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Signed-off-by: Dimitris Karakasilis <dimitris@spectrocloud.com>
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  1. 13
      Dockerfile
  2. 5
      Earthfile
  3. 21
      LICENSE
  4. 119
      Makefile
  5. 358
      README.md
  6. 437
      api/api.go
  7. 58
      api/api_test.go
  8. 13
      api/apt_suite_test.go
  9. 6
      charts/local-ai/Chart.yaml
  10. 44
      charts/local-ai/templates/_helpers.tpl
  11. 39
      charts/local-ai/templates/data-volume.yaml
  12. 39
      charts/local-ai/templates/deployment.yaml
  13. 19
      charts/local-ai/templates/service.yaml
  14. 38
      charts/local-ai/values.yaml
  15. 15
      docker-compose.yaml
  16. 54
      go.mod
  17. 197
      go.sum
  18. 92
      main.go
  19. 0
      models/.keep
  20. 274
      pkg/model/loader.go
  21. 6
      prompt-templates/alpaca.tmpl
  22. 4
      prompt-templates/ggml-gpt4all-j.tmpl
  23. 1
      prompt-templates/koala.tmpl
  24. 6
      prompt-templates/vicuna.tmpl
  25. 17
      renovate.json

13
Dockerfile
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ARG GO_VERSION=1.20
ARG DEBIAN_VERSION=11
ARG BUILD_TYPE=
FROM golang:$GO_VERSION as builder
WORKDIR /build
RUN apt-get update && apt-get install -y cmake
COPY . .
RUN make build
FROM debian:$DEBIAN_VERSION
COPY --from=builder /build/local-ai /usr/bin/local-ai
ENTRYPOINT [ "/usr/bin/local-ai" ]

5
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VERSION 0.7
build:
FROM DOCKERFILE -f Dockerfile .
SAVE ARTIFACT /usr/bin/local-ai AS LOCAL local-ai

21
LICENSE
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MIT License
Copyright (c) 2023 go-skynet authors
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

119
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GOCMD=go
GOTEST=$(GOCMD) test
GOVET=$(GOCMD) vet
BINARY_NAME=local-ai
# renovate: datasource=github-tags depName=go-skynet/go-llama.cpp
GOLLAMA_VERSION?=llama.cpp-25d7abb
# renovate: datasource=git-refs packageNameTemplate=https://github.com/go-skynet/go-gpt4all-j.cpp currentValueTemplate=master depNameTemplate=go-gpt4all-j.cpp
GOGPT4ALLJ_VERSION?=1f7bff57f66cb7062e40d0ac3abd2217815e5109
# renovate: datasource=git-refs packageNameTemplate=https://github.com/go-skynet/go-gpt2.cpp currentValueTemplate=master depNameTemplate=go-gpt2.cpp
GOGPT2_VERSION?=245a5bfe6708ab80dc5c733dcdbfbe3cfd2acdaa
GREEN := $(shell tput -Txterm setaf 2)
YELLOW := $(shell tput -Txterm setaf 3)
WHITE := $(shell tput -Txterm setaf 7)
CYAN := $(shell tput -Txterm setaf 6)
RESET := $(shell tput -Txterm sgr0)
C_INCLUDE_PATH=$(shell pwd)/go-llama:$(shell pwd)/go-gpt4all-j:$(shell pwd)/go-gpt2
LIBRARY_PATH=$(shell pwd)/go-llama:$(shell pwd)/go-gpt4all-j:$(shell pwd)/go-gpt2
# Use this if you want to set the default behavior
ifndef BUILD_TYPE
BUILD_TYPE:=default
endif
ifeq ($(BUILD_TYPE), "generic")
GENERIC_PREFIX:=generic-
else
GENERIC_PREFIX:=
endif
.PHONY: all test build vendor
all: help
## Build:
build: prepare ## Build the project
$(info ${GREEN}I local-ai build info:${RESET})
$(info ${GREEN}I BUILD_TYPE: ${YELLOW}$(BUILD_TYPE)${RESET})
C_INCLUDE_PATH=${C_INCLUDE_PATH} LIBRARY_PATH=${LIBRARY_PATH} $(GOCMD) build -o $(BINARY_NAME) ./
generic-build: ## Build the project using generic
BUILD_TYPE="generic" $(MAKE) build
## GPT4ALL-J
go-gpt4all-j:
git clone --recurse-submodules https://github.com/go-skynet/go-gpt4all-j.cpp go-gpt4all-j
cd go-gpt4all-j && git checkout -b build $(GOGPT4ALLJ_VERSION)
# This is hackish, but needed as both go-llama and go-gpt4allj have their own version of ggml..
@find ./go-gpt4all-j -type f -name "*.c" -exec sed -i'' -e 's/ggml_/ggml_gptj_/g' {} +
@find ./go-gpt4all-j -type f -name "*.cpp" -exec sed -i'' -e 's/ggml_/ggml_gptj_/g' {} +
@find ./go-gpt4all-j -type f -name "*.h" -exec sed -i'' -e 's/ggml_/ggml_gptj_/g' {} +
@find ./go-gpt4all-j -type f -name "*.cpp" -exec sed -i'' -e 's/gpt_/gptj_/g' {} +
@find ./go-gpt4all-j -type f -name "*.h" -exec sed -i'' -e 's/gpt_/gptj_/g' {} +
@find ./go-gpt4all-j -type f -name "*.cpp" -exec sed -i'' -e 's/json_/json_gptj_/g' {} +
@find ./go-gpt4all-j -type f -name "*.cpp" -exec sed -i'' -e 's/void replace/void json_gptj_replace/g' {} +
@find ./go-gpt4all-j -type f -name "*.cpp" -exec sed -i'' -e 's/::replace/::json_gptj_replace/g' {} +
go-gpt4all-j/libgptj.a: go-gpt4all-j
$(MAKE) -C go-gpt4all-j $(GENERIC_PREFIX)libgptj.a
# CEREBRAS GPT
go-gpt2:
git clone --recurse-submodules https://github.com/go-skynet/go-gpt2.cpp go-gpt2
cd go-gpt2 && git checkout -b build $(GOGPT2_VERSION)
# This is hackish, but needed as both go-llama and go-gpt4allj have their own version of ggml..
@find ./go-gpt2 -type f -name "*.c" -exec sed -i'' -e 's/ggml_/ggml_gpt2_/g' {} +
@find ./go-gpt2 -type f -name "*.cpp" -exec sed -i'' -e 's/ggml_/ggml_gpt2_/g' {} +
@find ./go-gpt2 -type f -name "*.h" -exec sed -i'' -e 's/ggml_/ggml_gpt2_/g' {} +
@find ./go-gpt2 -type f -name "*.cpp" -exec sed -i'' -e 's/gpt_/gpt2_/g' {} +
@find ./go-gpt2 -type f -name "*.h" -exec sed -i'' -e 's/gpt_/gpt2_/g' {} +
@find ./go-gpt2 -type f -name "*.cpp" -exec sed -i'' -e 's/json_/json_gpt2_/g' {} +
go-gpt2/libgpt2.a: go-gpt2
$(MAKE) -C go-gpt2 $(GENERIC_PREFIX)libgpt2.a
go-llama:
git clone -b $(GOLLAMA_VERSION) --recurse-submodules https://github.com/go-skynet/go-llama.cpp go-llama
go-llama/libbinding.a: go-llama
$(MAKE) -C go-llama $(GENERIC_PREFIX)libbinding.a
replace:
$(GOCMD) mod edit -replace github.com/go-skynet/go-llama.cpp=$(shell pwd)/go-llama
$(GOCMD) mod edit -replace github.com/go-skynet/go-gpt4all-j.cpp=$(shell pwd)/go-gpt4all-j
$(GOCMD) mod edit -replace github.com/go-skynet/go-gpt2.cpp=$(shell pwd)/go-gpt2
prepare: go-llama/libbinding.a go-gpt4all-j/libgptj.a go-gpt2/libgpt2.a replace
clean: ## Remove build related file
rm -fr ./go-llama
rm -rf ./go-gpt4all-j
rm -rf ./go-gpt2
rm -rf $(BINARY_NAME)
## Run:
run: prepare
C_INCLUDE_PATH=${C_INCLUDE_PATH} LIBRARY_PATH=${LIBRARY_PATH} $(GOCMD) run ./main.go
test-models/testmodel:
mkdir test-models
wget https://huggingface.co/concedo/cerebras-111M-ggml/resolve/main/cerberas-111m-q4_0.bin -O test-models/testmodel
test: prepare test-models/testmodel
@C_INCLUDE_PATH=${C_INCLUDE_PATH} LIBRARY_PATH=${LIBRARY_PATH} MODELS_PATH=$(abspath ./)/test-models $(GOCMD) test -v ./...
## Help:
help: ## Show this help.
@echo ''
@echo 'Usage:'
@echo ' ${YELLOW}make${RESET} ${GREEN}<target>${RESET}'
@echo ''
@echo 'Targets:'
@awk 'BEGIN {FS = ":.*?## "} { \
if (/^[a-zA-Z_-]+:.*?##.*$$/) {printf " ${YELLOW}%-20s${GREEN}%s${RESET}\n", $$1, $$2} \
else if (/^## .*$$/) {printf " ${CYAN}%s${RESET}\n", substr($$1,4)} \
}' $(MAKEFILE_LIST)

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<h1 align="center">
<br>
<img height="300" src="https://user-images.githubusercontent.com/2420543/233147843-88697415-6dbf-4368-a862-ab217f9f7342.jpeg"> <br>
LocalAI
<br>
</h1>
> :warning: This project has been renamed from `llama-cli` to `LocalAI` to reflect the fact that we are focusing on a fast drop-in OpenAI API rather on the CLI interface. We think that there are already many projects that can be used as a CLI interface already, for instance [llama.cpp](https://github.com/ggerganov/llama.cpp) and [gpt4all](https://github.com/nomic-ai/gpt4all). If you are were using `llama-cli` for CLI interactions and want to keep using it, use older versions or please open up an issue - contributions are welcome!
[![tests](https://github.com/go-skynet/LocalAI/actions/workflows/test.yml/badge.svg)](https://github.com/go-skynet/LocalAI/actions/workflows/test.yml) [![build container images](https://github.com/go-skynet/LocalAI/actions/workflows/image.yml/badge.svg)](https://github.com/go-skynet/LocalAI/actions/workflows/image.yml)
[![](https://dcbadge.vercel.app/api/server/uJAeKSAGDy?style=flat-square&theme=default-inverted)](https://discord.gg/uJAeKSAGDy)
**LocalAI** 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.
- OpenAI compatible API
- Supports multiple-models
- Once loaded the first time, it keep models loaded in memory for faster inference
- Support for prompt templates
- 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).
Reddit post: https://www.reddit.com/r/selfhosted/comments/12w4p2f/localai_openai_compatible_api_to_run_llm_models/
## Model compatibility
It is compatible with the models supported by [llama.cpp](https://github.com/ggerganov/llama.cpp) supports also [GPT4ALL-J](https://github.com/nomic-ai/gpt4all) and [cerebras-GPT with ggml](https://huggingface.co/lxe/Cerebras-GPT-2.7B-Alpaca-SP-ggml).
Tested with:
- Vicuna
- Alpaca
- [GPT4ALL](https://github.com/nomic-ai/gpt4all)
- [GPT4ALL-J](https://gpt4all.io/models/ggml-gpt4all-j.bin)
- Koala
- [cerebras-GPT with ggml](https://huggingface.co/lxe/Cerebras-GPT-2.7B-Alpaca-SP-ggml)
It should also be compatible with StableLM and GPTNeoX ggml models (untested)
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`.
## Usage
> `LocalAI` 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/local-ai?tab=tags&tag=latest).
The easiest way to run LocalAI is by using `docker-compose`:
```bash
git clone https://github.com/go-skynet/LocalAI
cd LocalAI
# 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
}'
```
### Example: Use GPT4ALL-J model
<details>
```bash
# Clone LocalAI
git clone https://github.com/go-skynet/LocalAI
cd LocalAI
# Download gpt4all-j to models/
wget https://gpt4all.io/models/ggml-gpt4all-j.bin -O models/ggml-gpt4all-j
# Use a template from the examples
cp -rf prompt-templates/ggml-gpt4all-j.tmpl 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":"ggml-gpt4all-j","object":"model"}]}
curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
"model": "ggml-gpt4all-j",
"messages": [{"role": "user", "content": "How are you?"}],
"temperature": 0.9
}'
# {"model":"ggml-gpt4all-j","choices":[{"message":{"role":"assistant","content":"I'm doing well, thanks. How about you?"}}]}
```
</details>
## Prompt templates
The API doesn't inject a default prompt for talking to the model. 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.
<details>
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](https://github.com/go-skynet/LocalAI/tree/master/prompt-templates) directory in this repository for templates for most popular models.
</details>
## API
`LocalAI` provides an API for running text generation as a service, that follows the OpenAI reference and can be used as a drop-in. The models once loaded the first time will be kept in memory.
<details>
Example of starting the API with `docker`:
```bash
docker run -p 8080:8080 -ti --rm quay.io/go-skynet/local-ai:latest --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 │
└───────────────────────────────────────────────────┘
```
You can control the API server options with command line arguments:
```
local-api --models-path <model_path> [--address <address>] [--threads <num_threads>]
```
The API takes takes the following parameters:
| Parameter | Environment Variable | Default Value | Description |
| ------------ | -------------------- | ------------- | -------------------------------------- |
| models-path | MODELS_PATH | | The path where you have models (ending with `.bin`). |
| threads | THREADS | Number of Physical 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. |
| debug | DEBUG | false | Enable debug mode. |
Once the server is running, you can start making requests to it using HTTP, using the OpenAI API.
</details>
### Supported OpenAI API endpoints
You can check out the [OpenAI API reference](https://platform.openai.com/docs/api-reference/chat/create).
Following the list of endpoints/parameters supported.
Note:
- You can also specify the model a part of the OpenAI token.
- If only one model is available, the API will use it for all the requests.
#### Chat completions
<details>
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`
</details>
#### Completions
<details>
For example, to generate a completion, 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`
</details>
#### List models
<details>
You can list all the models available with:
```
curl http://localhost:8080/v1/models
```
</details>
## 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):
```bash
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!
```
## Helm Chart Installation (run LocalAI in Kubernetes)
The local-ai Helm chart supports two options for the LocalAI server's models directory:
1. Basic deployment with no persistent volume. You must manually update the Deployment to configure your own models directory.
Install the chart with `.Values.deployment.volumes.enabled == false` and `.Values.dataVolume.enabled == false`.
2. Advanced, two-phase deployment to provision the models directory using a DataVolume. Requires [Containerized Data Importer CDI](https://github.com/kubevirt/containerized-data-importer) to be pre-installed in your cluster.
First, install the chart with `.Values.deployment.volumes.enabled == false` and `.Values.dataVolume.enabled == true`:
```bash
helm install local-ai charts/local-ai -n local-ai --create-namespace
```
Wait for CDI to create an importer Pod for the DataVolume and for the importer pod to finish provisioning the model archive inside the PV.
Once the PV is provisioned and the importer Pod removed, set `.Values.deployment.volumes.enabled == true` and `.Values.dataVolume.enabled == false` and upgrade the chart:
```bash
helm upgrade local-ai -n local-ai charts/local-ai
```
This will update the local-ai Deployment to mount the PV that was provisioned by the DataVolume.
## Windows compatibility
It should work, however you need to make sure you give enough resources to the container. See https://github.com/go-skynet/LocalAI/issues/2
## 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 `LocalAI` container image locally you can use `docker`:
```
# build the image
docker build -t LocalAI .
docker run LocalAI
```
Or build the binary with `make`:
```
make build
```
## Frequently asked questions
Here are answers to some of the most common questions.
### How do I get models?
<details>
Most ggml-based models should work, but newer models may require additions to the API. If a model doesn't work, please feel free to open up issues. However, be cautious about downloading models from the internet and directly onto your machine, as there may be security vulnerabilities in lama.cpp or ggml that could be maliciously exploited. Some models can be found on Hugging Face: https://huggingface.co/models?search=ggml, or models from gpt4all should also work: https://github.com/nomic-ai/gpt4all.
</details>
### What's the difference with Serge, or XXX?
<details>
LocalAI is a multi-model solution that doesn't focus on a specific model type (e.g., llama.cpp or alpaca.cpp), and it handles all of these internally for faster inference, easy to set up locally and deploy to Kubernetes.
</details>
### Can I use it with a Discord bot, or XXX?
<details>
Yes! If the client uses OpenAI and supports setting a different base URL to send requests to, you can use the LocalAI endpoint. This allows to use this with every application that was supposed to work with OpenAI, but without changing the application!
</details>
### Can this leverage GPUs?
<details>
Not currently, as ggml doesn't support GPUs yet: https://github.com/ggerganov/llama.cpp/discussions/915.
</details>
### Where is the webUI?
<details>
We are working on to have a good out of the box experience - however as LocalAI is an API you can already plug it into existing projects that provides are UI interfaces to OpenAI's APIs. There are several already on github, and should be compatible with LocalAI already (as it mimics the OpenAI API)
</details>
### Does it work with AutoGPT?
<details>
AutoGPT currently doesn't allow to set a different API URL, but there is a PR open for it, so this should be possible soon!
</details>
## Short-term roadmap
- [x] Mimic OpenAI API (https://github.com/go-skynet/LocalAI/issues/10)
- [ ] Binary releases (https://github.com/go-skynet/LocalAI/issues/6)
- [ ] Upstream our golang bindings to llama.cpp (https://github.com/ggerganov/llama.cpp/issues/351)
- [x] Multi-model support
- [ ] Have a webUI!
- [ ] Allow configuration of defaults for models.
- [ ] Enable automatic downloading of models from a curated gallery, with only free-licensed models.
## License
MIT
## Acknowledgements
- [llama.cpp](https://github.com/ggerganov/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!)

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package api
import (
"encoding/json"
"errors"
"fmt"
"strings"
"sync"
model "github.com/go-skynet/LocalAI/pkg/model"
gpt2 "github.com/go-skynet/go-gpt2.cpp"
gptj "github.com/go-skynet/go-gpt4all-j.cpp"
llama "github.com/go-skynet/go-llama.cpp"
"github.com/gofiber/fiber/v2"
"github.com/gofiber/fiber/v2/middleware/cors"
"github.com/gofiber/fiber/v2/middleware/recover"
"github.com/rs/zerolog"
"github.com/rs/zerolog/log"
)
// APIError provides error information returned by the OpenAI API.
type APIError struct {
Code any `json:"code,omitempty"`
Message string `json:"message"`
Param *string `json:"param,omitempty"`
Type string `json:"type"`
}
type ErrorResponse struct {
Error *APIError `json:"error,omitempty"`
}
type OpenAIResponse struct {
Created int `json:"created,omitempty"`
Object string `json:"chat.completion,omitempty"`
ID string `json:"id,omitempty"`
Model string `json:"model,omitempty"`
Choices []Choice `json:"choices,omitempty"`
}
type Choice struct {
Index int `json:"index,omitempty"`
FinishReason string `json:"finish_reason,omitempty"`
Message *Message `json:"message,omitempty"`
Text string `json:"text,omitempty"`
}
type Message struct {
Role string `json:"role,omitempty"`
Content string `json:"content,omitempty"`
}
type OpenAIModel struct {
ID string `json:"id"`
Object string `json:"object"`
}
type OpenAIRequest struct {
Model string `json:"model"`
// Prompt is read only by completion API calls
Prompt string `json:"prompt"`
Stop string `json:"stop"`
// Messages is read only by chat/completion API calls
Messages []Message `json:"messages"`
Echo bool `json:"echo"`
// Common options between all the API calls
TopP float64 `json:"top_p"`
TopK int `json:"top_k"`
Temperature float64 `json:"temperature"`
Maxtokens int `json:"max_tokens"`
N int `json:"n"`
// Custom parameters - not present in the OpenAI API
Batch int `json:"batch"`
F16 bool `json:"f16kv"`
IgnoreEOS bool `json:"ignore_eos"`
RepeatPenalty float64 `json:"repeat_penalty"`
Keep int `json:"n_keep"`
Seed int `json:"seed"`
}
// https://platform.openai.com/docs/api-reference/completions
func openAIEndpoint(chat, debug bool, loader *model.ModelLoader, threads, ctx int, f16 bool, mutexMap *sync.Mutex, mutexes map[string]*sync.Mutex) func(c *fiber.Ctx) error {
return func(c *fiber.Ctx) error {
var err error
var model *llama.LLama
var gptModel *gptj.GPTJ
var gpt2Model *gpt2.GPT2
var stableLMModel *gpt2.StableLM
input := new(OpenAIRequest)
// Get input data from the request body
if err := c.BodyParser(input); err != nil {
return err
}
modelFile := input.Model
received, _ := json.Marshal(input)
log.Debug().Msgf("Request received: %s", string(received))
// Set model from bearer token, if available
bearer := strings.TrimLeft(c.Get("authorization"), "Bearer ")
bearerExists := bearer != "" && loader.ExistsInModelPath(bearer)
// If no model was specified, take the first available
if modelFile == "" {
models, _ := loader.ListModels()
if len(models) > 0 {
modelFile = models[0]
log.Debug().Msgf("No model specified, using: %s", modelFile)
}
}
// If no model is found or specified, we bail out
if modelFile == "" && !bearerExists {
return fmt.Errorf("no model specified")
}
// If a model is found in bearer token takes precedence
if bearerExists {
log.Debug().Msgf("Using model from bearer token: %s", bearer)
modelFile = bearer
}
// Try to load the model
var llamaerr, gpt2err, gptjerr, stableerr error
llamaOpts := []llama.ModelOption{}
if ctx != 0 {
llamaOpts = append(llamaOpts, llama.SetContext(ctx))
}
if f16 {
llamaOpts = append(llamaOpts, llama.EnableF16Memory)
}
// TODO: this is ugly, better identifying the model somehow! however, it is a good stab for a first implementation..
model, llamaerr = loader.LoadLLaMAModel(modelFile, llamaOpts...)
if llamaerr != nil {
gptModel, gptjerr = loader.LoadGPTJModel(modelFile)
if gptjerr != nil {
gpt2Model, gpt2err = loader.LoadGPT2Model(modelFile)
if gpt2err != nil {
stableLMModel, stableerr = loader.LoadStableLMModel(modelFile)
if stableerr != nil {
return fmt.Errorf("llama: %s gpt: %s gpt2: %s stableLM: %s", llamaerr.Error(), gptjerr.Error(), gpt2err.Error(), stableerr.Error()) // llama failed first, so we want to catch both errors
}
}
}
}
// This is still needed, see: https://github.com/ggerganov/llama.cpp/discussions/784
mutexMap.Lock()
l, ok := mutexes[modelFile]
if !ok {
m := &sync.Mutex{}
mutexes[modelFile] = m
l = m
}
mutexMap.Unlock()
l.Lock()
defer l.Unlock()
// Set the parameters for the language model prediction
topP := input.TopP
if topP == 0 {
topP = 0.7
}
topK := input.TopK
if topK == 0 {
topK = 80
}
temperature := input.Temperature
if temperature == 0 {
temperature = 0.9
}
tokens := input.Maxtokens
if tokens == 0 {
tokens = 512
}
predInput := input.Prompt
if chat {
mess := []string{}
// TODO: encode roles
for _, i := range input.Messages {
mess = append(mess, i.Content)
}
predInput = strings.Join(mess, "\n")
}
// A model can have a "file.bin.tmpl" file associated with a prompt template prefix
templatedInput, err := loader.TemplatePrefix(modelFile, struct {
Input string
}{Input: predInput})
if err == nil {
predInput = templatedInput
log.Debug().Msgf("Template found, input modified to: %s", predInput)
}
result := []Choice{}
n := input.N
if input.N == 0 {
n = 1
}
var predFunc func() (string, error)
switch {
case stableLMModel != nil:
predFunc = func() (string, error) {
// Generate the prediction using the language model
predictOptions := []gpt2.PredictOption{
gpt2.SetTemperature(temperature),
gpt2.SetTopP(topP),
gpt2.SetTopK(topK),
gpt2.SetTokens(tokens),
gpt2.SetThreads(threads),
}
if input.Batch != 0 {
predictOptions = append(predictOptions, gpt2.SetBatch(input.Batch))
}
if input.Seed != 0 {
predictOptions = append(predictOptions, gpt2.SetSeed(input.Seed))
}
return stableLMModel.Predict(
predInput,
predictOptions...,
)
}
case gpt2Model != nil:
predFunc = func() (string, error) {
// Generate the prediction using the language model
predictOptions := []gpt2.PredictOption{
gpt2.SetTemperature(temperature),
gpt2.SetTopP(topP),
gpt2.SetTopK(topK),
gpt2.SetTokens(tokens),
gpt2.SetThreads(threads),
}
if input.Batch != 0 {
predictOptions = append(predictOptions, gpt2.SetBatch(input.Batch))
}
if input.Seed != 0 {
predictOptions = append(predictOptions, gpt2.SetSeed(input.Seed))
}
return gpt2Model.Predict(
predInput,
predictOptions...,
)
}
case gptModel != nil:
predFunc = func() (string, error) {
// Generate the prediction using the language model
predictOptions := []gptj.PredictOption{
gptj.SetTemperature(temperature),
gptj.SetTopP(topP),
gptj.SetTopK(topK),
gptj.SetTokens(tokens),
gptj.SetThreads(threads),
}
if input.Batch != 0 {
predictOptions = append(predictOptions, gptj.SetBatch(input.Batch))
}
if input.Seed != 0 {
predictOptions = append(predictOptions, gptj.SetSeed(input.Seed))
}
return gptModel.Predict(
predInput,
predictOptions...,
)
}
case model != nil:
predFunc = func() (string, error) {
// Generate the prediction using the language model
predictOptions := []llama.PredictOption{
llama.SetTemperature(temperature),
llama.SetTopP(topP),
llama.SetTopK(topK),
llama.SetTokens(tokens),
llama.SetThreads(threads),
}
if debug {
predictOptions = append(predictOptions, llama.Debug)
}
if input.Stop != "" {
predictOptions = append(predictOptions, llama.SetStopWords(input.Stop))
}
if input.RepeatPenalty != 0 {
predictOptions = append(predictOptions, llama.SetPenalty(input.RepeatPenalty))
}
if input.Keep != 0 {
predictOptions = append(predictOptions, llama.SetNKeep(input.Keep))
}
if input.Batch != 0 {
predictOptions = append(predictOptions, llama.SetBatch(input.Batch))
}
if input.F16 {
predictOptions = append(predictOptions, llama.EnableF16KV)
}
if input.IgnoreEOS {
predictOptions = append(predictOptions, llama.IgnoreEOS)
}
if input.Seed != 0 {
predictOptions = append(predictOptions, llama.SetSeed(input.Seed))
}
return model.Predict(
predInput,
predictOptions...,
)
}
}
for i := 0; i < n; i++ {
prediction, err := predFunc()
if err != nil {
return err
}
if input.Echo {
prediction = predInput + prediction
}
if chat {
result = append(result, Choice{Message: &Message{Role: "assistant", Content: prediction}})
} else {
result = append(result, Choice{Text: prediction})
}
}
jsonResult, _ := json.Marshal(result)
log.Debug().Msgf("Response: %s", jsonResult)
// Return the prediction in the response body
return c.JSON(OpenAIResponse{
Model: input.Model, // we have to return what the user sent here, due to OpenAI spec.
Choices: result,
})
}
}
func listModels(loader *model.ModelLoader) func(ctx *fiber.Ctx) error {
return func(c *fiber.Ctx) error {
models, err := loader.ListModels()
if err != nil {
return err
}
dataModels := []OpenAIModel{}
for _, m := range models {
dataModels = append(dataModels, OpenAIModel{ID: m, Object: "model"})
}
return c.JSON(struct {
Object string `json:"object"`
Data []OpenAIModel `json:"data"`
}{
Object: "list",
Data: dataModels,
})
}
}
func App(loader *model.ModelLoader, threads, ctxSize int, f16 bool, debug, disableMessage bool) *fiber.App {
zerolog.SetGlobalLevel(zerolog.InfoLevel)
if debug {
zerolog.SetGlobalLevel(zerolog.DebugLevel)
}
// Return errors as JSON responses
app := fiber.New(fiber.Config{
DisableStartupMessage: disableMessage,
// Override default error handler
ErrorHandler: func(ctx *fiber.Ctx, err error) error {
// Status code defaults to 500
code := fiber.StatusInternalServerError
// Retrieve the custom status code if it's a *fiber.Error
var e *fiber.Error
if errors.As(err, &e) {
code = e.Code
}
// Send custom error page
return ctx.Status(code).JSON(
ErrorResponse{
Error: &APIError{Message: err.Error(), Code: code},
},
)
},
})
// Default middleware config
app.Use(recover.New())
app.Use(cors.New())
// This is still needed, see: https://github.com/ggerganov/llama.cpp/discussions/784
mu := map[string]*sync.Mutex{}
var mumutex = &sync.Mutex{}
// openAI compatible API endpoint
app.Post("/v1/chat/completions", openAIEndpoint(true, debug, loader, threads, ctxSize, f16, mumutex, mu))
app.Post("/chat/completions", openAIEndpoint(true, debug, loader, threads, ctxSize, f16, mumutex, mu))
app.Post("/v1/completions", openAIEndpoint(false, debug, loader, threads, ctxSize, f16, mumutex, mu))
app.Post("/completions", openAIEndpoint(false, debug, loader, threads, ctxSize, f16, mumutex, mu))
app.Get("/v1/models", listModels(loader))
app.Get("/models", listModels(loader))
return app
}

58
api/api_test.go
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@ -1,58 +0,0 @@
package api_test
import (
"context"
"os"
. "github.com/go-skynet/LocalAI/api"
"github.com/go-skynet/LocalAI/pkg/model"
"github.com/gofiber/fiber/v2"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
"github.com/sashabaranov/go-openai"
)
var _ = Describe("API test", func() {
var app *fiber.App
var modelLoader *model.ModelLoader
var client *openai.Client
Context("API query", func() {
BeforeEach(func() {
modelLoader = model.NewModelLoader(os.Getenv("MODELS_PATH"))
app = App(modelLoader, 1, 512, false, false, true)
go app.Listen("127.0.0.1:9090")
defaultConfig := openai.DefaultConfig("")
defaultConfig.BaseURL = "http://127.0.0.1:9090/v1"
// Wait for API to be ready
client = openai.NewClientWithConfig(defaultConfig)
Eventually(func() error {
_, err := client.ListModels(context.TODO())
return err
}, "2m").ShouldNot(HaveOccurred())
})
AfterEach(func() {
app.Shutdown()
})
It("returns the models list", func() {
models, err := client.ListModels(context.TODO())
Expect(err).ToNot(HaveOccurred())
Expect(len(models.Models)).To(Equal(1))
Expect(models.Models[0].ID).To(Equal("testmodel"))
})
It("can generate completions", func() {
resp, err := client.CreateCompletion(context.TODO(), openai.CompletionRequest{Model: "testmodel", Prompt: "abcdedfghikl"})
Expect(err).ToNot(HaveOccurred())
Expect(len(resp.Choices)).To(Equal(1))
Expect(resp.Choices[0].Text).ToNot(BeEmpty())
})
It("returns errors", func() {
_, err := client.CreateCompletion(context.TODO(), openai.CompletionRequest{Model: "foomodel", Prompt: "abcdedfghikl"})
Expect(err).To(HaveOccurred())
Expect(err.Error()).To(ContainSubstring("error, status code: 500, message: llama: model does not exist"))
})
})
})

13
api/apt_suite_test.go
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@ -1,13 +0,0 @@
package api_test
import (
"testing"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
func TestLocalAI(t *testing.T) {
RegisterFailHandler(Fail)
RunSpecs(t, "LocalAI test suite")
}

6
charts/local-ai/Chart.yaml
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@ -1,6 +0,0 @@
apiVersion: v2
appVersion: 0.1.0
description: A Helm chart for LocalAI
name: local-ai
type: application
version: 1.0.0

44
charts/local-ai/templates/_helpers.tpl
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@ -1,44 +0,0 @@
{{/*
Expand the name of the chart.
*/}}
{{- define "local-ai.name" -}}
{{- default .Chart.Name .Values.nameOverride | trunc 63 | trimSuffix "-" }}
{{- end }}
{{/*
Create a default fully qualified app name.
We truncate at 63 chars because some Kubernetes name fields are limited to this (by the DNS naming spec).
If release name contains chart name it will be used as a full name.
*/}}
{{- define "local-ai.fullname" -}}
{{- if .Values.fullnameOverride }}
{{- .Values.fullnameOverride | trunc 63 | trimSuffix "-" }}
{{- else }}
{{- $name := default .Chart.Name .Values.nameOverride }}
{{- if contains $name .Release.Name }}
{{- .Release.Name | trunc 63 | trimSuffix "-" }}
{{- else }}
{{- printf "%s-%s" .Release.Name $name | trunc 63 | trimSuffix "-" }}
{{- end }}
{{- end }}
{{- end }}
{{/*
Create chart name and version as used by the chart label.
*/}}
{{- define "local-ai.chart" -}}
{{- printf "%s-%s" .Chart.Name .Chart.Version | replace "+" "_" | trunc 63 | trimSuffix "-" }}
{{- end }}
{{/*
Common labels
*/}}
{{- define "local-ai.labels" -}}
helm.sh/chart: {{ include "local-ai.chart" . }}
app.kubernetes.io/name: {{ include "local-ai.name" . }}
app.kubernetes.io/instance: "{{ .Release.Name }}"
app.kubernetes.io/managed-by: {{ .Release.Service }}
{{- if .Chart.AppVersion }}
app.kubernetes.io/version: {{ .Chart.AppVersion | quote }}
{{- end }}
{{- end }}

39
charts/local-ai/templates/data-volume.yaml
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@ -1,39 +0,0 @@
{{- if .Values.dataVolume.enabled }}
apiVersion: cdi.kubevirt.io/v1beta1
kind: DataVolume
metadata:
name: {{ template "local-ai.fullname" . }}
namespace: {{ .Release.Namespace | quote }}
labels:
{{- include "local-ai.labels" . | nindent 4 }}
spec:
contentType: archive
source:
{{ .Values.dataVolume.source.type }}:
url: {{ .Values.dataVolume.source.url }}
secretRef: {{ template "local-ai.fullname" . }}
{{- if and (eq .Values.dataVolume.source.type "http") .Values.dataVolume.source.secretExtraHeaders }}
secretExtraHeaders: {{ .Values.dataVolume.source.secretExtraHeaders }}
{{- end }}
{{- if .Values.dataVolume.source.caCertConfigMap }}
caCertConfigMap: {{ .Values.dataVolume.source.caCertConfigMap }}
{{- end }}
pvc:
accessModes: {{ .Values.dataVolume.pvc.accessModes }}
resources:
requests:
storage: {{ .Values.dataVolume.pvc.size }}
---
{{- if .Values.dataVolume.secret.enabled }}
apiVersion: v1
kind: Secret
metadata:
name: {{ template "local-ai.fullname" . }}
namespace: {{ .Release.Namespace | quote }}
labels:
{{- include "local-ai.labels" . | nindent 4 }}
data:
accessKeyId: {{ .Values.dataVolume.secret.username }}
secretKey: {{ .Values.dataVolume.secret.password }}
{{- end }}
{{- end }}

39
charts/local-ai/templates/deployment.yaml
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@ -1,39 +0,0 @@
apiVersion: apps/v1
kind: Deployment
metadata:
name: {{ template "local-ai.fullname" . }}
namespace: {{ .Release.Namespace | quote }}
labels:
{{- include "local-ai.labels" . | nindent 4 }}
spec:
selector:
matchLabels:
app.kubernetes.io/name: {{ include "local-ai.name" . }}
app.kubernetes.io/instance: {{ .Release.Name }}
replicas: 1
template:
metadata:
name: {{ template "local-ai.fullname" . }}
labels:
app.kubernetes.io/name: {{ include "local-ai.name" . }}
app.kubernetes.io/instance: {{ .Release.Name }}
spec:
containers:
- name: {{ template "local-ai.fullname" . }}
image: {{ .Values.deployment.image }}
env:
- name: THREADS
value: {{ .Values.deployment.env.threads | quote }}
- name: CONTEXT_SIZE
value: {{ .Values.deployment.env.contextSize | quote }}
- name: MODELS_PATH
value: {{ .Values.deployment.env.modelsPath }}
{{- if .Values.deployment.volume.enabled }}
volumeMounts:
- mountPath: {{ .Values.deployment.env.modelsPath }}
name: models
volumes:
- name: models
persistentVolumeClaim:
claimName: {{ template "local-ai.fullname" . }}
{{- end }}

19
charts/local-ai/templates/service.yaml
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@ -1,19 +0,0 @@
apiVersion: v1
kind: Service
metadata:
name: {{ template "local-ai.fullname" . }}
namespace: {{ .Release.Namespace | quote }}
labels:
{{- include "local-ai.labels" . | nindent 4 }}
{{- if .Values.service.annotations }}
annotations:
{{ toYaml .Values.service.annotations | indent 4 }}
{{- end }}
spec:
selector:
app.kubernetes.io/name: {{ include "local-ai.name" . }}
type: "{{ .Values.service.type }}"
ports:
- protocol: TCP
port: 8080
targetPort: 8080

38
charts/local-ai/values.yaml
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@ -1,38 +0,0 @@
deployment:
image: quay.io/go-skynet/local-ai:latest
env:
threads: 14
contextSize: 512
modelsPath: "/models"
volume:
enabled: false
service:
type: ClusterIP
annotations: {}
# If using an AWS load balancer, you'll need to override the default 60s load balancer idle timeout
# service.beta.kubernetes.io/aws-load-balancer-connection-idle-timeout: "1200"
# Optionally create a PVC containing a model binary, sourced from an arbitrary HTTP server or S3 bucket
# (requires https://github.com/kubevirt/containerized-data-importer)
dataVolume:
enabled: false
source:
type: "http" # Source type. One of: [ http | s3 ]
url: "http://<model_server>/<model_archive>" # e.g. koala-7B-4bit-128g.GGML.tar
# CertConfigMap is an optional ConfigMap reference, containing a Certificate Authority (CA) public key
# and a base64 encoded pem certificate
caCertConfigMap: ""
# SecretExtraHeaders is an optional list of Secret references, each containing an extra HTTP header
# that may include sensitive information. Only applicable for the http source type.
secretExtraHeaders: []
pvc:
accessModes:
- ReadWriteOnce
size: 5Gi
secret:
enabled: false
username: "" # base64 encoded
password: "" # base64 encoded

15
docker-compose.yaml
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@ -1,15 +0,0 @@
version: '3.6'
services:
api:
image: quay.io/go-skynet/local-ai:latest
build:
context: .
dockerfile: Dockerfile
ports:
- 8080:8080
env_file:
- .env
volumes:
- ./models:/models:cached
command: ["/usr/bin/local-ai" ]

54
go.mod
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@ -1,54 +0,0 @@
module github.com/go-skynet/LocalAI
go 1.19
require (
github.com/go-skynet/go-gpt2.cpp v0.0.0-20230422085954-245a5bfe6708
github.com/go-skynet/go-gpt4all-j.cpp v0.0.0-20230422090028-1f7bff57f66c
github.com/go-skynet/go-llama.cpp v0.0.0-20230424120713-e45cebe33c04
github.com/gofiber/fiber/v2 v2.44.0
github.com/jaypipes/ghw v0.10.0
github.com/onsi/ginkgo/v2 v2.9.2
github.com/onsi/gomega v1.27.6
github.com/rs/zerolog v1.29.1
github.com/sashabaranov/go-openai v1.9.0
github.com/urfave/cli/v2 v2.25.1
)
require (
github.com/StackExchange/wmi v1.2.1 // indirect
github.com/andybalholm/brotli v1.0.5 // indirect
github.com/cpuguy83/go-md2man/v2 v2.0.2 // indirect
github.com/ghodss/yaml v1.0.0 // indirect
github.com/go-logr/logr v1.2.3 // indirect
github.com/go-ole/go-ole v1.2.6 // indirect
github.com/go-task/slim-sprig v0.0.0-20230315185526-52ccab3ef572 // indirect
github.com/google/go-cmp v0.5.9 // indirect
github.com/google/pprof v0.0.0-20210407192527-94a9f03dee38 // indirect
github.com/google/uuid v1.3.0 // indirect
github.com/jaypipes/pcidb v1.0.0 // indirect
github.com/klauspost/compress v1.16.3 // indirect
github.com/kr/text v0.2.0 // indirect
github.com/mattn/go-colorable v0.1.13 // indirect
github.com/mattn/go-isatty v0.0.18 // indirect
github.com/mattn/go-runewidth v0.0.14 // indirect
github.com/mitchellh/go-homedir v1.1.0 // indirect
github.com/philhofer/fwd v1.1.2 // indirect
github.com/pkg/errors v0.9.1 // indirect
github.com/rivo/uniseg v0.2.0 // indirect
github.com/russross/blackfriday/v2 v2.1.0 // indirect
github.com/savsgio/dictpool v0.0.0-20221023140959-7bf2e61cea94 // indirect
github.com/savsgio/gotils v0.0.0-20230208104028-c358bd845dee // indirect
github.com/tinylib/msgp v1.1.8 // indirect
github.com/valyala/bytebufferpool v1.0.0 // indirect
github.com/valyala/fasthttp v1.45.0 // indirect
github.com/valyala/tcplisten v1.0.0 // indirect
github.com/xrash/smetrics v0.0.0-20201216005158-039620a65673 // indirect
golang.org/x/net v0.8.0 // indirect
golang.org/x/sys v0.7.0 // indirect
golang.org/x/text v0.8.0 // indirect
golang.org/x/tools v0.7.0 // indirect
gopkg.in/yaml.v2 v2.4.0 // indirect
gopkg.in/yaml.v3 v3.0.1 // indirect
howett.net/plist v1.0.0 // indirect
)

197
go.sum
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@ -1,197 +0,0 @@
github.com/StackExchange/wmi v1.2.1 h1:VIkavFPXSjcnS+O8yTq7NI32k0R5Aj+v39y29VYDOSA=
github.com/StackExchange/wmi v1.2.1/go.mod h1:rcmrprowKIVzvc+NUiLncP2uuArMWLCbu9SBzvHz7e8=
github.com/andybalholm/brotli v1.0.4 h1:V7DdXeJtZscaqfNuAdSRuRFzuiKlHSC/Zh3zl9qY3JY=
github.com/andybalholm/brotli v1.0.4/go.mod h1:fO7iG3H7G2nSZ7m0zPUDn85XEX2GTukHGRSepvi9Eig=
github.com/andybalholm/brotli v1.0.5 h1:8uQZIdzKmjc/iuPu7O2ioW48L81FgatrcpfFmiq/cCs=
github.com/andybalholm/brotli v1.0.5/go.mod h1:fO7iG3H7G2nSZ7m0zPUDn85XEX2GTukHGRSepvi9Eig=
github.com/chzyer/logex v1.1.10/go.mod h1:+Ywpsq7O8HXn0nuIou7OrIPyXbp3wmkHB+jjWRnGsAI=
github.com/chzyer/readline v0.0.0-20180603132655-2972be24d48e/go.mod h1:nSuG5e5PlCu98SY8svDHJxuZscDgtXS6KTTbou5AhLI=
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golang.org/x/net v0.0.0-20201021035429-f5854403a974/go.mod h1:sp8m0HH+o8qH0wwXwYZr8TS3Oi6o0r6Gce1SSxlDquU=
golang.org/x/net v0.0.0-20210226172049-e18ecbb05110/go.mod h1:m0MpNAwzfU5UDzcl9v0D8zg8gWTRqZa9RBIspLL5mdg=
golang.org/x/net v0.0.0-20211112202133-69e39bad7dc2/go.mod h1:9nx3DQGgdP8bBQD5qxJ1jj9UTztislL4KSBs9R2vV5Y=
golang.org/x/net v0.0.0-20220722155237-a158d28d115b/go.mod h1:XRhObCWvk6IyKnWLug+ECip1KBveYUHfp+8e9klMJ9c=
golang.org/x/net v0.0.0-20220906165146-f3363e06e74c/go.mod h1:YDH+HFinaLZZlnHAfSS6ZXJJ9M9t4Dl22yv3iI2vPwk=
golang.org/x/net v0.3.0/go.mod h1:MBQ8lrhLObU/6UmLb4fmbmk5OcyYmqtbGd/9yIeKjEE=
golang.org/x/net v0.8.0 h1:Zrh2ngAOFYneWTAIAPethzeaQLuHwhuBkuV6ZiRnUaQ=
golang.org/x/net v0.8.0/go.mod h1:QVkue5JL9kW//ek3r6jTKnTFis1tRmNAW2P1shuFdJc=
golang.org/x/sync v0.0.0-20190423024810-112230192c58/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
golang.org/x/sync v0.0.0-20201020160332-67f06af15bc9/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
golang.org/x/sync v0.0.0-20220722155255-886fb9371eb4/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
golang.org/x/sync v0.1.0/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
golang.org/x/sys v0.0.0-20190215142949-d0b11bdaac8a/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
golang.org/x/sys v0.0.0-20190412213103-97732733099d/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
golang.org/x/sys v0.0.0-20190916202348-b4ddaad3f8a3/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
golang.org/x/sys v0.0.0-20191204072324-ce4227a45e2e/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
golang.org/x/sys v0.0.0-20200930185726-fdedc70b468f/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
golang.org/x/sys v0.0.0-20201119102817-f84b799fce68/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
golang.org/x/sys v0.0.0-20210423082822-04245dca01da/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
golang.org/x/sys v0.0.0-20210615035016-665e8c7367d1/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
golang.org/x/sys v0.0.0-20210630005230-0f9fa26af87c/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
golang.org/x/sys v0.0.0-20210927094055-39ccf1dd6fa6/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
golang.org/x/sys v0.0.0-20220520151302-bc2c85ada10a/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
golang.org/x/sys v0.0.0-20220722155257-8c9f86f7a55f/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
golang.org/x/sys v0.0.0-20220728004956-3c1f35247d10/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
golang.org/x/sys v0.0.0-20220811171246-fbc7d0a398ab/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
golang.org/x/sys v0.3.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
golang.org/x/sys v0.6.0 h1:MVltZSvRTcU2ljQOhs94SXPftV6DCNnZViHeQps87pQ=
golang.org/x/sys v0.6.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
golang.org/x/sys v0.7.0 h1:3jlCCIQZPdOYu1h8BkNvLz8Kgwtae2cagcG/VamtZRU=
golang.org/x/sys v0.7.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
golang.org/x/term v0.0.0-20201126162022-7de9c90e9dd1/go.mod h1:bj7SfCRtBDWHUb9snDiAeCFNEtKQo2Wmx5Cou7ajbmo=
golang.org/x/term v0.0.0-20210927222741-03fcf44c2211/go.mod h1:jbD1KX2456YbFQfuXm/mYQcufACuNUgVhRMnK/tPxf8=
golang.org/x/term v0.3.0/go.mod h1:q750SLmJuPmVoN1blW3UFBPREJfb1KmY3vwxfr+nFDA=
golang.org/x/text v0.3.0/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ=
golang.org/x/text v0.3.3/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
golang.org/x/text v0.3.6/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
golang.org/x/text v0.3.7/go.mod h1:u+2+/6zg+i71rQMx5EYifcz6MCKuco9NR6JIITiCfzQ=
golang.org/x/text v0.5.0/go.mod h1:mrYo+phRRbMaCq/xk9113O4dZlRixOauAjOtrjsXDZ8=
golang.org/x/text v0.8.0 h1:57P1ETyNKtuIjB4SRd15iJxuhj8Gc416Y78H3qgMh68=
golang.org/x/text v0.8.0/go.mod h1:e1OnstbJyHTd6l/uOt8jFFHp6TRDWZR/bV3emEE/zU8=
golang.org/x/tools v0.0.0-20180917221912-90fa682c2a6e/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
golang.org/x/tools v0.0.0-20191119224855-298f0cb1881e/go.mod h1:b+2E5dAYhXwXZwtnZ6UAqBI28+e2cm9otk0dWdXHAEo=
golang.org/x/tools v0.0.0-20201022035929-9cf592e881e9/go.mod h1:emZCQorbCU4vsT4fOWvOPXz4eW1wZW4PmDk9uLelYpA=
golang.org/x/tools v0.1.12/go.mod h1:hNGJHUnrk76NpqgfD5Aqm5Crs+Hm0VOH/i9J2+nxYbc=
golang.org/x/tools v0.4.0/go.mod h1:UE5sM2OK9E/d67R0ANs2xJizIymRP5gJU295PvKXxjQ=
golang.org/x/tools v0.7.0 h1:W4OVu8VVOaIO0yzWMNdepAulS7YfoS3Zabrm8DOXXU4=
golang.org/x/tools v0.7.0/go.mod h1:4pg6aUX35JBAogB10C9AtvVL+qowtN4pT3CGSQex14s=
golang.org/x/xerrors v0.0.0-20190717185122-a985d3407aa7/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
golang.org/x/xerrors v0.0.0-20191011141410-1b5146add898/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
golang.org/x/xerrors v0.0.0-20200804184101-5ec99f83aff1/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
google.golang.org/protobuf v1.28.0 h1:w43yiav+6bVFTBQFZX0r7ipe9JQ1QsbMgHwbBziscLw=
gopkg.in/check.v1 v0.0.0-20161208181325-20d25e280405/go.mod h1:Co6ibVJAznAaIkqp8huTwlJQCZ016jof/cbN4VW5Yz0=
gopkg.in/check.v1 v1.0.0-20180628173108-788fd7840127 h1:qIbj1fsPNlZgppZ+VLlY7N33q108Sa+fhmuc+sWQYwY=
gopkg.in/yaml.v1 v1.0.0-20140924161607-9f9df34309c0/go.mod h1:WDnlLJ4WF5VGsH/HVa3CI79GS0ol3YnhVnKP89i0kNg=
gopkg.in/yaml.v2 v2.4.0 h1:D8xgwECY7CYvx+Y2n4sBz93Jn9JRvxdiyyo8CTfuKaY=
gopkg.in/yaml.v2 v2.4.0/go.mod h1:RDklbk79AGWmwhnvt/jBztapEOGDOx6ZbXqjP6csGnQ=
gopkg.in/yaml.v3 v3.0.0-20200313102051-9f266ea9e77c/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
gopkg.in/yaml.v3 v3.0.1 h1:fxVm/GzAzEWqLHuvctI91KS9hhNmmWOoWu0XTYJS7CA=
gopkg.in/yaml.v3 v3.0.1/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
howett.net/plist v1.0.0 h1:7CrbWYbPPO/PyNy38b2EB/+gYbjCe2DXBxgtOOZbSQM=
howett.net/plist v1.0.0/go.mod h1:lqaXoTrLY4hg8tnEzNru53gicrbv7rrk+2xJA/7hw9g=

92
main.go
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@ -1,92 +0,0 @@
package main
import (
"os"
api "github.com/go-skynet/LocalAI/api"
model "github.com/go-skynet/LocalAI/pkg/model"
"github.com/jaypipes/ghw"
"github.com/rs/zerolog"
"github.com/rs/zerolog/log"
"github.com/urfave/cli/v2"
)
func main() {
log.Logger = log.Output(zerolog.ConsoleWriter{Out: os.Stderr})
path, err := os.Getwd()
if err != nil {
log.Error().Msgf("error: %s", err.Error())
os.Exit(1)
}
threads := 4
cpu, err := ghw.CPU()
if err == nil {
threads = int(cpu.TotalCores)
}
app := &cli.App{
Name: "LocalAI",
Usage: "OpenAI compatible API for running LLaMA/GPT models locally on CPU with consumer grade hardware.",
Flags: []cli.Flag{
&cli.BoolFlag{
Name: "f16",
EnvVars: []string{"F16"},
},
&cli.BoolFlag{
Name: "debug",
EnvVars: []string{"DEBUG"},
},
&cli.IntFlag{
Name: "threads",
DefaultText: "Number of threads used for parallel computation. Usage of the number of physical cores in the system is suggested.",
EnvVars: []string{"THREADS"},
Value: threads,
},
&cli.StringFlag{
Name: "models-path",
DefaultText: "Path containing models used for inferencing",
EnvVars: []string{"MODELS_PATH"},
Value: path,
},
&cli.StringFlag{
Name: "address",
DefaultText: "Bind address for the API server.",
EnvVars: []string{"ADDRESS"},
Value: ":8080",
},
&cli.IntFlag{
Name: "context-size",
DefaultText: "Default context size of the model",
EnvVars: []string{"CONTEXT_SIZE"},
Value: 512,
},
},
Description: `
LocalAI is a drop-in replacement OpenAI API which runs inference locally.
Some of the models compatible are:
- Vicuna
- Koala
- GPT4ALL
- GPT4ALL-J
- Cerebras
- Alpaca
- StableLM (ggml quantized)
It uses llama.cpp, ggml and gpt4all as backend with golang c bindings.
`,
UsageText: `local-ai [options]`,
Copyright: "go-skynet authors",
Action: func(ctx *cli.Context) error {
return api.App(model.NewModelLoader(ctx.String("models-path")), ctx.Int("threads"), ctx.Int("context-size"), ctx.Bool("f16"), ctx.Bool("debug"), false).Listen(ctx.String("address"))
},
}
err = app.Run(os.Args)
if err != nil {
log.Error().Msgf("error: %s", err.Error())
os.Exit(1)
}
}

0
models/.keep
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274
pkg/model/loader.go
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@ -1,274 +0,0 @@
package model
import (
"bytes"
"fmt"
"io/ioutil"
"os"
"path/filepath"
"strings"
"sync"
"text/template"
"github.com/rs/zerolog/log"
gpt2 "github.com/go-skynet/go-gpt2.cpp"
gptj "github.com/go-skynet/go-gpt4all-j.cpp"
llama "github.com/go-skynet/go-llama.cpp"
)
type ModelLoader struct {
modelPath string
mu sync.Mutex
models map[string]*llama.LLama
gptmodels map[string]*gptj.GPTJ
gpt2models map[string]*gpt2.GPT2
gptstablelmmodels map[string]*gpt2.StableLM
promptsTemplates map[string]*template.Template
}
func NewModelLoader(modelPath string) *ModelLoader {
return &ModelLoader{
modelPath: modelPath,
gpt2models: make(map[string]*gpt2.GPT2),
gptmodels: make(map[string]*gptj.GPTJ),
gptstablelmmodels: make(map[string]*gpt2.StableLM),
models: make(map[string]*llama.LLama),
promptsTemplates: make(map[string]*template.Template),
}
}
func (ml *ModelLoader) ExistsInModelPath(s string) bool {
_, err := os.Stat(filepath.Join(ml.modelPath, s))
return err == nil
}
func (ml *ModelLoader) ListModels() ([]string, error) {
files, err := ioutil.ReadDir(ml.modelPath)
if err != nil {
return []string{}, err
}
models := []string{}
for _, file := range files {
// Skip templates, YAML and .keep files
if strings.HasSuffix(file.Name(), ".tmpl") || strings.HasSuffix(file.Name(), ".keep") || strings.HasSuffix(file.Name(), ".yaml") || strings.HasSuffix(file.Name(), ".yml") {
continue
}
models = append(models, file.Name())
}
return models, nil
}
func (ml *ModelLoader) TemplatePrefix(modelName string, in interface{}) (string, error) {
ml.mu.Lock()
defer ml.mu.Unlock()
m, ok := ml.promptsTemplates[modelName]
if !ok {
return "", fmt.Errorf("no prompt template available")
}
var buf bytes.Buffer
if err := m.Execute(&buf, in); err != nil {
return "", err
}
return buf.String(), nil
}
func (ml *ModelLoader) loadTemplateIfExists(modelName, modelFile string) error {
// Check if the template was already loaded
if _, ok := ml.promptsTemplates[modelName]; ok {
return nil
}
// Check if the model path exists
// skip any error here - we run anyway if a template is not exist
modelTemplateFile := fmt.Sprintf("%s.tmpl", modelName)
if !ml.ExistsInModelPath(modelTemplateFile) {
return nil
}
dat, err := os.ReadFile(filepath.Join(ml.modelPath, modelTemplateFile))
if err != nil {
return err
}
// Parse the template
tmpl, err := template.New("prompt").Parse(string(dat))
if err != nil {
return err
}
ml.promptsTemplates[modelName] = tmpl
return nil
}
func (ml *ModelLoader) LoadStableLMModel(modelName string) (*gpt2.StableLM, error) {
ml.mu.Lock()
defer ml.mu.Unlock()
// Check if we already have a loaded model
if !ml.ExistsInModelPath(modelName) {
return nil, fmt.Errorf("model does not exist")
}
if m, ok := ml.gptstablelmmodels[modelName]; ok {
log.Debug().Msgf("Model already loaded in memory: %s", modelName)
return m, nil
}
// Load the model and keep it in memory for later use
modelFile := filepath.Join(ml.modelPath, modelName)
log.Debug().Msgf("Loading model in memory from file: %s", modelFile)
model, err := gpt2.NewStableLM(modelFile)
if err != nil {
return nil, err
}
// If there is a prompt template, load it
if err := ml.loadTemplateIfExists(modelName, modelFile); err != nil {
return nil, err
}
ml.gptstablelmmodels[modelName] = model
return model, err
}
func (ml *ModelLoader) LoadGPT2Model(modelName string) (*gpt2.GPT2, error) {
ml.mu.Lock()
defer ml.mu.Unlock()
// Check if we already have a loaded model
if !ml.ExistsInModelPath(modelName) {
return nil, fmt.Errorf("model does not exist")
}
if m, ok := ml.gpt2models[modelName]; ok {
log.Debug().Msgf("Model already loaded in memory: %s", modelName)
return m, nil
}
// TODO: This needs refactoring, it's really bad to have it in here
// Check if we have a GPTStable model loaded instead - if we do we return an error so the API tries with StableLM
if _, ok := ml.gptstablelmmodels[modelName]; ok {
log.Debug().Msgf("Model is GPTStableLM: %s", modelName)
return nil, fmt.Errorf("this model is a GPTStableLM one")
}
// Load the model and keep it in memory for later use
modelFile := filepath.Join(ml.modelPath, modelName)
log.Debug().Msgf("Loading model in memory from file: %s", modelFile)
model, err := gpt2.New(modelFile)
if err != nil {
return nil, err
}
// If there is a prompt template, load it
if err := ml.loadTemplateIfExists(modelName, modelFile); err != nil {
return nil, err
}
ml.gpt2models[modelName] = model
return model, err
}
func (ml *ModelLoader) LoadGPTJModel(modelName string) (*gptj.GPTJ, error) {
ml.mu.Lock()
defer ml.mu.Unlock()
// Check if we already have a loaded model
if !ml.ExistsInModelPath(modelName) {
return nil, fmt.Errorf("model does not exist")
}
if m, ok := ml.gptmodels[modelName]; ok {
log.Debug().Msgf("Model already loaded in memory: %s", modelName)
return m, nil
}
// TODO: This needs refactoring, it's really bad to have it in here
// Check if we have a GPT2 model loaded instead - if we do we return an error so the API tries with GPT2
if _, ok := ml.gpt2models[modelName]; ok {
log.Debug().Msgf("Model is GPT2: %s", modelName)
return nil, fmt.Errorf("this model is a GPT2 one")
}
if _, ok := ml.gptstablelmmodels[modelName]; ok {
log.Debug().Msgf("Model is GPTStableLM: %s", modelName)
return nil, fmt.Errorf("this model is a GPTStableLM one")
}
// Load the model and keep it in memory for later use
modelFile := filepath.Join(ml.modelPath, modelName)
log.Debug().Msgf("Loading model in memory from file: %s", modelFile)
model, err := gptj.New(modelFile)
if err != nil {
return nil, err
}
// If there is a prompt template, load it
if err := ml.loadTemplateIfExists(modelName, modelFile); err != nil {
return nil, err
}
ml.gptmodels[modelName] = model
return model, err
}
func (ml *ModelLoader) LoadLLaMAModel(modelName string, opts ...llama.ModelOption) (*llama.LLama, error) {
ml.mu.Lock()
defer ml.mu.Unlock()
log.Debug().Msgf("Loading model name: %s", modelName)
// Check if we already have a loaded model
if !ml.ExistsInModelPath(modelName) {
return nil, fmt.Errorf("model does not exist")
}
if m, ok := ml.models[modelName]; ok {
log.Debug().Msgf("Model already loaded in memory: %s", modelName)
return m, nil
}
// TODO: This needs refactoring, it's really bad to have it in here
// Check if we have a GPTJ model loaded instead - if we do we return an error so the API tries with GPTJ
if _, ok := ml.gptmodels[modelName]; ok {
log.Debug().Msgf("Model is GPTJ: %s", modelName)
return nil, fmt.Errorf("this model is a GPTJ one")
}
if _, ok := ml.gpt2models[modelName]; ok {
log.Debug().Msgf("Model is GPT2: %s", modelName)
return nil, fmt.Errorf("this model is a GPT2 one")
}
if _, ok := ml.gptstablelmmodels[modelName]; ok {
log.Debug().Msgf("Model is GPTStableLM: %s", modelName)
return nil, fmt.Errorf("this model is a GPTStableLM one")
}
// Load the model and keep it in memory for later use
modelFile := filepath.Join(ml.modelPath, modelName)
log.Debug().Msgf("Loading model in memory from file: %s", modelFile)
model, err := llama.New(modelFile, opts...)
if err != nil {
return nil, err
}
// If there is a prompt template, load it
if err := ml.loadTemplateIfExists(modelName, modelFile); err != nil {
return nil, err
}
ml.models[modelName] = model
return model, err
}

6
prompt-templates/alpaca.tmpl
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@ -1,6 +0,0 @@
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{{.Input}}
### Response:

4
prompt-templates/ggml-gpt4all-j.tmpl
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@ -1,4 +0,0 @@
The prompt below is a question to answer, a task to complete, or a conversation to respond to; decide which and write an appropriate response.
### Prompt:
{{.Input}}
### Response:

1
prompt-templates/koala.tmpl
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@ -1 +0,0 @@
BEGINNING OF CONVERSATION: USER: {{.Input}} GPT:

6
prompt-templates/vicuna.tmpl
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@ -1,6 +0,0 @@
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{{.Input}}
### Response:

17
renovate.json
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@ -1,17 +0,0 @@
{
"$schema": "https://docs.renovatebot.com/renovate-schema.json",
"extends": [
"config:base"
],
"regexManagers": [
{
"fileMatch": [
"^Makefile$"
],
"matchStrings": [
"#\\s*renovate:\\s*datasource=(?<datasource>.*?) depName=(?<depName>.*?)( datasourceTemplate=(?<datasourceTemplate>.*?))?( packageNameTemplate=(?<packageNameTemplate>.*?))?( depNameTemplate=(?<depNameTemplate>.*?))?( valueTemplate=(?<currentValueTemplate>.*?))?( versioning=(?<versioning>.*?))?\\s+.+_VERSION=(?<currentValue>.*?)\\s"
],
"versioningTemplate": "{{#if versioning}}{{versioning}}{{/if}}"
}
]
}
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