* feat(llama.cpp): support lora with scale
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(llama.cpp): support yarn
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* wip
* wip
* Make it functional
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* wip
* Small fixups
* do not inject space on role encoding, encode img at beginning of messages
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add examples/config defaults
* Add include dir of current source dir
* cleanup
* fixes
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fixups
* Revert "fixups"
This reverts commit f1a4731cca.
* fixes
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(autogptq): add a separate conda environment for autogptq (#1137)
**Description**
This PR related to #1117
**Notes for Reviewers**
Here we lock down the version of the dependencies. Make sure it can be
used all the time without failed if the version of dependencies were
upgraded.
I change the order of importing packages according to the pylint, and no
change the logic of code. It should be ok.
I will do more investigate on writing some test cases for every backend.
I can run the service in my environment, but there is not exist a way to
test it. So, I am not confident on it.
Add a README.md in the `grpc` root. This is the common commands for
creating `conda` environment. And it can be used to the reference file
for creating extral gRPC backend document.
Signed-off-by: GitHub <noreply@github.com>
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* [Extra backend] Add seperate environment for ttsbark (#1141)
**Description**
This PR relates to #1117
**Notes for Reviewers**
Same to the latest PR:
* The code is also changed, but only the order of the import package
parts. And some code comments are also added.
* Add a configuration of the `conda` environment
* Add a simple test case for testing if the service can be startup in
current `conda` environment. It is succeed in VSCode, but the it is not
out of box on terminal. So, it is hard to say the test case really
useful.
**[Signed
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* feat(conda): add make target and entrypoints for the dockerfile
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(conda): Add seperate conda env for diffusers (#1145)
**Description**
This PR relates to #1117
**Notes for Reviewers**
* Add `conda` env `diffusers.yml`
* Add Makefile to create it automatically
* Add `run.sh` to support running as a extra backend
* Also adding it to the main Dockerfile
* Add make command in the root Makefile
* Testing the server, it can start up under the env
Signed-off-by: GitHub <noreply@github.com>
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(conda):Add seperate env for vllm (#1148)
**Description**
This PR is related to #1117
**Notes for Reviewers**
* The gRPC server can be started as normal
* The test case can be triggered in VSCode
* Same to other this kind of PRs, add `vllm.yml` Makefile and add
`run.sh` to the main Dockerfile, and command to the main Makefile
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* feat(conda):Add seperate env for huggingface (#1146)
**Description**
This PR is related to #1117
**Notes for Reviewers**
* Add conda env `huggingface.yml`
* Change the import order, and also remove the no-used packages
* Add `run.sh` and `make command` to the main Dockerfile and Makefile
* Add test cases for it. It can be triggered and succeed under VSCode
Python extension but it is hang by using `python -m unites
test_huggingface.py` in the terminal
```
Running tests (unittest): /workspaces/LocalAI/extra/grpc/huggingface
Running tests: /workspaces/LocalAI/extra/grpc/huggingface/test_huggingface.py::TestBackendServicer::test_embedding
/workspaces/LocalAI/extra/grpc/huggingface/test_huggingface.py::TestBackendServicer::test_load_model
/workspaces/LocalAI/extra/grpc/huggingface/test_huggingface.py::TestBackendServicer::test_server_startup
./test_huggingface.py::TestBackendServicer::test_embedding Passed
./test_huggingface.py::TestBackendServicer::test_load_model Passed
./test_huggingface.py::TestBackendServicer::test_server_startup Passed
Total number of tests expected to run: 3
Total number of tests run: 3
Total number of tests passed: 3
Total number of tests failed: 0
Total number of tests failed with errors: 0
Total number of tests skipped: 0
Finished running tests!
```
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* feat(conda): Add the seperate conda env for VALL-E X (#1147)
**Description**
This PR is related to #1117
**Notes for Reviewers**
* The gRPC server cannot start up
```
(ttsvalle) @Aisuko ➜ /workspaces/LocalAI (feat/vall-e-x) $ /opt/conda/envs/ttsvalle/bin/python /workspaces/LocalAI/extra/grpc/vall-e-x/ttsvalle.py
Traceback (most recent call last):
File "/workspaces/LocalAI/extra/grpc/vall-e-x/ttsvalle.py", line 14, in <module>
from utils.generation import SAMPLE_RATE, generate_audio, preload_models
ModuleNotFoundError: No module named 'utils'
```
The installation steps follow
https://github.com/Plachtaa/VALL-E-X#-installation below:
* Under the `ttsvalle` conda env
```
git clone https://github.com/Plachtaa/VALL-E-X.git
cd VALL-E-X
pip install -r requirements.txt
```
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Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix: set image type
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(conda):Add seperate conda env for exllama (#1149)
Add seperate env for exllama
Signed-off-by: Aisuko <urakiny@gmail.com>
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Setup conda
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Set image_type arg
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* ci: prepare only conda env in tests
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Dockerfile: comment manual pip calls
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* conda: add conda to PATH
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fixes
* add shebang
* Fixups
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* file perms
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* debug
* Install new conda in the worker
* Disable GPU tests for now until the worker is back
* Rename workflows
* debug
* Fixup conda install
* fixup(wrapper): pass args
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: GitHub <noreply@github.com>
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Signed-off-by: Aisuko <urakiny@gmail.com>
Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
Co-authored-by: Aisuko <urakiny@gmail.com>
This change facilitates users working behind corporate firewalls or proxies. By allowing the integration of custom CA certificates, users can handle SSL connections that are intercepted by company infrastructure.
* ci: run only cublas on selfhosted
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* debug
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* update git
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* change testing embeddings model link
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fix backend/cpp/llama CMakeList.txt on OSX - detect OSX and use homebrew libraries
* sneak a logging fix in too for gallery debugging
* additional logging
* wip: llama.cpp c++ gRPC server
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* make it work, attach it to the build process
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* update deps
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix: add protobuf dep
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* try fix protobuf on cmake
* cmake: workarounds
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* add packages
* cmake: use fixed version of grpc
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* cmake(grpc): install locally
* install grpc
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* install required deps for grpc on debian bullseye
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* debug
* debug
* Fixups
* no need to install cmake manually
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* ci: fixup macOS
* use brew whenever possible
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* macOS fixups
* debug
* fix container build
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* workaround
* try mac
https://stackoverflow.com/questions/23905661/on-mac-g-clang-fails-to-search-usr-local-include-and-usr-local-lib-by-def
* Disable temp. arm64 docker image builds
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
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[github.com/nomic-ai/gpt4all/gpt4all-bindings/golang](https://togithub.com/nomic-ai/gpt4all)
| require | digest | `10f9b49` -> `22de3c5` |
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Adding subcommands to do certain actions directly from the command line.
I'm starting with the models subcommand allowing you to list models from
your galleries and install them.
This PR partially fixes#816
My intention is to keep adding other subcommands, but I think this is a
good start, and I think this already provides value.
Also, I added a new dependency to generate the progress bar in the
command line, it is not "needed" but I think is a nice to have to have a
cooler interface.
Here is a screenshot:

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This PR contains the following updates:
| Package | Type | Update | Change |
|---|---|---|---|
|
[github.com/go-skynet/go-llama.cpp](https://togithub.com/go-skynet/go-llama.cpp)
| require | digest | `1676dcd` -> `aeba71e` |
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This PR contains the following updates:
| Package | Type | Update | Change |
|---|---|---|---|
| [google.golang.org/grpc](https://togithub.com/grpc/grpc-go) | require
| patch | `v1.58.2` -> `v1.58.3` |
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### Release Notes
<details>
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[Compare
Source](https://togithub.com/grpc/grpc-go/compare/v1.58.2...v1.58.3)
### Security
- server: prohibit more than MaxConcurrentStreams handlers from running
at once (CVE-2023-44487)
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This PR bundles together two unrelated features:
1. Model Gallery improvements - specifically, the ability to follow
".ref" gallery links (which I made up for this specific application) to
an actual gallery yaml file (in order to have stable URLs) and the
ability to load self-contained configurations, rather than always using
a base.yaml + overrides. This is groundwork for my python-based
huggingface scraper.
2. A while ago I introduced some Insomnia request templates for people
to use. Unfortunately, Insomnia has decided to tank their product... So
I've personally switched to using
[bruno](https://github.com/usebruno/bruno/). Corresponding equivalent
files that I use for my testing have been added. Just open the folder
from bruno and everything will work. No import process required.
---------
Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
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This PR contains the following updates:
| Package | Type | Update | Change |
|---|---|---|---|
| [github.com/onsi/ginkgo/v2](https://togithub.com/onsi/ginkgo) |
require | minor | `v2.12.1` -> `v2.13.0` |
---
### Release Notes
<details>
<summary>onsi/ginkgo (github.com/onsi/ginkgo/v2)</summary>
### [`v2.13.0`](https://togithub.com/onsi/ginkgo/releases/tag/v2.13.0)
[Compare
Source](https://togithub.com/onsi/ginkgo/compare/v2.12.1...v2.13.0)
#### 2.13.0
##### Features
Add PreviewSpect() to enable programmatic preview access to the suite
report (fixes
[#​1225](https://togithub.com/onsi/ginkgo/issues/1225))
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**Description**
This PR fixes lack of HIPBLAS support in LocalAI.
**Notes for Reviewers**
This PR builds on https://github.com/go-skynet/go-llama.cpp/pull/235 to
enable ROCm/HIPBLAS support for gguf models running under llama.cpp
backend (not the stable ggml one). It can be enabled by using
BUILD_TYPE=hipblas. This was tested on a gfx1100 card, but should work
for gfx900,gfx1030 and other cards. Card support can be set with
AMDGPU_TARGETS environment variable.
**[Signed
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Signed-off-by: 65a <65a@63bit.net>
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This PR contains the following updates:
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**Description**
This PR is related to #105
**Notes for Reviewers**
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Signed-off-by: Aisuko <urakiny@gmail.com>
Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
This particularly useful to set AWQ
**Description**
Follow up of #1015
**Notes for Reviewers**
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Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
**Description**
This PR fixes #na
**Notes for Reviewers**
n/a
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This PR contains the following updates:
| Package | Type | Update | Change |
|---|---|---|---|
| [google.golang.org/grpc](https://togithub.com/grpc/grpc-go) | require
| patch | `v1.58.1` -> `v1.58.2` |
---
### Release Notes
<details>
<summary>grpc/grpc-go (google.golang.org/grpc)</summary>
### [`v1.58.2`](https://togithub.com/grpc/grpc-go/releases/tag/v1.58.2):
Release 1.58.2
[Compare
Source](https://togithub.com/grpc/grpc-go/compare/v1.58.1...v1.58.2)
### Bug Fixes
- balancer/weighted_round_robin: fix ticker leak on update
A new ticker is created every time there is an update of addresses or
configuration, but was not properly stopped. This change stops the
ticker when it is no longer needed.
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|---|---|---|---|
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[github.com/nomic-ai/gpt4all/gpt4all-bindings/golang](https://togithub.com/nomic-ai/gpt4all)
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|---|---|---|---|
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This PR cleans up the `chatbot-ui`/`-manual` examples:
- Fixes `Dockerfile` vs `docker-compose` confusion
- Makes it clear where to view the web UI in `## Run` sections
---------
Signed-off-by: James Braza <jamesbraza@gmail.com>
[](https://renovatebot.com)
This PR contains the following updates:
| Package | Type | Update | Change |
|---|---|---|---|
| [google.golang.org/grpc](https://togithub.com/grpc/grpc-go) | require
| minor | `v1.57.0` -> `v1.58.1` |
---
### Release Notes
<details>
<summary>grpc/grpc-go (google.golang.org/grpc)</summary>
### [`v1.58.1`](https://togithub.com/grpc/grpc-go/releases/tag/v1.58.1):
Release 1.58.1
[Compare
Source](https://togithub.com/grpc/grpc-go/compare/v1.58.0...v1.58.1)
### Bug Fixes
- grpc: fix a bug that was decrementing active RPC count too early for
streaming RPCs; leading to channel moving to IDLE even though it had
open streams
- grpc: fix a bug where transports were not being closed upon channel
entering IDLE
### [`v1.58.0`](https://togithub.com/grpc/grpc-go/releases/tag/v1.58.0):
Release 1.58.0
[Compare
Source](https://togithub.com/grpc/grpc-go/compare/v1.57.0...v1.58.0)
### API Changes
See [#​6472](https://togithub.com/grpc/grpc-go/issues/6472) for
details about these changes.
- balancer: add `StateListener` to `NewSubConnOptions` for `SubConn`
state updates and deprecate `Balancer.UpdateSubConnState`
([#​6481](https://togithub.com/grpc/grpc-go/issues/6481))
- `UpdateSubConnState` will be deleted in the future.
- balancer: add `SubConn.Shutdown` and deprecate
`Balancer.RemoveSubConn`
([#​6493](https://togithub.com/grpc/grpc-go/issues/6493))
- `RemoveSubConn` will be deleted in the future.
- resolver: remove deprecated `AddressType`
([#​6451](https://togithub.com/grpc/grpc-go/issues/6451))
- This was previously used as a signal to enable the "grpclb" load
balancing policy, and to pass LB addresses to the policy. Instead,
`balancer/grpclb/state.Set()` should be used to add these addresses to
the name resolver's output. The built-in "dns" name resolver already
does this.
- resolver: add new field `Endpoints` to `State` and deprecate
`Addresses`
([#​6471](https://togithub.com/grpc/grpc-go/issues/6471))
- `Addresses` will be deleted in the future.
### New Features
- balancer/leastrequest: Add experimental support for least request LB
policy and least request configured as a custom xDS policy
([#​6510](https://togithub.com/grpc/grpc-go/issues/6510),
[#​6517](https://togithub.com/grpc/grpc-go/issues/6517))
- Set `GRPC_EXPERIMENTAL_ENABLE_LEAST_REQUEST=true` to enable
- stats: Add an RPC event for blocking caused by the LB policy's picker
([#​6422](https://togithub.com/grpc/grpc-go/issues/6422))
### Bug Fixes
- clusterresolver: fix deadlock when dns resolver responds inline with
update or error at build time
([#​6563](https://togithub.com/grpc/grpc-go/issues/6563))
- grpc: fix a bug where the channel could erroneously report
`TRANSIENT_FAILURE` when actually moving to `IDLE`
([#​6497](https://togithub.com/grpc/grpc-go/issues/6497))
- balancergroup: do not cache closed sub-balancers by default; affects
`rls`, `weightedtarget` and `clustermanager` LB policies
([#​6523](https://togithub.com/grpc/grpc-go/issues/6523))
- client: fix a bug that prevented detection of RPC status in
trailers-only RPC responses when using `ClientStream.Header()`, and
prevented retry of the RPC
([#​6557](https://togithub.com/grpc/grpc-go/issues/6557))
### Performance Improvements
- client & server: Add experimental `[With]SharedWriteBuffer` to improve
performance by reducing allocations when sending RPC messages. (Disabled
by default.)
([#​6309](https://togithub.com/grpc/grpc-go/issues/6309))
- Special Thanks:
[@​s-matyukevich](https://togithub.com/s-matyukevich)
</details>
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**Description**
This PR fixes#1038
Added Streamlit example and also updated readme for examples.
**[Signed
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**Description**
This PR fixes#1013.
It adds `draft_model` and `n_draft` to the model YAML config in order to
load models with speculative sampling. This should be compatible as well
with grammars.
example:
```yaml
backend: llama
context_size: 1024
name: my-model-name
parameters:
model: foo-bar
n_draft: 16
draft_model: model-name
```
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
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**Description**
This PR fixes #
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---------
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**Description**
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Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
**Description**
This PR fixes#985
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**Description**
Simple fix, percentage value is expected to be float in range 0..1
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**Description**
Enable setting the seed for the stable diffusion pipeline. This is done
through an additional `seed` parameter in the request, such as:
```bash
curl http://localhost:8080/v1/images/generations \
-H "Content-Type: application/json" \
-d '{"model": "stablediffusion", "prompt": "prompt", "n": 1, "step": 51, "size": "512x512", "seed": 3}'
```
**Notes for Reviewers**
When the `seed` parameter is not sent, `request.seed` defaults to `0`,
making it difficult to detect an actual seed of `0`. Is there a way to
change the default to `-1` for instance ?
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refactor for model gallery endpoints - bundle up resources into a
struct, make galleries mutable with some crud endpoints. This is
groundwork required for making efficient use of the new scraper - while
that PR isn't _quite_ ready yet, the goal is to have more, individually
smaller gallery files. Therefore, rather than requiring a full localai
service restart, these new endpoints have been added to make life
easier.
- Adds endpoints to add, list and remove model galleries at runtime
- Adds these endpoints to the Insomnia config
- Minor fix: loading file urls follows symbolic links now
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**Description**
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**Description**
This PR bumps llama.cpp (adding support to gguf v2) and changes the
default test model
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
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**Description**
we were otherwise expecting absolute paths. this make it relative to the
model file (as someone would expect)
**Notes for Reviewers**
**[Signed
commits](../CONTRIBUTING.md#signing-off-on-commits-developer-certificate-of-origin)**
- [ ] Yes, I signed my commits.
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**Description**
This PR fixes#914
Now diffusers respects the `lora_adapter` configuration parameter.
---------
Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
[](https://renovatebot.com)
This PR contains the following updates:
| Package | Type | Update | Change |
|---|---|---|---|
| [github.com/otiai10/openaigo](https://togithub.com/otiai10/openaigo) |
require | minor | `v1.5.2` -> `v1.6.0` |
---
### Release Notes
<details>
<summary>otiai10/openaigo (github.com/otiai10/openaigo)</summary>
###
[`v1.6.0`](https://togithub.com/otiai10/openaigo/compare/v1.5.2...v1.6.0)
[Compare
Source](https://togithub.com/otiai10/openaigo/compare/v1.5.2...v1.6.0)
</details>
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This PR contains the following updates:
| Package | Type | Update | Change |
|---|---|---|---|
| [github.com/tmc/langchaingo](https://togithub.com/tmc/langchaingo) |
require | digest | `fef0821` -> `1e2a401` |
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This PR contains the following updates:
| Package | Type | Update | Change |
|---|---|---|---|
|
[github.com/go-skynet/go-llama.cpp](https://togithub.com/go-skynet/go-llama.cpp)
| require | digest | `f03869d` -> `bf63302` |
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**Description**
This PR fixes #
**Notes for Reviewers**
**[Signed
commits](../CONTRIBUTING.md#signing-off-on-commits-developer-certificate-of-origin)**
- [ ] Yes, I signed my commits.
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Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
[](https://renovatebot.com)
This PR contains the following updates:
| Package | Type | Update | Change |
|---|---|---|---|
|
[github.com/nomic-ai/gpt4all/gpt4all-bindings/golang](https://togithub.com/nomic-ai/gpt4all)
| require | digest | `36f7fb5` -> `27a8b02` |
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**Description**
This PR syncs up the `llama` backend to use `gguf`
(https://github.com/go-skynet/go-llama.cpp/pull/180). It also adds
`llama-stable` to the targets so we can still load ggml. It adapts the
current tests to use the `llama-backend` for ggml and uses a `gguf`
model to run tests on the new backend.
In order to consume the new version of go-llama.cpp, it also bump go to
1.21 (images, pipelines, etc)
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
This is a starting point for developers to easily import a collection of
requests to hit LocalAI. Insomnia was chosen as it's open source, has a
graphical user interface for users desiring that, and has the ability to
easily export requests as cURL commands for our documentation site.
## Enable/Disable single backend (useful if only one GPU is available)
# SINGLE_ACTIVE_BACKEND=true
## Specify a build type. Available: cublas, openblas, clblas.
## cuBLAS: This is a GPU-accelerated version of the complete standard BLAS (Basic Linear Algebra Subprograms) library. It's provided by Nvidia and is part of their CUDA toolkit.
## OpenBLAS: This is an open-source implementation of the BLAS library that aims to provide highly optimized code for various platforms. It includes support for multi-threading and can be compiled to use hardware-specific features for additional performance. OpenBLAS can run on many kinds of hardware, including CPUs from Intel, AMD, and ARM.
## clBLAS: This is an open-source implementation of the BLAS library that uses OpenCL, a framework for writing programs that execute across heterogeneous platforms consisting of CPUs, GPUs, and other processors. clBLAS is designed to take advantage of the parallel computing power of GPUs but can also run on any hardware that supports OpenCL. This includes hardware from different vendors like Nvidia, AMD, and Intel.
# BUILD_TYPE=openblas
## Uncomment and set to true to enable rebuilding from source
@@ -41,3 +50,23 @@ MODELS_PATH=/models
## Specify a default upload limit in MB (whisper)
# UPLOAD_LIMIT
## List of external GRPC backends (note on the container image this variable is already set to use extra backends available in extra/)
Thank you for your interest in contributing to LocalAI! We appreciate your time and effort in helping to improve our project. Before you get started, please take a moment to review these guidelines.
## Table of Contents
- [Getting Started](#getting-started)
- [Prerequisites](#prerequisites)
- [Setting up the Development Environment](#setting-up-the-development-environment)
- [Contributing](#contributing)
- [Submitting an Issue](#submitting-an-issue)
- [Creating a Pull Request (PR)](#creating-a-pull-request-pr)
- [Coding Guidelines](#coding-guidelines)
- [Testing](#testing)
- [Documentation](#documentation)
- [Community and Communication](#community-and-communication)
## Getting Started
### Prerequisites
- Golang [1.21]
- Git
- macOS/Linux
### Setting up the Development Environment and running localAI in the local environment
1. Clone the repository: `git clone https://github.com/go-skynet/LocalAI.git`
2. Navigate to the project directory: `cd LocalAI`
3. Install the required dependencies: `make prepare`
4. Run LocalAI: `make run`
## Contributing
We welcome contributions from everyone! To get started, follow these steps:
### Submitting an Issue
If you find a bug, have a feature request, or encounter any issues, please check the [issue tracker](https://github.com/go-skynet/LocalAI/issues) to see if a similar issue has already been reported. If not, feel free to [create a new issue](https://github.com/go-skynet/LocalAI/issues/new) and provide as much detail as possible.
### Creating a Pull Request (PR)
1. Fork the repository.
2. Create a new branch with a descriptive name: `git checkout -b [branch name]`
3. Make your changes and commit them.
4. Push the changes to your fork: `git push origin [branch name]`
5. Create a new pull request from your branch to the main project's `main` or `master` branch.
6. Provide a clear description of your changes in the pull request.
7. Make any requested changes during the review process.
8. Once your PR is approved, it will be merged into the main project.
## Coding Guidelines
- No specific coding guidelines at the moment. Please make sure the code can be tested. The most popular lint tools like []`golangci-lint`](https://golangci-lint.run) can help you here.
## Testing
`make test` cannot handle all the model now. Please be sure to add a test case for the new features or the part was changed.
## Documentation
- We are welcome the contribution of the documents, please open new PR in the official document repo [localai-website](https://github.com/go-skynet/localai-website)
## Community and Communication
- You can reach out via the Github issue tracker.
- Open a new discussion at [Discussion](https://github.com/go-skynet/LocalAI/discussions)
- Join the Discord channel [Discord](https://discord.gg/uJAeKSAGDy)
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&&\
**LocalAI** is a drop-in replacement REST API that's compatible with OpenAI API specifications for local inferencing. It allows you to run LLMs (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.
[](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)
For a list of the supported model families, please see [the model compatibility table](https://localai.io/model-compatibility/index.html#model-compatibility-table).
**LocalAI** is a drop-in replacement REST API that's compatible with OpenAI API specifications for local inferencing. It allows you to run LLMs (and not only) locally or on-prem with consumer grade hardware, supporting multiple model families that are compatible with the ggml format, pytorch and more. Does not require GPU.
<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.
- 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:
- 📖 Text generation with GPTs (`llama.cpp`, `gpt4all.cpp`, ... and more)
- 🗣 Text to Audio 🎺🆕
- 🔈 Audio to Text (Audio transcription with `whisper.cpp`)
- 🎨 Image generation with stable diffusion
- 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.
- ⚡ 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!
See the [Getting started](https://localai.io/basics/getting_started/index.html) and [examples](https://github.com/go-skynet/LocalAI/tree/master/examples/) sections to learn how to use LocalAI. For a list of curated models check out the [model gallery](https://localai.io/models/).
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)!
 | | |
## Hot topics / Roadmap
- [x] Support for embeddings
- [x] Support for audio transcription with https://github.com/ggerganov/whisper.cpp
- [X] Support for text-to-audio
- [x] GPU/CUDA support ( https://github.com/go-skynet/LocalAI/issues/69 )
- [X] Enable automatic downloading of models from a curated gallery
- [X] Enable automatic downloading of models from HuggingFace
- [ ] Upstream our golang bindings to llama.cpp (https://github.com/ggerganov/llama.cpp/issues/351)
- [ ] Enable gallery management directly from the webui.
- 🔥🔥🔥 28-06-2023: **v1.20.0**: Added text to audio and gallery huggingface repositories! [Release notes](https://localai.io/basics/news/index.html#-28-06-2023-__v1200__-) [Changelog](https://github.com/go-skynet/LocalAI/releases/tag/v1.20.0)
- 🔥🔥🔥 19-06-2023: **v1.19.0**: CUDA support! [Release notes](https://localai.io/basics/news/index.html#-19-06-2023-__v1190__-) [Changelog](https://github.com/go-skynet/LocalAI/releases/tag/v1.19.0)
- 🔥🔥🔥 06-06-2023: **v1.18.0**: Many updates, new features, and much more 🚀, check out the [Release notes](https://localai.io/basics/news/index.html#-06-06-2023-__v1180__-)!
- 29-05-2023: LocalAI now has a website, [https://localai.io](https://localai.io)! check the news in the [dedicated section](https://localai.io/basics/news/index.html)!
For latest news, follow also on Twitter [@LocalAI_API](https://twitter.com/LocalAI_API) and [@mudler_it](https://twitter.com/mudler_it)
- [Question Answering on Documents locally with LangChain, LocalAI, Chroma, and GPT4All](https://mudler.pm/posts/localai-question-answering/)
- [Tutorial to use k8sgpt with LocalAI](https://medium.com/@tyler_97636/k8sgpt-localai-unlock-kubernetes-superpowers-for-free-584790de9b65)
## Contribute and help
## 💻 Usage
To help the project you can:
Check out the [Getting started](https://localai.io/basics/getting_started/index.html) section in our documentation.
- [Hacker news post](https://news.ycombinator.com/item?id=35726934) - help us out by voting if you like this project.
### 💡 Example: Use Luna-AI Llama model
- If you have technological skills and want to contribute to development, have a look at the open issues. If you are new you can have a look at the [good-first-issue](https://github.com/go-skynet/LocalAI/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) and [help-wanted](https://github.com/go-skynet/LocalAI/issues?q=is%3Aissue+is%3Aopen+label%3A%22help+wanted%22) labels.
See the [documentation](https://localai.io/basics/getting_started)
- If you don't have technological skills you can still help improving documentation or add examples or share your user-stories with our community, any help and contribution is welcome!
### 🔗 Resources
## Usage
- [How to build locally](https://localai.io/basics/build/index.html)
- [How to install in Kubernetes](https://localai.io/basics/getting_started/index.html#run-localai-in-kubernetes)
- [How tos section](https://localai.io/howtos/) (curated by our community)
## Citation
Check out the [Getting started](https://localai.io/basics/getting_started/index.html) section. Here below you will find generic, quick instructions to get ready and use LocalAI.
The easiest way to run LocalAI is by using `docker-compose` (to build locally, see [building LocalAI](https://localai.io/basics/build/index.html)):
```bash
git clone https://github.com/go-skynet/LocalAI
cd LocalAI
# (optional) Checkout a specific LocalAI tag
# git checkout -b build <TAG>
# copy your models to models/
cp your-model.bin models/
# (optional) Edit the .env file to set things like context size and threads
See the [build section](https://localai.io/basics/build/index.html) in our documentation for detailed instructions.
### Run LocalAI in Kubernetes
LocalAI can be installed inside Kubernetes with helm. See [installation instructions](https://localai.io/basics/getting_started/index.html#run-localai-in-kubernetes).
## Supported API endpoints
See the [list of the LocalAI features](https://localai.io/features/index.html) for a full tour of the available API endpoints.
## Frequently asked questions
See [the FAQ](https://localai.io/faq/index.html) section for a list of common questions.
## Projects already using LocalAI to run local models
Feel free to open up a PR to get your project listed!
resp,err:=client.CreateChatCompletion(context.TODO(),openai.ChatCompletionRequest{Model:"gpt4all-j",Messages:[]openai.ChatCompletionMessage{openai.ChatCompletionMessage{Role:"user",Content:"How are you?"}}})
FeatureFlagFeatureFlag`yaml:"feature_flags"`// Feature Flag registry. We move fast, and features may break on a per model/backend basis. Registry for (usually temporary) flags that indicate aborting something early.
// LLM configs (GPT4ALL, Llama.cpp, ...)
LLMConfig`yaml:",inline"`
// AutoGPTQ specifics
AutoGPTQAutoGPTQ`yaml:"autogptq"`
// Diffusers
DiffusersDiffusers`yaml:"diffusers"`
Stepint`yaml:"step"`
// GRPC Options
GRPCGRPC`yaml:"grpc"`
// Vall-e-x
VallEVallE`yaml:"vall-e"`
}
typeVallEstruct{
AudioPathstring`yaml:"audio_path"`
}
typeFeatureFlagmap[string]*bool
func(ffFeatureFlag)Enabled(sstring)bool{
v,exist:=ff[s]
returnexist&&v!=nil&&*v
}
typeGRPCstruct{
Attemptsint`yaml:"attempts"`
AttemptsSleepTimeint`yaml:"attempts_sleep_time"`
}
typeDiffusersstruct{
PipelineTypestring`yaml:"pipeline_type"`
SchedulerTypestring`yaml:"scheduler_type"`
CUDAbool`yaml:"cuda"`
EnableParametersstring`yaml:"enable_parameters"`// A list of comma separated parameters to specify
returnfmt.Errorf("backend %s experienced an error retrieving status info via rpc: %s, then failed local node process sample: %s",backendId,rpcErr.Error(),slbErr.Error())
 | | |
Here is a list of projects that can easily be integrated with the LocalAI backend.
### Projects
### AutoGPT
@@ -148,6 +157,26 @@ Allows to run any LocalAI-compatible model as a backend on the servers of https:
[Check it out here](https://runpod.io/gsc?template=uv9mtqnrd0&ref=984wlcra)
Demonstrates how to integrate an open-source copilot alternative that enhances code analysis, completion, and improvements. This approach seamlessly integrates with any LocalAI model, offering a more user-friendly experience.
[Check it out here](https://github.com/go-skynet/LocalAI/tree/master/examples/continue/)
### Streamlit bot
_by [@majoshi1](https://github.com/majoshi1)_

A chat bot made using `Streamlit` & LocalAI.
[Check it out here](https://github.com/go-skynet/LocalAI/tree/master/examples/streamlit-bot/)
## Want to contribute?
Create an issue, and put `Example: <description>` in the title! We will post your examples here.
# Edit the .env file to set a different model by editing `PRELOAD_MODELS`.
vim .env
docker-compose run --rm auto-gpt
```
Note: The example automatically downloads the `gpt4all` model as it is under a permissive license. The GPT4All model does not seem to be enough to run AutoGPT. WizardLM-7b-uncensored seems to perform better (with `f16: true`).
See the `.env` configuration file to set a different model with the [model-gallery](https://github.com/go-skynet/model-gallery) by editing `PRELOAD_MODELS`.
"messages": [{"role": "system", "content": "You are a helpful, intelligent pirate assistant. Always answer as helpfully as possible, while being a pirate and using pirate language. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."},
{"role": "user", "content": "How could one use electricity to cook an egg?"},
{"role": "assistant",
"content": "Shiver me timbers! Using electricity to cook an egg? Well, matey, I reckon that be a right curious idea! *adjusts spectacles* Now, I ain't sure if this be possible, but I'll do me best to help ye out.\n\nFirst things first, ye gotta understand that electricity be a powerful force, and it can't just cook an egg on its own. Ye see, electricity be like a mighty wind that can make things happen, but it needs somethin' to work with. So, if ye want to use electricity to cook an egg, ye gotta find a way to harness that power and make it do the cookin'.\n\nNow, I know what ye might be thinkin': \"How do I harness the power of electricity to cook an egg?\" Well, matey, there be a few ways to do it. One way be to use a special device called an \"electric frying pan.\" This be a pan that has a built-in heating element that gets hot when ye plug it into a wall socket. When the element gets hot, ye can crack an egg into the pan and watch as it cook"
},
{"role": "user", "content": "I don't have one of those, just a raw wire and plenty of power! How do we get it done?"}],
# LocalAI Demonstration with Embeddings and Chainlit
This demonstration shows you how to use embeddings with existing data in `LocalAI`, and how to integrate it with Chainlit for an interactive querying experience. We are using the `llama_index` library to facilitate the embedding and querying processes, and `chainlit` to provide an interactive interface. The `Weaviate` client is used as the embedding source.
## Prerequisites
Before proceeding, make sure you have the following installed:
- Weaviate client
- LocalAI and its dependencies
- Chainlit and its dependencies
## Getting Started
1. Clone this repository:
2. Navigate to the project directory:
3. Run the example: `chainlit run main.py`
# Highlight on `llama_index` and `chainlit`
`llama_index` is the key library that facilitates the process of embedding and querying data in LocalAI. It provides a seamless interface to integrate various components, such as `WeaviateVectorStore`, `LocalAI`, `ServiceContext`, and more, for a smooth querying experience.
`chainlit` is used to provide an interactive interface for users to query the data and see the results in real-time. It integrates with llama_index to handle the querying process and display the results to the user.
In this example, `llama_index` is used to set up the `VectorStoreIndex` and `QueryEngine`, and `chainlit` is used to handle the user interactions with `LocalAI` and display the results.
@@ -24,10 +24,13 @@ docker-compose up -d --pull always
# docker-compose up -d --build
```
Then browse to `http://localhost:3000` to view the Web UI.
## Pointing chatbot-ui to a separately managed LocalAI service
If you want to use the [chatbot-ui example](https://github.com/go-skynet/LocalAI/tree/master/examples/chatbot-ui) with an externally managed LocalAI service, you can alter the `docker-compose` file so that it looks like the below. You will notice the file is smaller, because we have removed the section that would normally start the LocalAI service. Take care to update the IP address (or FQDN) that the chatbot-ui service tries to access (marked `<<LOCALAI_IP>>` below):
```
If you want to use the [chatbot-ui example](https://github.com/go-skynet/LocalAI/tree/master/examples/chatbot-ui) with an externally managed LocalAI service, you can alter the `docker-compose.yaml` file so that it looks like the below. You will notice the file is smaller, because we have removed the section that would normally start the LocalAI service. Take care to update the IP address (or FQDN) that the chatbot-ui service tries to access (marked `<<LOCALAI_IP>>` below):
```yaml
version:'3.6'
services:
@@ -40,9 +43,8 @@ services:
- 'OPENAI_API_HOST=http://<<LOCALAI_IP>>:8080'
```
Once you've edited the Dockerfile, you can start it with `docker compose up`, then browse to `http://localhost:3000`.
Once you've edited the `docker-compose.yaml`, you can start it with `docker compose up`, then browse to `http://localhost:3000` to view the Web UI.
@@ -20,10 +20,13 @@ docker-compose up --pull always
# docker-compose up -d --build
```
Then browse to `http://localhost:3000` to view the Web UI.
## Pointing chatbot-ui to a separately managed LocalAI service
If you want to use the [chatbot-ui example](https://github.com/go-skynet/LocalAI/tree/master/examples/chatbot-ui) with an externally managed LocalAI service, you can alter the `docker-compose` file so that it looks like the below. You will notice the file is smaller, because we have removed the section that would normally start the LocalAI service. Take care to update the IP address (or FQDN) that the chatbot-ui service tries to access (marked `<<LOCALAI_IP>>` below):
```
If you want to use the [chatbot-ui example](https://github.com/go-skynet/LocalAI/tree/master/examples/chatbot-ui) with an externally managed LocalAI service, you can alter the `docker-compose.yaml` file so that it looks like the below. You will notice the file is smaller, because we have removed the section that would normally start the LocalAI service. Take care to update the IP address (or FQDN) that the chatbot-ui service tries to access (marked `<<LOCALAI_IP>>` below):
```yaml
version:'3.6'
services:
@@ -36,9 +39,8 @@ services:
- 'OPENAI_API_HOST=http://<<LOCALAI_IP>>:8080'
```
Once you've edited the Dockerfile, you can start it with `docker compose up`, then browse to `http://localhost:3000`.
Once you've edited the `docker-compose.yaml`, you can start it with `docker compose up`, then browse to `http://localhost:3000` to view the Web UI.
A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.
{{.Input}}
ASSISTANT:
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