizer := C.llama_tokenizer_init(t.model)
if tokenizer == nil {
return fmt.Errorf("failed to initialize tokenizer")
}
defer C.llama_tokenizer_free(tokenizer)
// Training loop
startTime := time.Now()
for step := 0; step < t.config.TrainingSteps; step++ {
// Select a random data file for this step
fileIdx := step % len(dataFiles)
dataFile := dataFiles[fileIdx]
// Read a batch of text from the file
data, err := os.ReadFile(dataFile)
if err != nil {
fmt.Printf("Warning: Failed to read file %s: %v\n", dataFile, err)
continue
}
// Convert to C string
cData := C.CString(string(data))
defer C.free(unsafe.Pointer(cData))
// Tokenize the text
var nTokens C.int
tokens := C.llama_tokenize(tokenizer, cData, C.int(len(data)), &nTokens)
if tokens == nil {
fmt.Printf("Warning: Failed to tokenize text from %s\n", dataFile)
continue
}
defer C.free(unsafe.Pointer(tokens))
// Train on this batch
result := C.llama_train_on_tokens(
t.model,
tokens,
nTokens,
trainParams,
)
if result != 0 {
return fmt.Errorf("training failed at step %d with error code: %d", step, result)
}
// Print progress
if (step+1) % 10 == 0 {
elapsed := time.Since(startTime)
fmt.Printf("Step %d/%d (%.2f%%), Time: %s\n",
step+1, t.config.TrainingSteps,
float64(step+1)/float64(t.config.TrainingSteps)*100.0,
elapsed)
}
// Save checkpoint
if t.config.SaveInterval > 0 && (step+1) % t.config.SaveInterval == 0 {
checkpointPath := fmt.Sprintf("%s/checkpoint_step_%d.bin", t.config.OutputDir, step+1)
if err := t.SaveCheckpoint(checkpointPath); err != nil {
fmt.Printf("Warning: Failed to save checkpoint: %v\n", err)
} else {
fmt.Printf("Saved checkpoint to %s\n", checkpointPath)
}
}
// Evaluation
if t.config.EvalInterval > 0 && (step+1) % t.config.EvalInterval == 0 {
fmt.Println("Evaluating model...")
// Evaluation logic would go here
}
}
// Save final model
finalModelPath := fmt.Sprintf("%s/gollama_%s_final.bin", t.config.OutputDir, t.config.ModelSize)
if err := t.SaveCheckpoint(finalModelPath); err != nil {
return fmt.Errorf("failed to save final model: %v", err)
}
fmt.Printf("Training completed. Final model saved to %s\n", finalModelPath)
return nil
}
func (t *Trainer) SaveCheckpoint(path string) error {
cPath := C.CString(path)
defer C.free(unsafe.Pointer(cPath))
result := C.llama_save_model(t.model, cPath)
if result != 0 {
return fmt.Errorf("failed to save model")
}
return nil
}
func (t *Trainer) Close() {
C.llama_free_model(t.model)
}
Building the Console Application
Now let's implement a console application for GoLlama:
package main
import (
"bufio"
"flag"
"fmt"
"os"
"strings"
"github.com/<GitHubUserName>/gollama/core"
"github.com/<GitHubUserName>/gollama/models"
)
func main() {
// Parse command line flags
modelFlag := flag.String("model", "", "Path to the model file")
modelDirFlag := flag.String("model-dir", "./models", "Directory containing models")
contextSizeFlag := flag.Int("ctx-size", 2048, "Context size for inference")
temperatureFlag := flag.Float64("temp", 0.7, "Temperature for sampling")
topPFlag := flag.Float64("top-p", 0.9, "Top-p value for sampling")
maxTokensFlag := flag.Int("max-tokens", 256, "Maximum number of tokens to generate")
flag.Parse()
// Initialize model manager
modelManager, err := models.NewModelManager(*modelDirFlag)
if err != nil {
fmt.Fprintf(os.Stderr, "Error initializing model manager: %v\n", err)
os.Exit(1)
}
// If no model specified, list available models and exit
if *modelFlag == "" {
availableModels, err := modelManager.ListAvailableModels()
if err != nil {
fmt.Fprintf(os.Stderr, "Error listing models: %v\n", err)
os.Exit(1)
}
if len(availableModels) == 0 {
fmt.Println("No models found in", *modelDirFlag)
fmt.Println("Please download a model and place it in the models directory.")
os.Exit(0)
}
fmt.Println("Available models:")
for i, model := range availableModels {
fmt.Printf("%d. %s\n", i+1, model)
}
fmt.Println("\nRun with --model <model_name> to use a specific model")
os.Exit(0)
}
// Load the specified model
model, err := modelManager.LoadModel(*modelFlag)
if err != nil {
fmt.Fprintf(os.Stderr, "Error loading model: %v\n", err)
os.Exit(1)
}
fmt.Printf("Model %s loaded successfully\n", *modelFlag)
fmt.Println("Type your prompts, or '/quit' to exit")
// Start REPL loop
scanner := bufio.NewScanner(os.Stdin)
for {
fmt.Print("> ")
if !scanner.Scan() {
break
}
input := scanner.Text()
if input == "/quit" {
break
}
// Generate response
response, err := model.Generate(
input,
*maxTokensFlag,
float32(*temperatureFlag),
float32(*topPFlag),
)
if err != nil {
fmt.Fprintf(os.Stderr, "Error generating response: %v\n", err)
continue
}
// Print the response
fmt.Println("\n" + strings.TrimPrefix(response, input))
fmt.Println()
}
fmt.Println("Goodbye!")
}
This console application provides a simple command-line interface for interacting with LLMs. It allows users to select a model, set generation parameters, and enter prompts for text generation. The application uses a REPL (Read-Eval-Print Loop) to continuously process user input until the user decides to quit.
Implementing a GUI Version
For users who prefer a graphical interface, let's implement a GUI version of GoLlama using the Fyne toolkit:
package main
import (
"fmt"
"strings"
"fyne.io/fyne/v2"
"fyne.io/fyne/v2/app"
"fyne.io/fyne/v2/container"
"fyne.io/fyne/v2/dialog"
"fyne.io/fyne/v2/layout"
"fyne.io/fyne/v2/theme"
"fyne.io/fyne/v2/widget"
"github.com/<GitHubUserName>/gollama/core"
"github.com/<GitHubUserName>/gollama/models"
)
func main() {
// Create Fyne application
a := app.New()
w := a.NewWindow("GoLlama")
w.Resize(fyne.NewSize(800, 600))
// Initialize model manager
modelManager, err := models.NewModelManager("./models")
if err != nil {
dialog.ShowError(fmt.Errorf("failed to initialize model manager: %v", err), w)
return
}
// Get available models
availableModels, err := modelManager.ListAvailableModels()
if err != nil {
dialog.ShowError(fmt.Errorf("failed to list models: %v", err), w)
return
}
if len(availableModels) == 0 {
dialog.ShowInformation("No Models Found", "Please download a model and place it in the models directory.", w)
return
}
// Create UI components
var currentModel *core.LlamaModel
// Model selection dropdown
modelSelect := widget.NewSelect(availableModels, func(modelName string) {
// Show loading indicator
progress := dialog.NewProgress("Loading Model", "Please wait while the model is loading...", w)
progress.Show()
// Load model in a goroutine to keep UI responsive
go func() {
model, err := modelManager.LoadModel(modelName)
if err != nil {
progress.Hide()
dialog.ShowError(fmt.Errorf("failed to load model: %v", err), w)
return
}
currentModel = model
progress.Hide()
}()
})
modelSelect.PlaceHolder = "Select a model"
// Parameter sliders
tempSlider := widget.NewSlider(0, 1)
tempSlider.Step = 0.05
tempSlider.Value = 0.7
topPSlider := widget.NewSlider(0, 1)
topPSlider.Step = 0.05
topPSlider.Value = 0.9
maxTokensEntry := widget.NewEntry()
maxTokensEntry.SetText("256")
// Input and output areas
promptInput := widget.NewMultiLineEntry()
promptInput.SetPlaceHolder("Enter your prompt here...")
responseOutput := widget.NewMultiLineEntry()
responseOutput.Disable()
// Generate button
generateBtn := widget.NewButtonWithIcon("Generate", theme.MailForwardIcon(), func() {
if currentModel == nil {
dialog.ShowInformation("No Model Selected", "Please select a model first.", w)
return
}
prompt := promptInput.Text
if strings.TrimSpace(prompt) == "" {
dialog.ShowInformation("Empty Prompt", "Please enter a prompt.", w)
return
}
// Parse max tokens
var maxTokens int
fmt.Sscanf(maxTokensEntry.Text, "%d", &maxTokens)
if maxTokens <= 0 {
maxTokens = 256
}
// Disable button during generation
generateBtn.Disable()
responseOutput.SetText("Generating...")
// Generate in a goroutine to keep UI responsive
go func() {
response, err := currentModel.Generate(
prompt,
maxTokens,
float32(tempSlider.Value),
float32(topPSlider.Value),
)
if err != nil {
responseOutput.SetText(fmt.Sprintf("Error: %v", err))
} else {
responseOutput.SetText(strings.TrimPrefix(response, prompt))
}
generateBtn.Enable()
}()
})
// Layout
modelBox := container.NewVBox(
widget.NewLabel("Select Model:"),
modelSelect,
)
paramsBox := container.NewVBox(
widget.NewLabel("Parameters:"),
container.NewGridWithColumns(2,
widget.NewLabel("Temperature:"),
tempSlider,
widget.NewLabel("Top-P:"),
topPSlider,
widget.NewLabel("Max Tokens:"),
maxTokensEntry,
),
)
inputBox := container.NewVBox(
widget.NewLabel("Prompt:"),
container.NewBorder(nil, nil, nil, nil, promptInput),
)
outputBox := container.NewVBox(
widget.NewLabel("Response:"),
container.NewBorder(nil, nil, nil, nil, responseOutput),
)
controlsBox := container.NewVBox(
generateBtn,
)
// Main layout
content := container.NewVBox(
modelBox,
paramsBox,
container.NewHSplit(
inputBox,
outputBox,
),
controlsBox,
)
w.SetContent(content)
w.ShowAndRun()
}
The GUI application provides a more user-friendly interface for interacting with LLMs. It includes a dropdown for model selection, sliders for adjusting generation parameters, and separate areas for entering prompts and displaying responses. The application uses goroutines to keep the UI responsive during model loading and text generation.
Performance Optimization
To ensure GoLlama runs efficiently, we should implement performance optimizations:
package optimization
import (
"runtime"
"sync"
"github.com/<GitHubUserName>/gollama/core"
)
type BatchProcessor struct {
model *core.LlamaModel
batchSize int
numWorkers int
workQueue chan string
resultQueue chan string
wg sync.WaitGroup
}
func NewBatchProcessor(model *core.LlamaModel, batchSize, numWorkers int) *BatchProcessor {
if numWorkers <= 0 {
numWorkers = runtime.NumCPU()
}
return &BatchProcessor{
model: model,
batchSize: batchSize,
numWorkers: numWorkers,
workQueue: make(chan string, batchSize),
resultQueue: make(chan string, batchSize),
}
}
func (bp *BatchProcessor) Start() {
// Start worker goroutines
for i := 0; i < bp.numWorkers; i++ {
bp.wg.Add(1)
go bp.worker()
}
}
func (bp *BatchProcessor) Stop() {
close(bp.workQueue)
bp.wg.Wait()
close(bp.resultQueue)
}
func (bp *BatchProcessor) worker() {
defer bp.wg.Done()
for prompt := range bp.workQueue {
// Process the prompt
response, err := bp.model.Generate(
prompt,
256, // Default max tokens
0.7, // Default temperature
0.9, // Default top-p
)
if err != nil {
bp.resultQueue <- "Error: " + err.Error()
} else {
bp.resultQueue <- response
}
}
}
func (bp *BatchProcessor) Submit(prompt string) {
bp.workQueue <- prompt
}
func (bp *BatchProcessor) Results() <-chan string {
return bp.resultQueue
}
// Memory optimization
func OptimizeMemoryUsage(model *core.LlamaModel) {
// Force garbage collection
runtime.GC()
// Set memory limit for the model context
model.SetMemoryLimit(4 * 1024 * 1024 * 1024) // 4GB limit
}
// CPU optimization
func OptimizeCPUUsage(model *core.LlamaModel) {
// Set number of threads for computation
numThreads := runtime.NumCPU()
model.SetThreads(numThreads)
// Enable/disable specific CPU features
model.EnableAVX(true)
model.EnableAVX2(true)
model.EnableFMA(true)
// Set thread affinity if needed
// This is platform-specific and would require additional implementation
}
// GPU optimization (if available)
func OptimizeGPUUsage(model *core.LlamaModel) {
// Check if CUDA is available
if model.IsCUDAAvailable() {
// Enable CUDA
model.EnableCUDA(true)
// Set CUDA device
model.SetCUDADevice(0)
// Set batch size for GPU processing
model.SetCUDABatchSize(512)
}
}
The performance optimization module provides functions for optimizing CPU and memory usage, as well as GPU acceleration if available. It also includes a batch processor that can process multiple prompts concurrently using a pool of worker goroutines.
Conclusion
GoLlama provides a comprehensive framework for working with LLMs in Go, leveraging the efficient llama.cpp library through CGO bindings. It offers functionality for inference, fine-tuning, quantization, and even training new models, all with the performance benefits of Go and the optimized C++ backend.
The application can be used as either a console tool or a GUI application, making it accessible to a wide range of users. With its modular architecture, GoLlama can be extended with additional features and optimizations as needed.
By providing local LLM capabilities, GoLlama enables privacy-preserving AI applications that don't require sending data to external services. This makes it suitable for sensitive applications in industries like healthcare, finance, and legal services.
As the field of AI and LLMs continues to evolve, GoLlama can serve as a foundation for building increasingly sophisticated applications that leverage the power of language models while maintaining control over data and computation.
Addendum
If you prefer using Apple Silicon hardware, you can add the following coding block right after the OptimizeGPUUsage function.
// Apple MPS optimization
func OptimizeMPSUsage(model *core.LlamaModel) {
// Check if MPS is available (macOS with Apple Silicon or compatible AMD GPU)
if model.IsMPSAvailable() {
// Enable Metal Performance Shaders
model.EnableMPS(true)
// Configure MPS memory allocation strategy
// Conservative: Allocates memory as needed, may be slower but uses less memory
// Aggressive: Pre-allocates more memory for better performance
model.SetMPSMemoryStrategy("aggressive")
// Set batch size for MPS processing
model.SetMPSBatchSize(128)
// Enable memory pooling to reduce allocation overhead
model.EnableMPSMemoryPooling(true)
}
}
// MPS-specific tensor operations
func ConfigureMPSTensorOps(model *core.LlamaModel) {
if model.IsMPSAvailable() {
// Enable specialized tensor operations for Apple Silicon
model.EnableMPSTensorCores(true)
// Configure precision (options: float16, float32, bfloat16)
// float16 is faster but less precise
model.SetMPSPrecision("float16")
// Enable Apple Neural Engine if available (M1/M2 chips)
if model.IsANEAvailable() {
model.EnableANE(true)
// Configure which operations should be offloaded to ANE
model.SetANEOperations([]string{"matmul", "attention"})
}
}
}
// MPS memory management
func OptimizeMPSMemory(model *core.LlamaModel) {
if model.IsMPSAvailable() {
// Set maximum memory usage (in bytes)
// This helps prevent out-of-memory errors on devices with limited RAM
model.SetMPSMaxMemory(8 * 1024 * 1024 * 1024) // 8GB limit
// Enable memory compression for weight matrices
model.EnableMPSMemoryCompression(true)
// Configure garbage collection behavior for MPS buffers
model.SetMPSGCStrategy("deferred") // Options: aggressive, deferred, manual
// Set up manual purging of unused MPS resources
model.EnableMPSPeriodicPurge(true, 60) // Purge unused resources every 60 seconds
}
}
// MPS-specific model optimization
func OptimizeMPSModelPerformance(model *core.LlamaModel) {
if model.IsMPSAvailable() {
// Optimize model graph for MPS execution
model.OptimizeMPSGraph()
// Enable MPS-specific kernel fusion optimizations
model.EnableMPSKernelFusion(true)
// Configure MPS command queue depth
// Higher values can improve throughput at the cost of latency
model.SetMPSCommandQueueDepth(3)
// Enable asynchronous command execution
model.EnableMPSAsyncExecution(true)
// Configure MPS-specific attention implementation
model.SetMPSAttentionImplementation("flash") // Options: standard, flash
}
}
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