🐫 llama-cli

llama-cli is a straightforward golang CLI interface for llama.cpp, providing a simple API and a command line interface that allows text generation using a GPT-based model like llama directly from the terminal.

Container images

The llama-cli container images come preloaded with the alpaca.cpp model, enabling you to start making predictions immediately! To begin, run:

docker run -ti --rm quay.io/go-skynet/llama-cli:latest  --instruction "What's an alpaca?" --topk 10000

You will receive a response like the following:

An alpaca is a member of the South American Camelid family, which includes the llama, guanaco and vicuña. It is a domesticated species that originates from the Andes mountain range in South America. Alpacas are used in the textile industry for their fleece, which is much softer than wool. Alpacas are also used for meat, milk, and fiber.

Basic usage

To use llama-cli, specify a pre-trained GPT-based model, an input text, and an instruction for text generation. llama-cli takes the following arguments:

llama-cli --model <model_path> --instruction <instruction> [--input <input>] [--template <template_path>] [--tokens <num_tokens>] [--threads <num_threads>] [--temperature <temperature>] [--topp <top_p>] [--topk <top_k>]

Parameter	Environment Variable	Default Value	Description
template	TEMPLATE		A file containing a template for output formatting (optional).
instruction	INSTRUCTION		Input prompt text or instruction. "-" for STDIN.
input	INPUT	-	Path to text or "-" for STDIN.
model	MODEL_PATH		The path to the pre-trained GPT-based model.
tokens	TOKENS	128	The maximum number of tokens to generate.
threads	THREADS	NumCPU()	The number of threads to use for text generation.
temperature	TEMPERATURE	0.95	Sampling temperature for model output.
top_p	TOP_P	0.85	The cumulative probability for top-p sampling.
top_k	TOP_K	20	The number of top-k tokens to consider for text generation.

Here's an example of using llama-cli:

llama-cli --model ~/ggml-alpaca-7b-q4.bin --instruction "What's an alpaca?"

This will generate text based on the given model and instruction.

Advanced usage

llama-cli also provides an API for running text generation as a service. You can start the API server using the following command:

llama-cli api --model <model_path> [--address <address>] [--threads <num_threads>]

The API takes takes the following arguments:

Parameter	Environment Variable	Default Value	Description
model	MODEL_PATH		The path to the pre-trained GPT-based model.
threads	THREADS	CPU cores	The number of threads to use for text generation.
address	ADDRESS	:8080	The address and port to listen on.

And you'll see:

┌───────────────────────────────────────────────────┐ 
│                   Fiber v2.42.0                   │ 
│               http://127.0.0.1:8080               │ 
│       (bound on host 0.0.0.0 and port 8080)       │ 
│                                                   │ 
│ Handlers ............. 1  Processes ........... 1 │ 
│ Prefork ....... Disabled  PID ................. 1 │ 
└───────────────────────────────────────────────────┘ 

Once the server is running, you can make requests to it using HTTP. For example, to generate text based on an instruction, you can send a POST request to the /predict endpoint with the instruction as the request body:

curl --location --request POST 'http://localhost:8080/predict' --header 'Content-Type: application/json' --data-raw '{
    "text": "What is an alpaca?",
    "topP": 0.8,
    "topK": 50,
    "temperature": 0.7,
    "tokens": 100
}'

Example of starting the API with docker:

docker run -ti --rm quay.io/go-skynet/llama-cli:latest api

Kubernetes

You can run the API directly in Kubernetes:

kubectl apply -f https://raw.githubusercontent.com/go-skynet/llama-cli/master/kubernetes/deployment.yaml