---
title: "Basic Text Generation"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Basic Text Generation}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)
```
This tutorial covers the lower-level API for full control over text generation. While `quick_llama()` is convenient for simple tasks, the core functions give you fine-grained control over model loading, context management, and generation parameters.

## The Core Workflow

The recommended workflow consists of four steps:

1. **`model_load()`** - Load the model into memory once
2. **`context_create()`** - Create a reusable context for inference
3. **`apply_chat_template()`** - Format prompts correctly for the model
4. **`generate()`** - Generate text from the context

## Step 1: Loading a Model

Use `model_load()` to load a GGUF model into memory:

```{r}
library(localLLM)

# Load the default model
model <- model_load("Llama-3.2-3B-Instruct-Q5_K_M.gguf")

# Or load from a URL (downloaded and cached automatically)
model <- model_load(
  "https://huggingface.co/unsloth/gemma-3-4b-it-qat-GGUF/resolve/main/gemma-3-4b-it-qat-Q5_K_M.gguf"
)

# With GPU acceleration (offload layers to GPU)
model <- model_load(
  "Llama-3.2-3B-Instruct-Q5_K_M.gguf",
  n_gpu_layers = 999  # Offload as many layers as possible
)
```

### Model Loading Options

| Parameter | Default | Description |
|-----------|---------|-------------|
| `model_path` | - | Path, URL, or cached model name |
| `n_gpu_layers` | 0 | Number of layers to offload to GPU |
| `use_mmap` | TRUE | Memory-map the model file |
| `use_mlock` | FALSE | Lock model in RAM (prevents swapping) |

## Step 2: Creating a Context

The context manages the inference state and memory allocation:

```{r}
# Create a context with default settings
ctx <- context_create(model)

# Create a context with custom settings
ctx <- context_create(
  model,
  n_ctx = 4096,      # Context window size (tokens)
  n_threads = 8,     # CPU threads for generation
  n_seq_max = 1      # Maximum parallel sequences
)
```

### Context Parameters

| Parameter | Default | Description |
|-----------|---------|-------------|
| `n_ctx` | 512 | Context window size in tokens |
| `n_threads` | auto | Number of CPU threads |
| `n_seq_max` | 1 | Max parallel sequences (for batch generation) |
| `verbosity` | 0 | Logging level (0=quiet, 3=verbose) |

The context window (`n_ctx`) determines how much text the model can "see" at once. Larger values allow longer conversations but use more memory.

## Step 3: Formatting Prompts with Chat Templates

Modern LLMs are trained on specific conversation formats. The `apply_chat_template()` function formats your messages correctly:

```{r}
# Define a conversation as a list of messages
messages <- list(
  list(role = "system", content = "You are a helpful R programming assistant."),
  list(role = "user", content = "How do I read a CSV file?")
)

# Apply the model's chat template
formatted_prompt <- apply_chat_template(model, messages)
cat(formatted_prompt)
```

```
#> <|begin_of_text|><|start_header_id|>system<|end_header_id|>
#>
#> You are a helpful R programming assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>
#>
#> How do I read a CSV file?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```

### Multi-Turn Conversations

You can include multiple turns in the conversation:

```{r}
messages <- list(
  list(role = "system", content = "You are a helpful assistant."),
  list(role = "user", content = "What is R?"),
  list(role = "assistant", content = "R is a programming language for statistical computing."),
  list(role = "user", content = "How do I install packages?")
)

formatted_prompt <- apply_chat_template(model, messages)
```

## Step 4: Generating Text

Use `generate()` to produce text from the formatted prompt:

```{r}
# Basic generation
output <- generate(ctx, formatted_prompt)
cat(output)
```

```
#> To read a CSV file in R, you can use the `read.csv()` function:
#>
#> ```r
#> data <- read.csv("your_file.csv")
#> ```
```

### Generation Parameters

```{r}
output <- generate(
  ctx,
  formatted_prompt,
  max_tokens = 200,        # Maximum tokens to generate
  temperature = 0.0,       # Creativity (0 = deterministic)
  top_k = 40,              # Consider top K tokens
  top_p = 1.0,             # Nucleus sampling threshold
  repeat_last_n = 0,       # Tokens to consider for repetition penalty
  penalty_repeat = 1.0,    # Repetition penalty (>1 discourages)
  seed = 1234              # Random seed for reproducibility
)
```

| Parameter | Default | Description |
|-----------|---------|-------------|
| `max_tokens` | 256 | Maximum tokens to generate |
| `temperature` | 0.0 | Sampling temperature (0 = greedy) |
| `top_k` | 40 | Top-K sampling |
| `top_p` | 1.0 | Nucleus sampling (1.0 = disabled) |
| `repeat_last_n` | 0 | Window for repetition penalty |
| `penalty_repeat` | 1.0 | Repetition penalty multiplier |
| `seed` | 1234 | Random seed |

## Complete Example

Here's a complete workflow putting it all together:

```{r}
library(localLLM)

# 1. Load model with GPU acceleration
model <- model_load(
  "Llama-3.2-3B-Instruct-Q5_K_M.gguf",
  n_gpu_layers = 999
)

# 2. Create context with appropriate size
ctx <- context_create(model, n_ctx = 4096)

# 3. Define conversation
messages <- list(
  list(
    role = "system",
    content = "You are a helpful R programming assistant who provides concise code examples."
  ),
  list(
    role = "user",
    content = "How do I create a bar plot in ggplot2?"
  )
)

# 4. Format prompt
formatted_prompt <- apply_chat_template(model, messages)

# 5. Generate response
output <- generate(
  ctx,
  formatted_prompt,
  max_tokens = 300,
  temperature = 0,
  seed = 42
)

cat(output)
```

```
#> Here's how to create a bar plot in ggplot2:
#>
#> ```r
#> library(ggplot2)
#>
#> # Sample data
#> df <- data.frame(
#>   category = c("A", "B", "C", "D"),
#>   value = c(25, 40, 30, 45)
#> )
#>
#> # Create bar plot
#> ggplot(df, aes(x = category, y = value)) +
#>   geom_bar(stat = "identity", fill = "steelblue") +
#>   theme_minimal() +
#>   labs(title = "Bar Plot Example", x = "Category", y = "Value")
#> ```
```

## Tokenization

For advanced use cases, you can work directly with tokens:

```{r}
# Convert text to tokens
tokens <- tokenize(model, "Hello, world!")
print(tokens)
```

```
#> [1] 9906 11  1695   0
```

```{r}
# Convert tokens back to text
text <- detokenize(model, tokens)
print(text)
```

```
#> [1] "Hello, world!"
```

## Tips and Best Practices

### 1. Reuse Models and Contexts

Loading a model is expensive. Load once and reuse:

```{r}
# Good: Load once, use many times
model <- model_load("model.gguf")
ctx <- context_create(model)

for (prompt in prompts) {
  result <- generate(ctx, prompt)
}

# Bad: Loading in a loop
for (prompt in prompts) {
  model <- model_load("model.gguf")  # Slow!
  ctx <- context_create(model)
  result <- generate(ctx, prompt)
}
```

### 2. Size Your Context Appropriately

Larger contexts use more memory. Match `n_ctx` to your needs:

```{r}
# For short Q&A
ctx <- context_create(model, n_ctx = 512)

# For longer conversations
ctx <- context_create(model, n_ctx = 4096)

# For document analysis
ctx <- context_create(model, n_ctx = 8192)
```

### 3. Use GPU When Available

GPU acceleration provides 5-10x speedup:

```{r}
# Check your hardware
hw <- hardware_profile()
print(hw$gpu)

# Enable GPU
model <- model_load("model.gguf", n_gpu_layers = 999)
```

## Next Steps

- **[Parallel Processing](tutorial-parallel-processing.html)**: Process multiple prompts efficiently
- **[Model Comparison](tutorial-model-comparison.html)**: Compare multiple models systematically
- **[Reproducible Output](reproducible-output.html)**: Ensure reproducible results