🐫 llama-cli

llama-cli is a straightforward, drop-in replacement API compatible with OpenAI for local CPU inferencing, based on llama.cpp, gpt4all and ggml, including support GPT4ALL-J which is Apache 2.0 Licensed and can be used for commercial purposes.

• OpenAI compatible API
• Supports multiple-models
• Once loaded the first time, it keep models loaded in memory for faster inference
• Provides a simple command line interface that allows text generation directly from the terminal
• Support for prompt templates
• Doesn't shell-out, but uses C bindings for a faster inference and better performance. Uses go-llama.cpp and go-gpt4all-j.cpp.

Model compatibility

It is compatible with the models supported by llama.cpp and also GPT4ALL-J.

Note: You might need to convert older models to the new format, see here for instance to run gpt4all.

Usage

The easiest way to run llama-cli is by using docker-compose:

git clone https://github.com/go-skynet/llama-cli
cd llama-cli

# copy your models to models/
cp your-model.bin models/

# (optional) Edit the .env file to set things like context size and threads
# vim .env

# start with docker-compose
docker compose up -d --build

# Now API is accessible at localhost:8080
curl http://localhost:8080/v1/models
# {"object":"list","data":[{"id":"your-model.bin","object":"model"}]}

curl http://localhost:8080/v1/completions -H "Content-Type: application/json" -d '{
     "model": "your-model.bin",            
     "prompt": "A long time ago in a galaxy far, far away",
     "temperature": 0.7
   }'

Note: The API doesn't inject a default prompt for talking to the model, while the CLI does. You have to use a prompt similar to what's described in the standford-alpaca docs: https://github.com/tatsu-lab/stanford_alpaca#data-release.

You can use a default template for every model present in your model path, by creating a corresponding file with the .tmpl suffix next to your model. For instance, if the model is called foo.bin, you can create a sibiling file, foo.bin.tmpl which will be used as a default prompt, for instance this can be used with alpaca:

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{{.Input}}

### Response:

See the prompt-templates directory in this repository for templates for most popular models.

Container images

llama-cli comes by default as a container image. You can check out all the available images with corresponding tags here

To begin, run:

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

Where --model is the path of the model you want to use.

Note: you need to mount a volume to the docker container in order to load a model, for instance:

# assuming your model is in /path/to/your/models/foo.bin
docker run -v /path/to/your/models:/models -ti --rm quay.io/go-skynet/llama-cli:latest  --instruction "What's an alpaca?" --topk 10000 --model /models/foo.bin

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 when running from the CLI:

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		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. ( values between 0.1 and 1.0 )
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.
context-size	CONTEXT_SIZE	512	Default token context size.

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.

API

llama-cli also provides an API for running text generation as a service. The models once loaded the first time will be kept in memory.

Example of starting the API with docker:

docker run -p 8080:8080 -ti --rm quay.io/go-skynet/llama-cli:latest api --models-path /path/to/models --context-size 700 --threads 4

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 │ 
└───────────────────────────────────────────────────┘ 

Note: Models have to end up with .bin.

You can control the API server options with command line arguments:

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

The API takes takes the following:

Parameter	Environment Variable	Default Value	Description
models-path	MODELS_PATH		The path where you have models (ending with .bin).
threads	THREADS	CPU cores	The number of threads to use for text generation.
address	ADDRESS	:8080	The address and port to listen on.
context-size	CONTEXT_SIZE	512	Default token context size.

Once the server is running, you can start making requests to it using HTTP, using the OpenAI API.

Supported OpenAI API endpoints

You can check out the OpenAI API reference.

Following the list of endpoints/parameters supported.

Chat completions

For example, to generate a chat completion, you can send a POST request to the /v1/chat/completions endpoint with the instruction as the request body:

curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
     "model": "ggml-koala-7b-model-q4_0-r2.bin",
     "messages": [{"role": "user", "content": "Say this is a test!"}],
     "temperature": 0.7
   }'

Available additional parameters: top_p, top_k, max_tokens

Completions

For example, to generate a comletion, you can send a POST request to the /v1/completions endpoint with the instruction as the request body:

curl http://localhost:8080/v1/completions -H "Content-Type: application/json" -d '{
     "model": "ggml-koala-7b-model-q4_0-r2.bin",
     "prompt": "A long time ago in a galaxy far, far away",
     "temperature": 0.7
   }'

Available additional parameters: top_p, top_k, max_tokens

List models

You can list all the models available with:

curl http://localhost:8080/v1/models

Using other models

gpt4all (https://github.com/nomic-ai/gpt4all) works as well, however the original model needs to be converted (same applies for old alpaca models, too):

wget -O tokenizer.model https://huggingface.co/decapoda-research/llama-30b-hf/resolve/main/tokenizer.model
mkdir models
cp gpt4all.. models/
git clone https://gist.github.com/eiz/828bddec6162a023114ce19146cb2b82
pip install sentencepiece
python 828bddec6162a023114ce19146cb2b82/gistfile1.txt models tokenizer.model
# There will be a new model with the ".tmp" extension, you have to use that one!

Windows compatibility

It should work, however you need to make sure you give enough resources to the container. See https://github.com/go-skynet/llama-cli/issues/2

Kubernetes

You can run the API directly in Kubernetes:

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

Build locally

Pre-built images might fit well for most of the modern hardware, however you can and might need to build the images manually.

In order to build the llama-cli container image locally you can use docker:

# build the image as "alpaca-image"
docker build -t llama-cli .
docker run llama-cli --instruction "What's an alpaca?"

Or build the binary with:

# build the image as "alpaca-image"
docker run --privileged -v /var/run/docker.sock:/var/run/docker.sock --rm -t -v "$(pwd)":/workspace -v earthly-tmp:/tmp/earthly:rw earthly/earthly:v0.7.2 +build
# run the binary
./llama-cli --instruction "What's an alpaca?"

Short-term roadmap

• Mimic OpenAI API (https://github.com/go-skynet/llama-cli/issues/10)
• Binary releases (https://github.com/go-skynet/llama-cli/issues/6)
• Upstream our golang bindings to llama.cpp (https://github.com/ggerganov/llama.cpp/issues/351)
• Multi-model support
• Have a webUI!

License

MIT

Acknowledgements

• llama.cpp
• https://github.com/tatsu-lab/stanford_alpaca
• https://github.com/cornelk/llama-go for the initial ideas
• https://github.com/antimatter15/alpaca.cpp for the light model version (this is compatible and tested only with that checkpoint model!)