Ettore Di Giacinto
c4e94c88d7
|
2 years ago | |
---|---|---|
.github/workflows | 2 years ago | |
api | 2 years ago | |
client | 2 years ago | |
kubernetes | 2 years ago | |
models | 2 years ago | |
pkg/model | 2 years ago | |
.dockerignore | 2 years ago | |
.env | 2 years ago | |
.gitignore | 2 years ago | |
.goreleaser.yaml | 2 years ago | |
Dockerfile | 2 years ago | |
Earthfile | 2 years ago | |
LICENSE | 2 years ago | |
README.md | 2 years ago | |
docker-compose.yaml | 2 years ago | |
go.mod | 2 years ago | |
go.sum | 2 years ago | |
main.go | 2 years ago |
README.md
π« llama-cli
llama-cli is a straightforward golang CLI interface and API compatible with OpenAI for llama.cpp, it supports multiple-models and also provides a simple command line interface that allows text generation using a GPT-based model like llama directly from the terminal.
It is compatible with the models supported by llama.cpp
. You might need to convert older models to the new format, see here for instance to run gpt4all
.
llama-cli
doesn't shell-out, it uses https://github.com/go-skynet/go-llama.cpp, which is a golang binding of llama.cpp.
Usage
You can use 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 the number of concurrent threads used for inference
# echo "THREADS=14" > .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: You can use a use a default template for every model 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:
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 vicuΓ±a. 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_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. ( 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:
βββββββββββββββββββββββββββββββββββββββββββββββββββββ
β Fiber v2.42.0 β
β http://127.0.0.1:8080 β
β (bound on host 0.0.0.0 and port 8080) β
β β
β Handlers ............. 1 Processes ........... 1 β
β Prefork ....... Disabled PID ................. 1 β
βββββββββββββββββββββββββββββββββββββββββββββββββββββ
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
Web interface
There is also available a simple web interface (for instance, http://localhost:8080/) which can be used as a playground.
Note: The API doesn't inject a template for talking to the instance, 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, for instance:
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Response:
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!
Golang client API
The llama-cli
codebase has also a small client in go that can be used alongside with the api:
package main
import (
"fmt"
client "github.com/go-skynet/llama-cli/client"
)
func main() {
cli := client.NewClient("http://ip:port")
out, err := cli.Predict("What's an alpaca?")
if err != nil {
panic(err)
}
fmt.Println(out)
}
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!)