Workflow:Ggml org Llama cpp Text Generation
| Knowledge Sources | |
|---|---|
| Domains | LLMs, Inference, Text_Generation |
| Last Updated | 2026-02-14 22:00 GMT |
Overview
End-to-end process for generating text completions from a prompt using a GGUF language model with the llama.cpp C API.
Description
This workflow demonstrates the fundamental llama.cpp inference pipeline: loading a GGUF model, tokenizing a text prompt, processing it through the model, and iteratively sampling new tokens to produce a text completion. It represents the simplest possible use of the llama.cpp library and is the foundation upon which all other inference workflows (chat, server, speculative decoding) are built. The process uses a batched decode loop where each generated token is fed back as input to predict the next, continuing until an end-of-generation token is produced or a maximum token count is reached.
Usage
Execute this workflow when you need to perform basic text completion (non-conversational) from a GGUF model. This is appropriate for code completion, text continuation, creative writing prompts, or any scenario where a single prompt needs a generated continuation without multi-turn conversation management.
Execution Steps
Step 1: Load Compute Backends
Initialize the ggml backend system by loading all available compute backends (CPU, CUDA, Metal, Vulkan, etc.). This step discovers the available hardware accelerators and prepares them for use during model loading and inference.
Key considerations:
- Backends are discovered automatically based on compiled-in support
- The number of GPU layers to offload is configurable
- CPU fallback is always available
Step 2: Load the Model
Load the GGUF model file from disk using the model loading API. This parses the GGUF metadata, allocates memory for model weights, maps tensors to compute devices, and optionally offloads layers to GPU memory.
Key considerations:
- GPU layer offloading (n_gpu_layers) controls memory vs speed trade-off
- Memory-mapped I/O (mmap) is used by default for efficient loading
- Model architecture is auto-detected from GGUF metadata
- Split models (multiple GGUF shards) are supported transparently
Step 3: Create Inference Context
Initialize an inference context from the loaded model with parameters controlling context window size, batch size, and threading configuration. The context manages the KV cache, computation graphs, and decoding state.
Key considerations:
- Context size (n_ctx) determines the maximum sequence length
- Batch size affects throughput for prompt processing
- The number of threads controls CPU parallelism
- KV cache memory scales with context size and model dimensions
Step 4: Tokenize the Prompt
Convert the input text prompt into a sequence of token IDs using the model's vocabulary. The tokenizer type (BPE, SentencePiece, or WordPiece) is determined by the model's metadata and handles special tokens, unicode text, and subword splitting.
Key considerations:
- Add BOS (beginning of sequence) token if required by the model
- Check that the prompt fits within the configured context size
- Special tokens are handled according to model-specific rules
Step 5: Process the Prompt
Feed the tokenized prompt into the model by creating a batch containing all prompt tokens and running a decode operation. This prefills the KV cache with the prompt's attention state, preparing the model for generation.
Key considerations:
- Long prompts may be processed in multiple batch chunks
- Only the logits for the last token position are needed for generation
- Prompt processing is typically faster than per-token generation due to parallelism
Step 6: Generate Tokens
Enter the main generation loop: sample a token from the logits distribution, output it, add it to a new single-token batch, and decode again to produce the next set of logits. Continue until an end-of-generation token is sampled or the maximum token count is reached.
Key considerations:
- Sampling strategy (greedy, top-k, top-p, temperature) affects output diversity
- The sampler chain applies multiple sampling steps in sequence
- Each decode step extends the KV cache by one position
- Tokens are detokenized and printed incrementally for streaming output