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191 lines
7.7 KiB
191 lines
7.7 KiB
## :camel: llama-cli
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llama-cli is a straightforward golang CLI interface for [llama.cpp](https://github.com/ggerganov/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.
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## Container images
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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:v0.3 --instruction "What's an alpaca?" --topk 10000
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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_PATH | | 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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| alpaca | ALPACA | true | Set to true for alpaca models. |
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| gpt4all | GPT4ALL | false | Set to true for gpt4all models. |
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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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## Advanced usage
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`llama-cli` also provides an API for running text generation as a service. The model will be pre-loaded and 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:v0.3 api --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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You can control the API server options with command line arguments:
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```
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llama-cli api --model <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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| model | MODEL_PATH | | The path to the pre-trained GPT-based model. |
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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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| alpaca | ALPACA | true | Set to true for alpaca models. |
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| gpt4all | GPT4ALL | false | Set to true for gpt4all models. |
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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:
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```
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curl --location --request POST 'http://localhost:8080/predict' --header 'Content-Type: application/json' --data-raw '{
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"text": "What is an alpaca?",
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"topP": 0.8,
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"topK": 50,
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"temperature": 0.7,
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"tokens": 100
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}'
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```
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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:
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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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{instruction}
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### Response:
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```
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## Using other models
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You can specify a model binary to be used for inference with `--model`.
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13B and 30B alpaca models are known to work:
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```
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# Download the model image, extract the model
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# Use the model with llama-cli
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docker run -v $PWD:/models -p 8080:8080 -ti --rm quay.io/go-skynet/llama-cli:v0.3-lite api --model /models/model.bin
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```
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gpt4all (https://github.com/nomic-ai/gpt4all) works as well, however the original model needs to be converted:
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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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```
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### Golang client API
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The `llama-cli` codebase has also a small client in go that can be used alongside with the api:
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```golang
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package main
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import (
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"fmt"
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client "github.com/go-skynet/llama-cli/client"
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)
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func main() {
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cli := client.NewClient("http://ip:30007")
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out, err := cli.Predict("What's an alpaca?")
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if err != nil {
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panic(err)
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}
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fmt.Println(out)
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}
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```
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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 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 +image --IMAGE=alpaca-image
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# run the image
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docker run alpaca-image --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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