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264 lines
11 KiB
Markdown
264 lines
11 KiB
Markdown
## :camel: llama-cli
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llama-cli is a straightforward, drop-in replacement API compatible with OpenAI for local CPU inferencing, based on [llama.cpp](https://github.com/ggerganov/llama.cpp), [gpt4all](https://github.com/nomic-ai/gpt4all) and [ggml](https://github.com/ggerganov/ggml), including support GPT4ALL-J which is Apache 2.0 Licensed and can be used for commercial purposes.
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- OpenAI compatible API
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- Supports multiple-models
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- Once loaded the first time, it keep models loaded in memory for faster inference
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- Provides a simple command line interface that allows text generation directly from the terminal
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- Support for prompt templates
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- Doesn't shell-out, but uses C bindings for a faster inference and better performance. Uses [go-llama.cpp](https://github.com/go-skynet/go-llama.cpp) and [go-gpt4all-j.cpp](https://github.com/go-skynet/go-gpt4all-j.cpp).
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## Model compatibility
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It is compatible with the models supported by [llama.cpp](https://github.com/ggerganov/llama.cpp) and also [GPT4ALL-J](https://github.com/nomic-ai/gpt4all).
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Note: You might need to convert older models to the new format, see [here](https://github.com/ggerganov/llama.cpp#using-gpt4all) for instance to run `gpt4all`.
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## Usage
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The easiest way to run llama-cli is by using `docker-compose`:
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```bash
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git clone https://github.com/go-skynet/llama-cli
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cd llama-cli
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# copy your models to models/
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cp your-model.bin models/
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# (optional) Edit the .env file to set things like context size and threads
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# vim .env
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# start with docker-compose
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docker compose up -d --build
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# Now API is accessible at localhost:8080
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curl http://localhost:8080/v1/models
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# {"object":"list","data":[{"id":"your-model.bin","object":"model"}]}
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curl http://localhost:8080/v1/completions -H "Content-Type: application/json" -d '{
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"model": "your-model.bin",
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"prompt": "A long time ago in a galaxy far, far away",
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"temperature": 0.7
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}'
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```
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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.
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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:
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```
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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{{.Input}}
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### Response:
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```
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See the [prompt-templates](https://github.com/go-skynet/llama-cli/tree/master/prompt-templates) directory in this repository for templates for most popular models.
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## Container images
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`llama-cli` comes by default as a container image. You can check out all the available images with corresponding tags [here](https://quay.io/repository/go-skynet/llama-cli?tab=tags&tag=latest)
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To begin, run:
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```
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docker run -ti --rm quay.io/go-skynet/llama-cli:latest --instruction "What's an alpaca?" --topk 10000 --model ...
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```
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Where `--model` is the path of the model you want to use.
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Note: you need to mount a volume to the docker container in order to load a model, for instance:
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```
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# assuming your model is in /path/to/your/models/foo.bin
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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
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```
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You will receive a response like the following:
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```
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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.
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```
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## Basic usage
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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:
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```
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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>]
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```
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| Parameter | Environment Variable | Default Value | Description |
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| ------------ | -------------------- | ------------- | -------------------------------------- |
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| template | TEMPLATE | | A file containing a template for output formatting (optional). |
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| instruction | INSTRUCTION | | Input prompt text or instruction. "-" for STDIN. |
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| input | INPUT | - | Path to text or "-" for STDIN. |
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| model | MODEL | | The path to the pre-trained GPT-based model. |
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| tokens | TOKENS | 128 | The maximum number of tokens to generate. |
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| threads | THREADS | NumCPU() | The number of threads to use for text generation. |
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| temperature | TEMPERATURE | 0.95 | Sampling temperature for model output. ( values between `0.1` and `1.0` ) |
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| top_p | TOP_P | 0.85 | The cumulative probability for top-p sampling. |
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| top_k | TOP_K | 20 | The number of top-k tokens to consider for text generation. |
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| context-size | CONTEXT_SIZE | 512 | Default token context size. |
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Here's an example of using `llama-cli`:
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```
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llama-cli --model ~/ggml-alpaca-7b-q4.bin --instruction "What's an alpaca?"
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```
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This will generate text based on the given model and instruction.
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## API
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`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.
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Example of starting the API with `docker`:
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```bash
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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
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```
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And you'll see:
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```
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┌───────────────────────────────────────────────────┐
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│ Fiber v2.42.0 │
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│ http://127.0.0.1:8080 │
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│ (bound on host 0.0.0.0 and port 8080) │
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│ │
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│ Handlers ............. 1 Processes ........... 1 │
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│ Prefork ....... Disabled PID ................. 1 │
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└───────────────────────────────────────────────────┘
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```
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Note: Models have to end up with `.bin`.
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You can control the API server options with command line arguments:
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```
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llama-cli api --models-path <model_path> [--address <address>] [--threads <num_threads>]
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```
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The API takes takes the following:
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| Parameter | Environment Variable | Default Value | Description |
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| ------------ | -------------------- | ------------- | -------------------------------------- |
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| models-path | MODELS_PATH | | The path where you have models (ending with `.bin`). |
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| threads | THREADS | CPU cores | The number of threads to use for text generation. |
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| address | ADDRESS | :8080 | The address and port to listen on. |
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| context-size | CONTEXT_SIZE | 512 | Default token context size. |
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Once the server is running, you can start making requests to it using HTTP, using the OpenAI API.
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### Supported OpenAI API endpoints
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You can check out the [OpenAI API reference](https://platform.openai.com/docs/api-reference/chat/create).
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Following the list of endpoints/parameters supported.
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#### Chat completions
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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:
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```
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curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
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"model": "ggml-koala-7b-model-q4_0-r2.bin",
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"messages": [{"role": "user", "content": "Say this is a test!"}],
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"temperature": 0.7
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}'
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```
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Available additional parameters: `top_p`, `top_k`, `max_tokens`
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#### Completions
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For example, to generate a comletion, you can send a POST request to the `/v1/completions` endpoint with the instruction as the request body:
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```
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curl http://localhost:8080/v1/completions -H "Content-Type: application/json" -d '{
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"model": "ggml-koala-7b-model-q4_0-r2.bin",
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"prompt": "A long time ago in a galaxy far, far away",
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"temperature": 0.7
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}'
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```
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Available additional parameters: `top_p`, `top_k`, `max_tokens`
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#### List models
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You can list all the models available with:
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```
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curl http://localhost:8080/v1/models
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```
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## Using other models
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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):
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```bash
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wget -O tokenizer.model https://huggingface.co/decapoda-research/llama-30b-hf/resolve/main/tokenizer.model
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mkdir models
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cp gpt4all.. models/
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git clone https://gist.github.com/eiz/828bddec6162a023114ce19146cb2b82
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pip install sentencepiece
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python 828bddec6162a023114ce19146cb2b82/gistfile1.txt models tokenizer.model
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# There will be a new model with the ".tmp" extension, you have to use that one!
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```
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### Windows compatibility
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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
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### Kubernetes
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You can run the API directly in Kubernetes:
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```bash
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kubectl apply -f https://raw.githubusercontent.com/go-skynet/llama-cli/master/kubernetes/deployment.yaml
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```
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### Build locally
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Pre-built images might fit well for most of the modern hardware, however you can and might need to build the images manually.
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In order to build the `llama-cli` container image locally you can use `docker`:
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```
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# build the image as "alpaca-image"
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docker build -t llama-cli .
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docker run llama-cli --instruction "What's an alpaca?"
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```
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Or build the binary with:
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```
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# build the image as "alpaca-image"
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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
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# run the binary
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./llama-cli --instruction "What's an alpaca?"
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```
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## Short-term roadmap
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- [x] Mimic OpenAI API (https://github.com/go-skynet/llama-cli/issues/10)
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- Binary releases (https://github.com/go-skynet/llama-cli/issues/6)
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- Upstream our golang bindings to llama.cpp (https://github.com/ggerganov/llama.cpp/issues/351)
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- [x] Multi-model support
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- Have a webUI!
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## License
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MIT
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## Acknowledgements
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- [llama.cpp](https://github.com/ggerganov/llama.cpp)
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- https://github.com/tatsu-lab/stanford_alpaca
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- https://github.com/cornelk/llama-go for the initial ideas
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- https://github.com/antimatter15/alpaca.cpp for the light model version (this is compatible and tested only with that checkpoint model!)
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