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* llama : refactor k-shift implementation ggml-ci * llama : rename llama_kv_cache_seq_shift to llama_kv_cache_seq_add * llama : cont k-shift refactoring + normalize type names ggml-ci * minor : fix MPI builds * llama : reuse n_rot from the build context ggml-ci * llama : revert enum name changes from this PR ggml-ci * llama : update llama_rope_type * llama : add comment about rope values * llama : fix build * passkey : apply kv cache updates explicitly ggml-ci * llama : change name to llama_kv_cache_update() * llama : add llama_kv_cache_seq_pos_max() * passkey : fix llama_kv_cache_seq_pos_max() usage * llama : some llama_kv_cell simplifications * llama : add llama_kv_cache_compress (EXPERIMENTAL) * llama : add alternative KV cache merging (EXPERIMENTAL) * llama : add llama_kv_cache_defrag * llama : comments * llama : remove llama_kv_cache_compress will add in a separate PR ggml-ci * llama : defragment via non-overlapping moves * llama : ggml_graph based defrag implementation ggml-ci * llama : switch the loop order in build_defrag * llama : add comments |
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| .. | ||
| CMakeLists.txt | ||
| infill.cpp | ||
| README.md | ||
llama.cpp/example/infill
This example shows how to use the infill mode with Code Llama models supporting infill mode. Currently the 7B and 13B models support infill mode.
Infill supports most of the options available in the main example.
For further information have a look at the main README.md in llama.cpp/example/main/README.md
Common Options
In this section, we cover the most commonly used options for running the infill program with the LLaMA models:
-m FNAME, --model FNAME: Specify the path to the LLaMA model file (e.g.,models/7B/ggml-model.bin).-i, --interactive: Run the program in interactive mode, allowing you to provide input directly and receive real-time responses.-n N, --n-predict N: Set the number of tokens to predict when generating text. Adjusting this value can influence the length of the generated text.-c N, --ctx-size N: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference.
Input Prompts
The infill program provides several ways to interact with the LLaMA models using input prompts:
--in-prefix PROMPT_BEFORE_CURSOR: Provide the prefix directly as a command-line option.--in-suffix PROMPT_AFTER_CURSOR: Provide the suffix directly as a command-line option.--interactive-first: Run the program in interactive mode and wait for input right away. (More on this below.)
Interaction
The infill program offers a seamless way to interact with LLaMA models, allowing users to receive real-time infill suggestions. The interactive mode can be triggered using --interactive, and --interactive-first
Interaction Options
-i, --interactive: Run the program in interactive mode, allowing users to get real time code suggestions from model.--interactive-first: Run the program in interactive mode and immediately wait for user input before starting the text generation.--color: Enable colorized output to differentiate visually distinguishing between prompts, user input, and generated text.
Example
./infill -t 10 -ngl 0 -m models/codellama-13b.Q5_K_S.gguf -c 4096 --temp 0.7 --repeat_penalty 1.1 -n 20 --in-prefix "def helloworld():\n print(\"hell" --in-suffix "\n print(\"goodbye world\")\n "