mirror of
https://github.com/mudler/LocalAI.git
synced 2024-06-07 19:40:48 +00:00
Ettore Di Giacinto
GitHub
parent
7e2d101a46
commit
95eb72bfd3
27
.github/workflows/test-extra.yml
vendored
27
.github/workflows/test-extra.yml
vendored
|
|
@ -248,3 +248,30 @@ jobs:
|
|||
export PATH=$PATH:/opt/conda/bin
|
||||
make -C backend/python/vall-e-x
|
||||
make -C backend/python/vall-e-x test
|
||||
|
||||
tests-coqui:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Clone
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
submodules: true
|
||||
- name: Dependencies
|
||||
run: |
|
||||
sudo apt-get update
|
||||
sudo apt-get install build-essential ffmpeg
|
||||
curl https://repo.anaconda.com/pkgs/misc/gpgkeys/anaconda.asc | gpg --dearmor > conda.gpg && \
|
||||
sudo install -o root -g root -m 644 conda.gpg /usr/share/keyrings/conda-archive-keyring.gpg && \
|
||||
gpg --keyring /usr/share/keyrings/conda-archive-keyring.gpg --no-default-keyring --fingerprint 34161F5BF5EB1D4BFBBB8F0A8AEB4F8B29D82806 && \
|
||||
sudo /bin/bash -c 'echo "deb [arch=amd64 signed-by=/usr/share/keyrings/conda-archive-keyring.gpg] https://repo.anaconda.com/pkgs/misc/debrepo/conda stable main" > /etc/apt/sources.list.d/conda.list' && \
|
||||
sudo /bin/bash -c 'echo "deb [arch=amd64 signed-by=/usr/share/keyrings/conda-archive-keyring.gpg] https://repo.anaconda.com/pkgs/misc/debrepo/conda stable main" | tee -a /etc/apt/sources.list.d/conda.list' && \
|
||||
sudo apt-get update && \
|
||||
sudo apt-get install -y conda
|
||||
sudo apt-get install -y ca-certificates cmake curl patch espeak espeak-ng
|
||||
sudo rm -rfv /usr/bin/conda || true
|
||||
|
||||
- name: Test coqui
|
||||
run: |
|
||||
export PATH=$PATH:/opt/conda/bin
|
||||
make -C backend/python/coqui
|
||||
make -C backend/python/coqui test
|
||||
10
Dockerfile
10
Dockerfile
|
|
@ -13,7 +13,7 @@ ARG TARGETVARIANT
|
|||
|
||||
ENV BUILD_TYPE=${BUILD_TYPE}
|
||||
|
||||
ENV EXTERNAL_GRPC_BACKENDS="huggingface-embeddings:/build/backend/python/sentencetransformers/run.sh,petals:/build/backend/python/petals/run.sh,transformers:/build/backend/python/transformers/run.sh,sentencetransformers:/build/backend/python/sentencetransformers/run.sh,autogptq:/build/backend/python/autogptq/run.sh,bark:/build/backend/python/bark/run.sh,diffusers:/build/backend/python/diffusers/run.sh,exllama:/build/backend/python/exllama/run.sh,vall-e-x:/build/backend/python/vall-e-x/run.sh,vllm:/build/backend/python/vllm/run.sh,exllama2:/build/backend/python/exllama2/run.sh,transformers-musicgen:/build/backend/python/transformers-musicgen/run.sh"
|
||||
ENV EXTERNAL_GRPC_BACKENDS="coqui:/build/backend/python/coqui/run.sh,huggingface-embeddings:/build/backend/python/sentencetransformers/run.sh,petals:/build/backend/python/petals/run.sh,transformers:/build/backend/python/transformers/run.sh,sentencetransformers:/build/backend/python/sentencetransformers/run.sh,autogptq:/build/backend/python/autogptq/run.sh,bark:/build/backend/python/bark/run.sh,diffusers:/build/backend/python/diffusers/run.sh,exllama:/build/backend/python/exllama/run.sh,vall-e-x:/build/backend/python/vall-e-x/run.sh,vllm:/build/backend/python/vllm/run.sh,exllama2:/build/backend/python/exllama2/run.sh,transformers-musicgen:/build/backend/python/transformers-musicgen/run.sh"
|
||||
|
||||
ENV GALLERIES='[{"name":"model-gallery", "url":"github:go-skynet/model-gallery/index.yaml"}, {"url": "github:go-skynet/model-gallery/huggingface.yaml","name":"huggingface"}]'
|
||||
ARG GO_TAGS="stablediffusion tts"
|
||||
|
|
@ -69,10 +69,7 @@ RUN curl https://repo.anaconda.com/pkgs/misc/gpgkeys/anaconda.asc | gpg --dearmo
|
|||
ENV PATH="/root/.cargo/bin:${PATH}"
|
||||
RUN pip install --upgrade pip
|
||||
RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
|
||||
|
||||
|
||||
# \
|
||||
# ; fi
|
||||
RUN apt-get install -y espeak-ng espeak
|
||||
|
||||
###################################
|
||||
###################################
|
||||
|
|
@ -192,6 +189,9 @@ RUN if [ "${IMAGE_TYPE}" = "extras" ]; then \
|
|||
RUN if [ "${IMAGE_TYPE}" = "extras" ]; then \
|
||||
PATH=$PATH:/opt/conda/bin make -C backend/python/transformers-musicgen \
|
||||
; fi
|
||||
RUN if [ "${IMAGE_TYPE}" = "extras" ]; then \
|
||||
PATH=$PATH:/opt/conda/bin make -C backend/python/coqui \
|
||||
; fi
|
||||
|
||||
# Define the health check command
|
||||
HEALTHCHECK --interval=1m --timeout=10m --retries=10 \
|
||||
|
|
|
|||
2
Makefile
2
Makefile
|
|
@ -395,6 +395,7 @@ protogen-python:
|
|||
python3 -m grpc_tools.protoc -Ibackend/ --python_out=backend/python/exllama/ --grpc_python_out=backend/python/exllama/ backend/backend.proto
|
||||
python3 -m grpc_tools.protoc -Ibackend/ --python_out=backend/python/bark/ --grpc_python_out=backend/python/bark/ backend/backend.proto
|
||||
python3 -m grpc_tools.protoc -Ibackend/ --python_out=backend/python/diffusers/ --grpc_python_out=backend/python/diffusers/ backend/backend.proto
|
||||
python3 -m grpc_tools.protoc -Ibackend/ --python_out=backend/python/coqui/ --grpc_python_out=backend/python/coqui/ backend/backend.proto
|
||||
python3 -m grpc_tools.protoc -Ibackend/ --python_out=backend/python/vall-e-x/ --grpc_python_out=backend/python/vall-e-x/ backend/backend.proto
|
||||
python3 -m grpc_tools.protoc -Ibackend/ --python_out=backend/python/vllm/ --grpc_python_out=backend/python/vllm/ backend/backend.proto
|
||||
python3 -m grpc_tools.protoc -Ibackend/ --python_out=backend/python/petals/ --grpc_python_out=backend/python/petals/ backend/backend.proto
|
||||
|
|
@ -405,6 +406,7 @@ protogen-python:
|
|||
prepare-extra-conda-environments:
|
||||
$(MAKE) -C backend/python/autogptq
|
||||
$(MAKE) -C backend/python/bark
|
||||
$(MAKE) -C backend/python/coqui
|
||||
$(MAKE) -C backend/python/diffusers
|
||||
$(MAKE) -C backend/python/vllm
|
||||
$(MAKE) -C backend/python/sentencetransformers
|
||||
|
|
|
|||
54
README.md
54
README.md
|
|
@ -26,10 +26,6 @@
|
|||
|
||||
[](https://github.com/go-skynet/LocalAI/actions/workflows/test.yml)[](https://github.com/go-skynet/LocalAI/actions/workflows/release.yaml)[](https://github.com/go-skynet/LocalAI/actions/workflows/image.yml)[](https://github.com/go-skynet/LocalAI/actions/workflows/bump_deps.yaml)[](https://artifacthub.io/packages/search?repo=localai)
|
||||
|
||||
**LocalAI** is the free, Open Source OpenAI alternative. LocalAI act as a drop-in replacement REST API that’s compatible with OpenAI API specifications for local inferencing. It allows you to run LLMs, generate images, audio (and not only) locally or on-prem with consumer grade hardware, supporting multiple model families. Does not require GPU.
|
||||
|
||||
<p align="center"><b>Follow LocalAI </b></p>
|
||||
|
||||
<p align="center">
|
||||
<a href="https://twitter.com/LocalAI_API" target="blank">
|
||||
<img src="https://img.shields.io/twitter/follow/LocalAI_API?label=Follow: LocalAI_API&style=social" alt="Follow LocalAI_API"/>
|
||||
|
|
@ -38,38 +34,17 @@
|
|||
<img src="https://dcbadge.vercel.app/api/server/uJAeKSAGDy?style=flat-square&theme=default-inverted" alt="Join LocalAI Discord Community"/>
|
||||
</a>
|
||||
|
||||
<p align="center"><b>Connect with the Creator </b></p>
|
||||
|
||||
<p align="center">
|
||||
<a href="https://twitter.com/mudler_it" target="blank">
|
||||
<img src="https://img.shields.io/twitter/follow/mudler_it?label=Follow: mudler_it&style=social" alt="Follow mudler_it"/>
|
||||
</a>
|
||||
<a href='https://github.com/mudler'>
|
||||
<img alt="Follow on Github" src="https://img.shields.io/badge/Follow-mudler-black?logo=github&link=https%3A%2F%2Fgithub.com%2Fmudler">
|
||||
</a>
|
||||
</p>
|
||||
|
||||
<p align="center"><b>Share LocalAI Repository</b></p>
|
||||
|
||||
<p align="center">
|
||||
|
||||
<a href="https://twitter.com/intent/tweet?text=Check%20this%20GitHub%20repository%20out.%20LocalAI%20-%20Let%27s%20you%20easily%20run%20LLM%20locally.&url=https://github.com/go-skynet/LocalAI&hashtags=LocalAI,AI" target="blank">
|
||||
<img src="https://img.shields.io/twitter/follow/_LocalAI?label=Share Repo on Twitter&style=social" alt="Follow _LocalAI"/></a>
|
||||
<a href="https://t.me/share/url?text=Check%20this%20GitHub%20repository%20out.%20LocalAI%20-%20Let%27s%20you%20easily%20run%20LLM%20locally.&url=https://github.com/go-skynet/LocalAI" target="_blank"><img src="https://img.shields.io/twitter/url?label=Telegram&logo=Telegram&style=social&url=https://github.com/go-skynet/LocalAI" alt="Share on Telegram"/></a>
|
||||
<a href="https://api.whatsapp.com/send?text=Check%20this%20GitHub%20repository%20out.%20LocalAI%20-%20Let%27s%20you%20easily%20run%20LLM%20locally.%20https://github.com/go-skynet/LocalAI"><img src="https://img.shields.io/twitter/url?label=whatsapp&logo=whatsapp&style=social&url=https://github.com/go-skynet/LocalAI" /></a> <a href="https://www.reddit.com/submit?url=https://github.com/go-skynet/LocalAI&title=Check%20this%20GitHub%20repository%20out.%20LocalAI%20-%20Let%27s%20you%20easily%20run%20LLM%20locally.
|
||||
" target="blank">
|
||||
<img src="https://img.shields.io/twitter/url?label=Reddit&logo=Reddit&style=social&url=https://github.com/go-skynet/LocalAI" alt="Share on Reddit"/>
|
||||
</a> <a href="mailto:?subject=Check%20this%20GitHub%20repository%20out.%20LocalAI%20-%20Let%27s%20you%20easily%20run%20LLM%20locally.%3A%0Ahttps://github.com/go-skynet/LocalAI" target="_blank"><img src="https://img.shields.io/twitter/url?label=Gmail&logo=Gmail&style=social&url=https://github.com/go-skynet/LocalAI"/></a> <a href="https://www.buymeacoffee.com/mudler" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy Me A Coffee" height="23" width="100" style="border-radius:1px"></a>
|
||||
|
||||
</p>
|
||||
|
||||
## 💻 [Getting started](https://localai.io/basics/getting_started/index.html)
|
||||
**LocalAI** is the free, Open Source OpenAI alternative. LocalAI act as a drop-in replacement REST API that’s compatible with OpenAI API specifications for local inferencing. It allows you to run LLMs, generate images, audio (and not only) locally or on-prem with consumer grade hardware, supporting multiple model families. Does not require GPU.
|
||||
|
||||
## 🔥🔥 Hot topics / Roadmap
|
||||
|
||||
[Roadmap](https://github.com/mudler/LocalAI/issues?q=is%3Aissue+is%3Aopen+label%3Aroadmap)
|
||||
|
||||
🆕 New! [LLM finetuning guide](https://localai.io/advanced/fine-tuning/)
|
||||
- 🐸 Coqui: https://github.com/mudler/LocalAI/pull/1489
|
||||
- Inline templates: https://github.com/mudler/LocalAI/pull/1452
|
||||
- Mixtral: https://github.com/mudler/LocalAI/pull/1449
|
||||
- Img2vid https://github.com/mudler/LocalAI/pull/1442
|
||||
- Musicgen https://github.com/mudler/LocalAI/pull/1387
|
||||
|
||||
Hot topics (looking for contributors):
|
||||
- Backends v2: https://github.com/mudler/LocalAI/issues/1126
|
||||
|
|
@ -77,22 +52,7 @@ Hot topics (looking for contributors):
|
|||
|
||||
If you want to help and contribute, issues up for grabs: https://github.com/mudler/LocalAI/issues?q=is%3Aissue+is%3Aopen+label%3A%22up+for+grabs%22
|
||||
|
||||
|
||||
|
||||
<hr>
|
||||
|
||||
In a nutshell:
|
||||
|
||||
- Local, OpenAI drop-in alternative REST API. You own your data.
|
||||
- NO GPU required. NO Internet access is required either
|
||||
- Optional, GPU Acceleration is available in `llama.cpp`-compatible LLMs. See also the [build section](https://localai.io/basics/build/index.html).
|
||||
- Supports multiple models
|
||||
- 🏃 Once loaded the first time, it keep models loaded in memory for faster inference
|
||||
- ⚡ Doesn't shell-out, but uses C++ bindings for a faster inference and better performance.
|
||||
|
||||
LocalAI was created by [Ettore Di Giacinto](https://github.com/mudler/) and is a community-driven project, focused on making the AI accessible to anyone. Any contribution, feedback and PR is welcome!
|
||||
|
||||
Note that this started just as a [fun weekend project](https://localai.io/#backstory) in order to try to create the necessary pieces for a full AI assistant like `ChatGPT`: the community is growing fast and we are working hard to make it better and more stable. If you want to help, please consider contributing (see below)!
|
||||
## 💻 [Getting started](https://localai.io/basics/getting_started/index.html)
|
||||
|
||||
## 🚀 [Features](https://localai.io/features/)
|
||||
|
||||
|
|
|
|||
|
|
@ -53,7 +53,7 @@ dependencies:
|
|||
- mpmath==1.3.0
|
||||
- multidict==6.0.4
|
||||
- multiprocess==0.70.15
|
||||
- networkx==3.1
|
||||
- networkx
|
||||
- numpy==1.26.0
|
||||
- nvidia-cublas-cu12==12.1.3.1
|
||||
- nvidia-cuda-cupti-cu12==12.1.105
|
||||
|
|
@ -68,7 +68,7 @@ dependencies:
|
|||
- nvidia-nvjitlink-cu12==12.2.140
|
||||
- nvidia-nvtx-cu12==12.1.105
|
||||
- packaging==23.2
|
||||
- pandas==2.1.1
|
||||
- pandas
|
||||
- peft==0.5.0
|
||||
- git+https://github.com/bigscience-workshop/petals
|
||||
- protobuf==4.24.4
|
||||
|
|
@ -90,6 +90,7 @@ dependencies:
|
|||
- torchaudio==2.1.0
|
||||
- tqdm==4.66.1
|
||||
- transformers==4.34.0
|
||||
- TTS==0.22.0
|
||||
- triton==2.1.0
|
||||
- typing-extensions==4.8.0
|
||||
- tzdata==2023.3
|
||||
|
|
|
|||
|
|
@ -33,6 +33,7 @@ dependencies:
|
|||
- boto3==1.28.61
|
||||
- botocore==1.31.61
|
||||
- certifi==2023.7.22
|
||||
- TTS==0.22.0
|
||||
- charset-normalizer==3.3.0
|
||||
- datasets==2.14.5
|
||||
- sentence-transformers==2.2.2
|
||||
|
|
@ -53,10 +54,10 @@ dependencies:
|
|||
- mpmath==1.3.0
|
||||
- multidict==6.0.4
|
||||
- multiprocess==0.70.15
|
||||
- networkx==3.1
|
||||
- networkx
|
||||
- numpy==1.26.0
|
||||
- packaging==23.2
|
||||
- pandas==2.1.1
|
||||
- pandas
|
||||
- peft==0.5.0
|
||||
- git+https://github.com/bigscience-workshop/petals
|
||||
- protobuf==4.24.4
|
||||
|
|
|
|||
15
backend/python/coqui/Makefile
Normal file
15
backend/python/coqui/Makefile
Normal file
|
|
@ -0,0 +1,15 @@
|
|||
.PHONY: coqui
|
||||
coqui:
|
||||
$(MAKE) -C ../common-env/transformers
|
||||
|
||||
.PHONY: run
|
||||
run:
|
||||
@echo "Running coqui..."
|
||||
bash run.sh
|
||||
@echo "coqui run."
|
||||
|
||||
.PHONY: test
|
||||
test:
|
||||
@echo "Testing coqui..."
|
||||
bash test.sh
|
||||
@echo "coqui tested."
|
||||
11
backend/python/coqui/README.md
Normal file
11
backend/python/coqui/README.md
Normal file
|
|
@ -0,0 +1,11 @@
|
|||
# Creating a separate environment for ttsbark project
|
||||
|
||||
```
|
||||
make coqui
|
||||
```
|
||||
|
||||
# Testing the gRPC server
|
||||
|
||||
```
|
||||
make test
|
||||
```
|
||||
61
backend/python/coqui/backend_pb2.py
Normal file
61
backend/python/coqui/backend_pb2.py
Normal file
File diff suppressed because one or more lines are too long
363
backend/python/coqui/backend_pb2_grpc.py
Normal file
363
backend/python/coqui/backend_pb2_grpc.py
Normal file
|
|
@ -0,0 +1,363 @@
|
|||
# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
|
||||
"""Client and server classes corresponding to protobuf-defined services."""
|
||||
import grpc
|
||||
|
||||
import backend_pb2 as backend__pb2
|
||||
|
||||
|
||||
class BackendStub(object):
|
||||
"""Missing associated documentation comment in .proto file."""
|
||||
|
||||
def __init__(self, channel):
|
||||
"""Constructor.
|
||||
|
||||
Args:
|
||||
channel: A grpc.Channel.
|
||||
"""
|
||||
self.Health = channel.unary_unary(
|
||||
'/backend.Backend/Health',
|
||||
request_serializer=backend__pb2.HealthMessage.SerializeToString,
|
||||
response_deserializer=backend__pb2.Reply.FromString,
|
||||
)
|
||||
self.Predict = channel.unary_unary(
|
||||
'/backend.Backend/Predict',
|
||||
request_serializer=backend__pb2.PredictOptions.SerializeToString,
|
||||
response_deserializer=backend__pb2.Reply.FromString,
|
||||
)
|
||||
self.LoadModel = channel.unary_unary(
|
||||
'/backend.Backend/LoadModel',
|
||||
request_serializer=backend__pb2.ModelOptions.SerializeToString,
|
||||
response_deserializer=backend__pb2.Result.FromString,
|
||||
)
|
||||
self.PredictStream = channel.unary_stream(
|
||||
'/backend.Backend/PredictStream',
|
||||
request_serializer=backend__pb2.PredictOptions.SerializeToString,
|
||||
response_deserializer=backend__pb2.Reply.FromString,
|
||||
)
|
||||
self.Embedding = channel.unary_unary(
|
||||
'/backend.Backend/Embedding',
|
||||
request_serializer=backend__pb2.PredictOptions.SerializeToString,
|
||||
response_deserializer=backend__pb2.EmbeddingResult.FromString,
|
||||
)
|
||||
self.GenerateImage = channel.unary_unary(
|
||||
'/backend.Backend/GenerateImage',
|
||||
request_serializer=backend__pb2.GenerateImageRequest.SerializeToString,
|
||||
response_deserializer=backend__pb2.Result.FromString,
|
||||
)
|
||||
self.AudioTranscription = channel.unary_unary(
|
||||
'/backend.Backend/AudioTranscription',
|
||||
request_serializer=backend__pb2.TranscriptRequest.SerializeToString,
|
||||
response_deserializer=backend__pb2.TranscriptResult.FromString,
|
||||
)
|
||||
self.TTS = channel.unary_unary(
|
||||
'/backend.Backend/TTS',
|
||||
request_serializer=backend__pb2.TTSRequest.SerializeToString,
|
||||
response_deserializer=backend__pb2.Result.FromString,
|
||||
)
|
||||
self.TokenizeString = channel.unary_unary(
|
||||
'/backend.Backend/TokenizeString',
|
||||
request_serializer=backend__pb2.PredictOptions.SerializeToString,
|
||||
response_deserializer=backend__pb2.TokenizationResponse.FromString,
|
||||
)
|
||||
self.Status = channel.unary_unary(
|
||||
'/backend.Backend/Status',
|
||||
request_serializer=backend__pb2.HealthMessage.SerializeToString,
|
||||
response_deserializer=backend__pb2.StatusResponse.FromString,
|
||||
)
|
||||
|
||||
|
||||
class BackendServicer(object):
|
||||
"""Missing associated documentation comment in .proto file."""
|
||||
|
||||
def Health(self, request, context):
|
||||
"""Missing associated documentation comment in .proto file."""
|
||||
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
|
||||
context.set_details('Method not implemented!')
|
||||
raise NotImplementedError('Method not implemented!')
|
||||
|
||||
def Predict(self, request, context):
|
||||
"""Missing associated documentation comment in .proto file."""
|
||||
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
|
||||
context.set_details('Method not implemented!')
|
||||
raise NotImplementedError('Method not implemented!')
|
||||
|
||||
def LoadModel(self, request, context):
|
||||
"""Missing associated documentation comment in .proto file."""
|
||||
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
|
||||
context.set_details('Method not implemented!')
|
||||
raise NotImplementedError('Method not implemented!')
|
||||
|
||||
def PredictStream(self, request, context):
|
||||
"""Missing associated documentation comment in .proto file."""
|
||||
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
|
||||
context.set_details('Method not implemented!')
|
||||
raise NotImplementedError('Method not implemented!')
|
||||
|
||||
def Embedding(self, request, context):
|
||||
"""Missing associated documentation comment in .proto file."""
|
||||
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
|
||||
context.set_details('Method not implemented!')
|
||||
raise NotImplementedError('Method not implemented!')
|
||||
|
||||
def GenerateImage(self, request, context):
|
||||
"""Missing associated documentation comment in .proto file."""
|
||||
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
|
||||
context.set_details('Method not implemented!')
|
||||
raise NotImplementedError('Method not implemented!')
|
||||
|
||||
def AudioTranscription(self, request, context):
|
||||
"""Missing associated documentation comment in .proto file."""
|
||||
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
|
||||
context.set_details('Method not implemented!')
|
||||
raise NotImplementedError('Method not implemented!')
|
||||
|
||||
def TTS(self, request, context):
|
||||
"""Missing associated documentation comment in .proto file."""
|
||||
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
|
||||
context.set_details('Method not implemented!')
|
||||
raise NotImplementedError('Method not implemented!')
|
||||
|
||||
def TokenizeString(self, request, context):
|
||||
"""Missing associated documentation comment in .proto file."""
|
||||
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
|
||||
context.set_details('Method not implemented!')
|
||||
raise NotImplementedError('Method not implemented!')
|
||||
|
||||
def Status(self, request, context):
|
||||
"""Missing associated documentation comment in .proto file."""
|
||||
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
|
||||
context.set_details('Method not implemented!')
|
||||
raise NotImplementedError('Method not implemented!')
|
||||
|
||||
|
||||
def add_BackendServicer_to_server(servicer, server):
|
||||
rpc_method_handlers = {
|
||||
'Health': grpc.unary_unary_rpc_method_handler(
|
||||
servicer.Health,
|
||||
request_deserializer=backend__pb2.HealthMessage.FromString,
|
||||
response_serializer=backend__pb2.Reply.SerializeToString,
|
||||
),
|
||||
'Predict': grpc.unary_unary_rpc_method_handler(
|
||||
servicer.Predict,
|
||||
request_deserializer=backend__pb2.PredictOptions.FromString,
|
||||
response_serializer=backend__pb2.Reply.SerializeToString,
|
||||
),
|
||||
'LoadModel': grpc.unary_unary_rpc_method_handler(
|
||||
servicer.LoadModel,
|
||||
request_deserializer=backend__pb2.ModelOptions.FromString,
|
||||
response_serializer=backend__pb2.Result.SerializeToString,
|
||||
),
|
||||
'PredictStream': grpc.unary_stream_rpc_method_handler(
|
||||
servicer.PredictStream,
|
||||
request_deserializer=backend__pb2.PredictOptions.FromString,
|
||||
response_serializer=backend__pb2.Reply.SerializeToString,
|
||||
),
|
||||
'Embedding': grpc.unary_unary_rpc_method_handler(
|
||||
servicer.Embedding,
|
||||
request_deserializer=backend__pb2.PredictOptions.FromString,
|
||||
response_serializer=backend__pb2.EmbeddingResult.SerializeToString,
|
||||
),
|
||||
'GenerateImage': grpc.unary_unary_rpc_method_handler(
|
||||
servicer.GenerateImage,
|
||||
request_deserializer=backend__pb2.GenerateImageRequest.FromString,
|
||||
response_serializer=backend__pb2.Result.SerializeToString,
|
||||
),
|
||||
'AudioTranscription': grpc.unary_unary_rpc_method_handler(
|
||||
servicer.AudioTranscription,
|
||||
request_deserializer=backend__pb2.TranscriptRequest.FromString,
|
||||
response_serializer=backend__pb2.TranscriptResult.SerializeToString,
|
||||
),
|
||||
'TTS': grpc.unary_unary_rpc_method_handler(
|
||||
servicer.TTS,
|
||||
request_deserializer=backend__pb2.TTSRequest.FromString,
|
||||
response_serializer=backend__pb2.Result.SerializeToString,
|
||||
),
|
||||
'TokenizeString': grpc.unary_unary_rpc_method_handler(
|
||||
servicer.TokenizeString,
|
||||
request_deserializer=backend__pb2.PredictOptions.FromString,
|
||||
response_serializer=backend__pb2.TokenizationResponse.SerializeToString,
|
||||
),
|
||||
'Status': grpc.unary_unary_rpc_method_handler(
|
||||
servicer.Status,
|
||||
request_deserializer=backend__pb2.HealthMessage.FromString,
|
||||
response_serializer=backend__pb2.StatusResponse.SerializeToString,
|
||||
),
|
||||
}
|
||||
generic_handler = grpc.method_handlers_generic_handler(
|
||||
'backend.Backend', rpc_method_handlers)
|
||||
server.add_generic_rpc_handlers((generic_handler,))
|
||||
|
||||
|
||||
# This class is part of an EXPERIMENTAL API.
|
||||
class Backend(object):
|
||||
"""Missing associated documentation comment in .proto file."""
|
||||
|
||||
@staticmethod
|
||||
def Health(request,
|
||||
target,
|
||||
options=(),
|
||||
channel_credentials=None,
|
||||
call_credentials=None,
|
||||
insecure=False,
|
||||
compression=None,
|
||||
wait_for_ready=None,
|
||||
timeout=None,
|
||||
metadata=None):
|
||||
return grpc.experimental.unary_unary(request, target, '/backend.Backend/Health',
|
||||
backend__pb2.HealthMessage.SerializeToString,
|
||||
backend__pb2.Reply.FromString,
|
||||
options, channel_credentials,
|
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
|
||||
|
||||
@staticmethod
|
||||
def Predict(request,
|
||||
target,
|
||||
options=(),
|
||||
channel_credentials=None,
|
||||
call_credentials=None,
|
||||
insecure=False,
|
||||
compression=None,
|
||||
wait_for_ready=None,
|
||||
timeout=None,
|
||||
metadata=None):
|
||||
return grpc.experimental.unary_unary(request, target, '/backend.Backend/Predict',
|
||||
backend__pb2.PredictOptions.SerializeToString,
|
||||
backend__pb2.Reply.FromString,
|
||||
options, channel_credentials,
|
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
|
||||
|
||||
@staticmethod
|
||||
def LoadModel(request,
|
||||
target,
|
||||
options=(),
|
||||
channel_credentials=None,
|
||||
call_credentials=None,
|
||||
insecure=False,
|
||||
compression=None,
|
||||
wait_for_ready=None,
|
||||
timeout=None,
|
||||
metadata=None):
|
||||
return grpc.experimental.unary_unary(request, target, '/backend.Backend/LoadModel',
|
||||
backend__pb2.ModelOptions.SerializeToString,
|
||||
backend__pb2.Result.FromString,
|
||||
options, channel_credentials,
|
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
|
||||
|
||||
@staticmethod
|
||||
def PredictStream(request,
|
||||
target,
|
||||
options=(),
|
||||
channel_credentials=None,
|
||||
call_credentials=None,
|
||||
insecure=False,
|
||||
compression=None,
|
||||
wait_for_ready=None,
|
||||
timeout=None,
|
||||
metadata=None):
|
||||
return grpc.experimental.unary_stream(request, target, '/backend.Backend/PredictStream',
|
||||
backend__pb2.PredictOptions.SerializeToString,
|
||||
backend__pb2.Reply.FromString,
|
||||
options, channel_credentials,
|
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
|
||||
|
||||
@staticmethod
|
||||
def Embedding(request,
|
||||
target,
|
||||
options=(),
|
||||
channel_credentials=None,
|
||||
call_credentials=None,
|
||||
insecure=False,
|
||||
compression=None,
|
||||
wait_for_ready=None,
|
||||
timeout=None,
|
||||
metadata=None):
|
||||
return grpc.experimental.unary_unary(request, target, '/backend.Backend/Embedding',
|
||||
backend__pb2.PredictOptions.SerializeToString,
|
||||
backend__pb2.EmbeddingResult.FromString,
|
||||
options, channel_credentials,
|
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
|
||||
|
||||
@staticmethod
|
||||
def GenerateImage(request,
|
||||
target,
|
||||
options=(),
|
||||
channel_credentials=None,
|
||||
call_credentials=None,
|
||||
insecure=False,
|
||||
compression=None,
|
||||
wait_for_ready=None,
|
||||
timeout=None,
|
||||
metadata=None):
|
||||
return grpc.experimental.unary_unary(request, target, '/backend.Backend/GenerateImage',
|
||||
backend__pb2.GenerateImageRequest.SerializeToString,
|
||||
backend__pb2.Result.FromString,
|
||||
options, channel_credentials,
|
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
|
||||
|
||||
@staticmethod
|
||||
def AudioTranscription(request,
|
||||
target,
|
||||
options=(),
|
||||
channel_credentials=None,
|
||||
call_credentials=None,
|
||||
insecure=False,
|
||||
compression=None,
|
||||
wait_for_ready=None,
|
||||
timeout=None,
|
||||
metadata=None):
|
||||
return grpc.experimental.unary_unary(request, target, '/backend.Backend/AudioTranscription',
|
||||
backend__pb2.TranscriptRequest.SerializeToString,
|
||||
backend__pb2.TranscriptResult.FromString,
|
||||
options, channel_credentials,
|
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
|
||||
|
||||
@staticmethod
|
||||
def TTS(request,
|
||||
target,
|
||||
options=(),
|
||||
channel_credentials=None,
|
||||
call_credentials=None,
|
||||
insecure=False,
|
||||
compression=None,
|
||||
wait_for_ready=None,
|
||||
timeout=None,
|
||||
metadata=None):
|
||||
return grpc.experimental.unary_unary(request, target, '/backend.Backend/TTS',
|
||||
backend__pb2.TTSRequest.SerializeToString,
|
||||
backend__pb2.Result.FromString,
|
||||
options, channel_credentials,
|
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
|
||||
|
||||
@staticmethod
|
||||
def TokenizeString(request,
|
||||
target,
|
||||
options=(),
|
||||
channel_credentials=None,
|
||||
call_credentials=None,
|
||||
insecure=False,
|
||||
compression=None,
|
||||
wait_for_ready=None,
|
||||
timeout=None,
|
||||
metadata=None):
|
||||
return grpc.experimental.unary_unary(request, target, '/backend.Backend/TokenizeString',
|
||||
backend__pb2.PredictOptions.SerializeToString,
|
||||
backend__pb2.TokenizationResponse.FromString,
|
||||
options, channel_credentials,
|
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
|
||||
|
||||
@staticmethod
|
||||
def Status(request,
|
||||
target,
|
||||
options=(),
|
||||
channel_credentials=None,
|
||||
call_credentials=None,
|
||||
insecure=False,
|
||||
compression=None,
|
||||
wait_for_ready=None,
|
||||
timeout=None,
|
||||
metadata=None):
|
||||
return grpc.experimental.unary_unary(request, target, '/backend.Backend/Status',
|
||||
backend__pb2.HealthMessage.SerializeToString,
|
||||
backend__pb2.StatusResponse.FromString,
|
||||
options, channel_credentials,
|
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
|
||||
97
backend/python/coqui/coqui_server.py
Normal file
97
backend/python/coqui/coqui_server.py
Normal file
|
|
@ -0,0 +1,97 @@
|
|||
#!/usr/bin/env python3
|
||||
"""
|
||||
This is an extra gRPC server of LocalAI for Bark TTS
|
||||
"""
|
||||
from concurrent import futures
|
||||
import time
|
||||
import argparse
|
||||
import signal
|
||||
import sys
|
||||
import os
|
||||
import backend_pb2
|
||||
import backend_pb2_grpc
|
||||
|
||||
import torch
|
||||
from TTS.api import TTS
|
||||
|
||||
import grpc
|
||||
|
||||
|
||||
_ONE_DAY_IN_SECONDS = 60 * 60 * 24
|
||||
|
||||
# If MAX_WORKERS are specified in the environment use it, otherwise default to 1
|
||||
MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
|
||||
COQUI_LANGUAGE = os.environ.get('COQUI_LANGUAGE', 'en')
|
||||
|
||||
# Implement the BackendServicer class with the service methods
|
||||
class BackendServicer(backend_pb2_grpc.BackendServicer):
|
||||
"""
|
||||
BackendServicer is the class that implements the gRPC service
|
||||
"""
|
||||
def Health(self, request, context):
|
||||
return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
|
||||
def LoadModel(self, request, context):
|
||||
|
||||
# Get device
|
||||
device = "cuda" if request.CUDA else "cpu"
|
||||
|
||||
if not torch.cuda.is_available() and request.CUDA:
|
||||
return backend_pb2.Result(success=False, message="CUDA is not available")
|
||||
|
||||
# List available 🐸TTS models
|
||||
print(TTS().list_models())
|
||||
if os.path.isabs(request.AudioPath):
|
||||
self.AudioPath = request.AudioPath
|
||||
elif request.AudioPath and request.ModelFile != "" and not os.path.isabs(request.AudioPath):
|
||||
# get base path of modelFile
|
||||
modelFileBase = os.path.dirname(request.ModelFile)
|
||||
# modify LoraAdapter to be relative to modelFileBase
|
||||
self.AudioPath = os.path.join(modelFileBase, request.AudioPath)
|
||||
|
||||
try:
|
||||
print("Preparing models, please wait", file=sys.stderr)
|
||||
self.tts = TTS(request.Model).to(device)
|
||||
except Exception as err:
|
||||
return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
|
||||
# Implement your logic here for the LoadModel service
|
||||
# Replace this with your desired response
|
||||
return backend_pb2.Result(message="Model loaded successfully", success=True)
|
||||
|
||||
def TTS(self, request, context):
|
||||
try:
|
||||
self.tts.tts_to_file(text=request.text, speaker_wav=self.AudioPath, language=COQUI_LANGUAGE, file_path=request.dst)
|
||||
except Exception as err:
|
||||
return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
|
||||
return backend_pb2.Result(success=True)
|
||||
|
||||
def serve(address):
|
||||
server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS))
|
||||
backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
|
||||
server.add_insecure_port(address)
|
||||
server.start()
|
||||
print("Server started. Listening on: " + address, file=sys.stderr)
|
||||
|
||||
# Define the signal handler function
|
||||
def signal_handler(sig, frame):
|
||||
print("Received termination signal. Shutting down...")
|
||||
server.stop(0)
|
||||
sys.exit(0)
|
||||
|
||||
# Set the signal handlers for SIGINT and SIGTERM
|
||||
signal.signal(signal.SIGINT, signal_handler)
|
||||
signal.signal(signal.SIGTERM, signal_handler)
|
||||
|
||||
try:
|
||||
while True:
|
||||
time.sleep(_ONE_DAY_IN_SECONDS)
|
||||
except KeyboardInterrupt:
|
||||
server.stop(0)
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="Run the gRPC server.")
|
||||
parser.add_argument(
|
||||
"--addr", default="localhost:50051", help="The address to bind the server to."
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
serve(args.addr)
|
||||
14
backend/python/coqui/run.sh
Executable file
14
backend/python/coqui/run.sh
Executable file
|
|
@ -0,0 +1,14 @@
|
|||
#!/bin/bash
|
||||
|
||||
##
|
||||
## A bash script wrapper that runs the ttsbark server with conda
|
||||
|
||||
export PATH=$PATH:/opt/conda/bin
|
||||
|
||||
# Activate conda environment
|
||||
source activate transformers
|
||||
|
||||
# get the directory where the bash script is located
|
||||
DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
|
||||
|
||||
python $DIR/coqui_server.py $@
|
||||
82
backend/python/coqui/test.py
Normal file
82
backend/python/coqui/test.py
Normal file
|
|
@ -0,0 +1,82 @@
|
|||
"""
|
||||
A test script to test the gRPC service
|
||||
"""
|
||||
import unittest
|
||||
import subprocess
|
||||
import time
|
||||
import backend_pb2
|
||||
import backend_pb2_grpc
|
||||
|
||||
import grpc
|
||||
|
||||
|
||||
class TestBackendServicer(unittest.TestCase):
|
||||
"""
|
||||
TestBackendServicer is the class that tests the gRPC service
|
||||
"""
|
||||
def setUp(self):
|
||||
"""
|
||||
This method sets up the gRPC service by starting the server
|
||||
"""
|
||||
self.service = subprocess.Popen(["python3", "coqui_server.py", "--addr", "localhost:50051"])
|
||||
time.sleep(10)
|
||||
|
||||
def tearDown(self) -> None:
|
||||
"""
|
||||
This method tears down the gRPC service by terminating the server
|
||||
"""
|
||||
self.service.terminate()
|
||||
self.service.wait()
|
||||
|
||||
def test_server_startup(self):
|
||||
"""
|
||||
This method tests if the server starts up successfully
|
||||
"""
|
||||
try:
|
||||
self.setUp()
|
||||
with grpc.insecure_channel("localhost:50051") as channel:
|
||||
stub = backend_pb2_grpc.BackendStub(channel)
|
||||
response = stub.Health(backend_pb2.HealthMessage())
|
||||
self.assertEqual(response.message, b'OK')
|
||||
except Exception as err:
|
||||
print(err)
|
||||
self.fail("Server failed to start")
|
||||
finally:
|
||||
self.tearDown()
|
||||
|
||||
def test_load_model(self):
|
||||
"""
|
||||
This method tests if the model is loaded successfully
|
||||
"""
|
||||
try:
|
||||
self.setUp()
|
||||
with grpc.insecure_channel("localhost:50051") as channel:
|
||||
stub = backend_pb2_grpc.BackendStub(channel)
|
||||
response = stub.LoadModel(backend_pb2.ModelOptions(Model="tts_models/en/vctk/vits"))
|
||||
print(response)
|
||||
self.assertTrue(response.success)
|
||||
self.assertEqual(response.message, "Model loaded successfully")
|
||||
except Exception as err:
|
||||
print(err)
|
||||
self.fail("LoadModel service failed")
|
||||
finally:
|
||||
self.tearDown()
|
||||
|
||||
def test_tts(self):
|
||||
"""
|
||||
This method tests if the embeddings are generated successfully
|
||||
"""
|
||||
try:
|
||||
self.setUp()
|
||||
with grpc.insecure_channel("localhost:50051") as channel:
|
||||
stub = backend_pb2_grpc.BackendStub(channel)
|
||||
response = stub.LoadModel(backend_pb2.ModelOptions(Model="tts_models/en/vctk/vits"))
|
||||
self.assertTrue(response.success)
|
||||
tts_request = backend_pb2.TTSRequest(text="80s TV news production music hit for tonight's biggest story")
|
||||
tts_response = stub.TTS(tts_request)
|
||||
self.assertIsNotNone(tts_response)
|
||||
except Exception as err:
|
||||
print(err)
|
||||
self.fail("TTS service failed")
|
||||
finally:
|
||||
self.tearDown()
|
||||
11
backend/python/coqui/test.sh
Normal file
11
backend/python/coqui/test.sh
Normal file
|
|
@ -0,0 +1,11 @@
|
|||
#!/bin/bash
|
||||
##
|
||||
## A bash script wrapper that runs the bark server with conda
|
||||
|
||||
# Activate conda environment
|
||||
source activate transformers
|
||||
|
||||
# get the directory where the bash script is located
|
||||
DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
|
||||
|
||||
python -m unittest $DIR/test.py
|
||||
|
|
@ -24,8 +24,6 @@ title = "LocalAI"
|
|||
|
||||
**LocalAI** is the free, Open Source OpenAI alternative. LocalAI act as a drop-in replacement REST API that's compatible with OpenAI API specifications for local inferencing. It allows you to run LLMs, generate images, audio (and not only) locally or on-prem with consumer grade hardware, supporting multiple model families that are compatible with the ggml format. Does not require GPU. It is maintained by [mudler](https://github.com/mudler).
|
||||
|
||||
<p align="center"><b>Follow LocalAI </b></p>
|
||||
|
||||
<p align="center">
|
||||
<a href="https://twitter.com/LocalAI_API" target="blank">
|
||||
<img src="https://img.shields.io/twitter/follow/LocalAI_API?label=Follow: LocalAI_API&style=social" alt="Follow LocalAI_API"/>
|
||||
|
|
@ -34,33 +32,6 @@ title = "LocalAI"
|
|||
<img src="https://dcbadge.vercel.app/api/server/uJAeKSAGDy?style=flat-square&theme=default-inverted" alt="Join LocalAI Discord Community"/>
|
||||
</a>
|
||||
|
||||
<p align="center"><b>Connect with the Creator </b></p>
|
||||
|
||||
<p align="center">
|
||||
<a href="https://twitter.com/mudler_it" target="blank">
|
||||
<img src="https://img.shields.io/twitter/follow/mudler_it?label=Follow: mudler_it&style=social" alt="Follow mudler_it"/>
|
||||
</a>
|
||||
<a href='https://github.com/mudler'>
|
||||
<img alt="Follow on Github" src="https://img.shields.io/badge/Follow-mudler-black?logo=github&link=https%3A%2F%2Fgithub.com%2Fmudler">
|
||||
</a>
|
||||
</p>
|
||||
|
||||
<p align="center"><b>Share LocalAI Repository</b></p>
|
||||
|
||||
<p align="center">
|
||||
|
||||
<a href="https://twitter.com/intent/tweet?text=Check%20this%20GitHub%20repository%20out.%20LocalAI%20-%20Let%27s%20you%20easily%20run%20LLM%20locally.&url=https://github.com/go-skynet/LocalAI&hashtags=LocalAI,AI" target="blank">
|
||||
<img src="https://img.shields.io/twitter/follow/_LocalAI?label=Share Repo on Twitter&style=social" alt="Follow _LocalAI"/></a>
|
||||
<a href="https://t.me/share/url?text=Check%20this%20GitHub%20repository%20out.%20LocalAI%20-%20Let%27s%20you%20easily%20run%20LLM%20locally.&url=https://github.com/go-skynet/LocalAI" target="_blank"><img src="https://img.shields.io/twitter/url?label=Telegram&logo=Telegram&style=social&url=https://github.com/go-skynet/LocalAI" alt="Share on Telegram"/></a>
|
||||
<a href="https://api.whatsapp.com/send?text=Check%20this%20GitHub%20repository%20out.%20LocalAI%20-%20Let%27s%20you%20easily%20run%20LLM%20locally.%20https://github.com/go-skynet/LocalAI"><img src="https://img.shields.io/twitter/url?label=whatsapp&logo=whatsapp&style=social&url=https://github.com/go-skynet/LocalAI" /></a> <a href="https://www.reddit.com/submit?url=https://github.com/go-skynet/LocalAI&title=Check%20this%20GitHub%20repository%20out.%20LocalAI%20-%20Let%27s%20you%20easily%20run%20LLM%20locally.
|
||||
" target="blank">
|
||||
<img src="https://img.shields.io/twitter/url?label=Reddit&logo=Reddit&style=social&url=https://github.com/go-skynet/LocalAI" alt="Share on Reddit"/>
|
||||
</a> <a href="mailto:?subject=Check%20this%20GitHub%20repository%20out.%20LocalAI%20-%20Let%27s%20you%20easily%20run%20LLM%20locally.%3A%0Ahttps://github.com/go-skynet/LocalAI" target="_blank"><img src="https://img.shields.io/twitter/url?label=Gmail&logo=Gmail&style=social&url=https://github.com/go-skynet/LocalAI"/></a> <a href="https://www.buymeacoffee.com/mudler" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy Me A Coffee" height="23" width="100" style="border-radius:1px"></a>
|
||||
|
||||
</p>
|
||||
|
||||
<hr>
|
||||
|
||||
In a nutshell:
|
||||
|
||||
- Local, OpenAI drop-in alternative REST API. You own your data.
|
||||
|
|
@ -70,9 +41,10 @@ In a nutshell:
|
|||
- 🏃 Once loaded the first time, it keep models loaded in memory for faster inference
|
||||
- ⚡ Doesn't shell-out, but uses C++ bindings for a faster inference and better performance.
|
||||
|
||||
LocalAI was created by [Ettore Di Giacinto](https://github.com/mudler/) and is a community-driven project, focused on making the AI accessible to anyone. Any contribution, feedback and PR is welcome!
|
||||
LocalAI is focused on making the AI accessible to anyone. Any contribution, feedback and PR is welcome!
|
||||
|
||||
Note that this started just as a fun weekend project by [mudler](https://github.com/mudler) in order to try to create the necessary pieces for a full AI assistant like `ChatGPT`: the community is growing fast and we are working hard to make it better and more stable. If you want to help, please consider contributing (see below)!
|
||||
|
||||
Note that this started just as a [fun weekend project](https://localai.io/#backstory) in order to try to create the necessary pieces for a full AI assistant like `ChatGPT`: the community is growing fast and we are working hard to make it better and more stable. If you want to help, please consider contributing (see below)!
|
||||
|
||||
## 🚀 Features
|
||||
|
||||
|
|
@ -86,19 +58,6 @@ Note that this started just as a [fun weekend project](https://localai.io/#backs
|
|||
- 🖼️ [Download Models directly from Huggingface ](https://localai.io/models/)
|
||||
- 🆕 [Vision API](https://localai.io/features/gpt-vision/)
|
||||
|
||||
|
||||
## 🔥🔥 Hot topics / Roadmap
|
||||
|
||||
[Roadmap](https://github.com/mudler/LocalAI/issues?q=is%3Aissue+is%3Aopen+label%3Aroadmap)
|
||||
|
||||
🆕 New! [LLM finetuning guide](https://localai.io/advanced/fine-tuning/)
|
||||
|
||||
Hot topics (looking for contributors):
|
||||
- Backends v2: https://github.com/mudler/LocalAI/issues/1126
|
||||
- Improving UX v2: https://github.com/mudler/LocalAI/issues/1373
|
||||
|
||||
If you want to help and contribute, issues up for grabs: https://github.com/mudler/LocalAI/issues?q=is%3Aissue+is%3Aopen+label%3A%22up+for+grabs%22
|
||||
|
||||
## How does it work?
|
||||
|
||||
LocalAI is an API written in Go that serves as an OpenAI shim, enabling software already developed with OpenAI SDKs to seamlessly integrate with LocalAI. It can be effortlessly implemented as a substitute, even on consumer-grade hardware. This capability is achieved by employing various C++ backends, including [ggml](https://github.com/ggerganov/ggml), to perform inference on LLMs using both CPU and, if desired, GPU. Internally LocalAI backends are just gRPC server, indeed you can specify and build your own gRPC server and extend LocalAI in runtime as well. It is possible to specify external gRPC server and/or binaries that LocalAI will manage internally.
|
||||
|
|
@ -139,6 +98,8 @@ LocalAI couldn't have been built without the help of great software already avai
|
|||
- https://github.com/rhasspy/piper
|
||||
- https://github.com/cmp-nct/ggllm.cpp
|
||||
|
||||
|
||||
|
||||
## Backstory
|
||||
|
||||
As much as typical open source projects starts, I, [mudler](https://github.com/mudler/), was fiddling around with [llama.cpp](https://github.com/ggerganov/llama.cpp) over my long nights and wanted to have a way to call it from `go`, as I am a Golang developer and use it extensively. So I've created `LocalAI` (or what was initially known as `llama-cli`) and added an API to it.
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user