workaround upstream issue

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

.github/workflows/image-pr.yml

@@ -47,7 +47,7 @@ jobs:
           #   makeflags: "--jobs=3 --output-sync=target"
           - build-type: 'cublas'
             cuda-major-version: "12"
-            cuda-minor-version: "4"
+            cuda-minor-version: "0"
             platforms: 'linux/amd64'
             tag-latest: 'false'
             tag-suffix: '-cublas-cuda12-ffmpeg'
@@ -120,7 +120,7 @@ jobs:
           #   makeflags: "--jobs=3 --output-sync=target"
           # - build-type: 'cublas'
           #   cuda-major-version: "12"
-          #   cuda-minor-version: "4"
+          #   cuda-minor-version: "0"
           #   platforms: 'linux/amd64'
           #   tag-latest: 'false'
           #   tag-suffix: '-cublas-cuda12-ffmpeg-core'

.github/workflows/image.yml

@@ -75,7 +75,7 @@ jobs:
             makeflags: "--jobs=3 --output-sync=target"
           - build-type: 'cublas'
             cuda-major-version: "12"
-            cuda-minor-version: "4"
+            cuda-minor-version: "0"
             platforms: 'linux/amd64'
             tag-latest: 'false'
             tag-suffix: '-cublas-cuda12'
@@ -100,7 +100,7 @@ jobs:
             makeflags: "--jobs=3 --output-sync=target"
           - build-type: 'cublas'
             cuda-major-version: "12"
-            cuda-minor-version: "4"
+            cuda-minor-version: "0"
             platforms: 'linux/amd64'
             tag-latest: 'auto'
             tag-suffix: '-cublas-cuda12-ffmpeg'
@@ -285,7 +285,7 @@ jobs:
             makeflags: "--jobs=4 --output-sync=target"
           - build-type: 'cublas'
             cuda-major-version: "12"
-            cuda-minor-version: "4"
+            cuda-minor-version: "0"
             platforms: 'linux/amd64'
             tag-latest: 'false'
             tag-suffix: '-cublas-cuda12-core'
@@ -307,7 +307,7 @@ jobs:
             makeflags: "--jobs=4 --output-sync=target"
           - build-type: 'cublas'
             cuda-major-version: "12"
-            cuda-minor-version: "4"
+            cuda-minor-version: "0"
             platforms: 'linux/amd64'
             tag-latest: 'false'
             tag-suffix: '-cublas-cuda12-ffmpeg-core'

.github/workflows/release.yaml

@@ -4,6 +4,8 @@ on:
   push:
     branches:
       - master
+    tags:
+      - 'v*'
   pull_request:
 
 env:
@@ -29,11 +31,10 @@ jobs:
         with:
           go-version: '1.21.x'
           cache: false
-
       - name: Dependencies
         run: |
           sudo apt-get update
-          sudo apt-get install build-essential ffmpeg protobuf-compiler ccache gawk
+          sudo apt-get install build-essential ffmpeg protobuf-compiler ccache upx-ucl gawk
           sudo apt-get install -qy binutils-aarch64-linux-gnu gcc-aarch64-linux-gnu g++-aarch64-linux-gnu libgmock-dev
       - name: Install CUDA Dependencies
         run: |
@@ -149,7 +150,7 @@ jobs:
       - name: Dependencies
         run: |
           sudo apt-get update
-          sudo apt-get install -y wget curl build-essential ffmpeg protobuf-compiler ccache gawk cmake libgmock-dev
+          sudo apt-get install -y wget curl build-essential ffmpeg protobuf-compiler ccache upx-ucl gawk cmake libgmock-dev
       - name: Intel Dependencies
         run: |
           wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | sudo tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null
@@ -250,7 +251,7 @@ jobs:
       - name: Dependencies
         run: |
           sudo apt-get update
-          sudo apt-get install -y --no-install-recommends libopencv-dev protobuf-compiler ccache
+          sudo apt-get install -y --no-install-recommends libopencv-dev protobuf-compiler ccache upx-ucl
           go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@1958fcbe2ca8bd93af633f11e97d44e567e945af
           go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.34.2
       - name: Build stablediffusion

.github/workflows/test.yml

@@ -70,7 +70,7 @@ jobs:
       - name: Dependencies
         run: |
           sudo apt-get update
-          sudo apt-get install build-essential curl ffmpeg
+          sudo apt-get install build-essential ccache upx-ucl curl ffmpeg
           sudo apt-get install -y libgmock-dev
           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 && \

Dockerfile

@@ -24,7 +24,7 @@ RUN apt-get update && \
         cmake \
         curl \
         git \
-        unzip && \
+        unzip upx-ucl && \
     apt-get clean && \
     rm -rf /var/lib/apt/lists/*
 
@@ -99,7 +99,7 @@ FROM requirements-${IMAGE_TYPE} AS requirements-drivers
 
 ARG BUILD_TYPE
 ARG CUDA_MAJOR_VERSION=12
-ARG CUDA_MINOR_VERSION=4
+ARG CUDA_MINOR_VERSION=0
 
 ENV BUILD_TYPE=${BUILD_TYPE}
 

Makefile

@@ -8,7 +8,7 @@ DETECT_LIBS?=true
 # llama.cpp versions
 GOLLAMA_REPO?=https://github.com/go-skynet/go-llama.cpp
 GOLLAMA_VERSION?=2b57a8ae43e4699d3dc5d1496a1ccd42922993be
-CPPLLAMA_VERSION?=705b7ecf60e667ced57c15d67aa86865e3cc7aa7
+CPPLLAMA_VERSION?=b841d0740855c5af1344a81f261139a45a2b39ee
 
 # gpt4all version
 GPT4ALL_REPO?=https://github.com/nomic-ai/gpt4all
@@ -58,7 +58,7 @@ RANDOM := $(shell bash -c 'echo $$RANDOM')
 
 VERSION?=$(shell git describe --always --tags || echo "dev" )
 # go tool nm ./local-ai | grep Commit
-LD_FLAGS?=
+LD_FLAGS?=-s -w
 override LD_FLAGS += -X "github.com/mudler/LocalAI/internal.Version=$(VERSION)"
 override LD_FLAGS += -X "github.com/mudler/LocalAI/internal.Commit=$(shell git rev-parse HEAD)"
 
@@ -72,6 +72,14 @@ WHITE  := $(shell tput -Txterm setaf 7)
 CYAN   := $(shell tput -Txterm setaf 6)
 RESET  := $(shell tput -Txterm sgr0)
 
+UPX?=
+# check if upx exists
+ifeq (, $(shell which upx))
+	UPX=
+else
+	UPX=$(shell which upx)
+endif
+
 # Default Docker bridge IP
 E2E_BRIDGE_IP?=172.17.0.1
 
@@ -377,6 +385,7 @@ build: prepare backend-assets grpcs ## Build the project
 	$(info ${GREEN}I BUILD_TYPE: ${YELLOW}$(BUILD_TYPE)${RESET})
 	$(info ${GREEN}I GO_TAGS: ${YELLOW}$(GO_TAGS)${RESET})
 	$(info ${GREEN}I LD_FLAGS: ${YELLOW}$(LD_FLAGS)${RESET})
+	$(info ${GREEN}I UPX: ${YELLOW}$(UPX)${RESET})
 ifneq ($(BACKEND_LIBS),)
 	$(MAKE) backend-assets/lib
 	cp -f $(BACKEND_LIBS) backend-assets/lib/
@@ -421,7 +430,7 @@ else
 endif
 
 dist-cross-linux-arm64:
-	CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_NATIVE=off" GRPC_BACKENDS="backend-assets/grpc/llama-cpp-fallback backend-assets/grpc/llama-cpp-grpc backend-assets/util/llama-cpp-rpc-server" \
+	CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_NATIVE=off" GRPC_BACKENDS="backend-assets/grpc/llama-cpp-fallback backend-assets/grpc/llama-cpp-grpc backend-assets/util/llama-cpp-rpc-server" GO_TAGS="p2p" \
 	STATIC=true $(MAKE) build
 	mkdir -p release
 # if BUILD_ID is empty, then we don't append it to the binary name
@@ -471,7 +480,7 @@ prepare-e2e:
 	mkdir -p $(TEST_DIR)
 	cp -rfv $(abspath ./tests/e2e-fixtures)/gpu.yaml $(TEST_DIR)/gpu.yaml
 	test -e $(TEST_DIR)/ggllm-test-model.bin || wget -q https://huggingface.co/TheBloke/CodeLlama-7B-Instruct-GGUF/resolve/main/codellama-7b-instruct.Q2_K.gguf -O $(TEST_DIR)/ggllm-test-model.bin
-	docker build --build-arg GRPC_BACKENDS="$(GRPC_BACKENDS)" --build-arg IMAGE_TYPE=core --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg CUDA_MAJOR_VERSION=12 --build-arg CUDA_MINOR_VERSION=4 --build-arg FFMPEG=true -t localai-tests .
+	docker build --build-arg GRPC_BACKENDS="$(GRPC_BACKENDS)" --build-arg IMAGE_TYPE=core --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg CUDA_MAJOR_VERSION=12 --build-arg CUDA_MINOR_VERSION=0 --build-arg FFMPEG=true -t localai-tests .
 
 run-e2e-image:
 	ls -liah $(abspath ./tests/e2e-fixtures)
@@ -733,13 +742,22 @@ backend-assets/grpc: protogen-go replace
 backend-assets/grpc/bert-embeddings: sources/go-bert.cpp sources/go-bert.cpp/libgobert.a backend-assets/grpc
 	CGO_LDFLAGS="$(CGO_LDFLAGS)" C_INCLUDE_PATH=$(CURDIR)/sources/go-bert.cpp LIBRARY_PATH=$(CURDIR)/sources/go-bert.cpp \
 	$(GOCMD) build -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o backend-assets/grpc/bert-embeddings ./backend/go/llm/bert/
+ifneq ($(UPX),)
+	$(UPX) backend-assets/grpc/bert-embeddings
+endif
 
 backend-assets/grpc/gpt4all: sources/gpt4all sources/gpt4all/gpt4all-bindings/golang/libgpt4all.a backend-assets/gpt4all backend-assets/grpc
 	CGO_LDFLAGS="$(CGO_LDFLAGS)" C_INCLUDE_PATH=$(CURDIR)/sources/gpt4all/gpt4all-bindings/golang/ LIBRARY_PATH=$(CURDIR)/sources/gpt4all/gpt4all-bindings/golang/ \
 	$(GOCMD) build -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o backend-assets/grpc/gpt4all ./backend/go/llm/gpt4all/
+ifneq ($(UPX),)
+	$(UPX) backend-assets/grpc/gpt4all
+endif
 
 backend-assets/grpc/huggingface: backend-assets/grpc
 	$(GOCMD) build -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o backend-assets/grpc/huggingface ./backend/go/llm/langchain/
+ifneq ($(UPX),)
+	$(UPX) backend-assets/grpc/huggingface
+endif
 
 backend/cpp/llama/llama.cpp:
 	LLAMA_VERSION=$(CPPLLAMA_VERSION) $(MAKE) -C backend/cpp/llama llama.cpp
@@ -765,6 +783,9 @@ else
 	echo "BUILD_GRPC_FOR_BACKEND_LLAMA is not defined."
 	LLAMA_VERSION=$(CPPLLAMA_VERSION) $(MAKE) -C backend/cpp/${VARIANT} grpc-server
 endif
+ifneq ($(UPX),)
+	$(UPX) backend/cpp/${VARIANT}/grpc-server
+endif
 
 # This target is for manually building a variant with-auto detected flags
 backend-assets/grpc/llama-cpp: backend-assets/grpc backend/cpp/llama/llama.cpp
@@ -837,33 +858,57 @@ backend-assets/grpc/llama-cpp-grpc: backend-assets/grpc backend/cpp/llama/llama.
 backend-assets/util/llama-cpp-rpc-server: backend-assets/grpc/llama-cpp-grpc
 	mkdir -p backend-assets/util/
 	cp -rf backend/cpp/llama-grpc/llama.cpp/build/bin/rpc-server backend-assets/util/llama-cpp-rpc-server
+ifneq ($(UPX),)
+	$(UPX) backend-assets/util/llama-cpp-rpc-server
+endif
 
 backend-assets/grpc/llama-ggml: sources/go-llama.cpp sources/go-llama.cpp/libbinding.a backend-assets/grpc
 	CGO_LDFLAGS="$(CGO_LDFLAGS)" C_INCLUDE_PATH=$(CURDIR)/sources/go-llama.cpp LIBRARY_PATH=$(CURDIR)/sources/go-llama.cpp \
 	$(GOCMD) build -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o backend-assets/grpc/llama-ggml ./backend/go/llm/llama-ggml/
+ifneq ($(UPX),)
+	$(UPX) backend-assets/grpc/llama-ggml
+endif
 
 backend-assets/grpc/piper: sources/go-piper sources/go-piper/libpiper_binding.a backend-assets/grpc backend-assets/espeak-ng-data
 	CGO_CXXFLAGS="$(PIPER_CGO_CXXFLAGS)" CGO_LDFLAGS="$(PIPER_CGO_LDFLAGS)" LIBRARY_PATH=$(CURDIR)/sources/go-piper \
 	$(GOCMD) build -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o backend-assets/grpc/piper ./backend/go/tts/
+ifneq ($(UPX),)
+	$(UPX) backend-assets/grpc/piper
+endif
 
 backend-assets/grpc/rwkv: sources/go-rwkv.cpp sources/go-rwkv.cpp/librwkv.a backend-assets/grpc
 	CGO_LDFLAGS="$(CGO_LDFLAGS)" C_INCLUDE_PATH=$(CURDIR)/sources/go-rwkv.cpp LIBRARY_PATH=$(CURDIR)/sources/go-rwkv.cpp \
 	$(GOCMD) build -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o backend-assets/grpc/rwkv ./backend/go/llm/rwkv
+ifneq ($(UPX),)
+	$(UPX) backend-assets/grpc/rwkv
+endif
 
 backend-assets/grpc/stablediffusion: sources/go-stable-diffusion sources/go-stable-diffusion/libstablediffusion.a backend-assets/grpc
 	CGO_LDFLAGS="$(CGO_LDFLAGS)" CPATH="$(CPATH):$(CURDIR)/sources/go-stable-diffusion/:/usr/include/opencv4" LIBRARY_PATH=$(CURDIR)/sources/go-stable-diffusion/ \
 	$(GOCMD) build -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o backend-assets/grpc/stablediffusion ./backend/go/image/stablediffusion
+ifneq ($(UPX),)
+	$(UPX) backend-assets/grpc/stablediffusion
+endif
 
 backend-assets/grpc/tinydream: sources/go-tiny-dream sources/go-tiny-dream/libtinydream.a backend-assets/grpc
 	CGO_LDFLAGS="$(CGO_LDFLAGS)" LIBRARY_PATH=$(CURDIR)/go-tiny-dream \
 	$(GOCMD) build -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o backend-assets/grpc/tinydream ./backend/go/image/tinydream
+ifneq ($(UPX),)
+	$(UPX) backend-assets/grpc/tinydream
+endif
 
 backend-assets/grpc/whisper: sources/whisper.cpp sources/whisper.cpp/libwhisper.a backend-assets/grpc
 	CGO_LDFLAGS="$(CGO_LDFLAGS) $(CGO_LDFLAGS_WHISPER)" C_INCLUDE_PATH="$(CURDIR)/sources/whisper.cpp/include:$(CURDIR)/sources/whisper.cpp/ggml/include" LIBRARY_PATH=$(CURDIR)/sources/whisper.cpp \
 	$(GOCMD) build -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o backend-assets/grpc/whisper ./backend/go/transcribe/
+ifneq ($(UPX),)
+	$(UPX) backend-assets/grpc/whisper
+endif
 
 backend-assets/grpc/local-store: backend-assets/grpc
 	$(GOCMD) build -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o backend-assets/grpc/local-store ./backend/go/stores/
+ifneq ($(UPX),)
+	$(UPX) backend-assets/grpc/local-store
+endif
 
 grpcs: prepare $(GRPC_BACKENDS)
 

backend/cpp/llama/CMakeLists.txt

@@ -75,11 +75,24 @@ add_library(hw_grpc_proto
   ${hw_proto_hdrs} )
 
 add_executable(${TARGET} grpc-server.cpp utils.hpp json.hpp)
-target_link_libraries(${TARGET} PRIVATE common llama myclip ${CMAKE_THREAD_LIBS_INIT} absl::flags hw_grpc_proto
-  absl::flags_parse
-  gRPC::${_REFLECTION}
-  gRPC::${_GRPC_GRPCPP}
-  protobuf::${_PROTOBUF_LIBPROTOBUF})
+
+# Conditionally link SYCL to grpc-server
+# https://github.com/ggerganov/llama.cpp/issues/8665
+if ( DEFINED ENV{ONEAPI_ROOT})
+    target_link_libraries(${TARGET} PRIVATE common llama myclip ${CMAKE_THREAD_LIBS_INIT} absl::flags hw_grpc_proto
+      absl::flags_parse
+      gRPC::${_REFLECTION}
+      gRPC::${_GRPC_GRPCPP}
+      protobuf::${_PROTOBUF_LIBPROTOBUF}
+      sycl)
+else()
+    target_link_libraries(${TARGET} PRIVATE common llama myclip ${CMAKE_THREAD_LIBS_INIT} absl::flags hw_grpc_proto
+      absl::flags_parse
+      gRPC::${_REFLECTION}
+      gRPC::${_GRPC_GRPCPP}
+      protobuf::${_PROTOBUF_LIBPROTOBUF})
+endif()
+
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
 if(TARGET BUILD_INFO)
   add_dependencies(${TARGET} BUILD_INFO)

backend/cpp/llama/CMakeLists.txt.rpc-8662

@@ -0,0 +1,8 @@
+# https://github.com/ggerganov/llama.cpp/issues/8665
+
+add_executable(rpc-server rpc-server.cpp)
+if ( DEFINED ENV{ONEAPI_ROOT})
+target_link_libraries(rpc-server PRIVATE ggml llama sycl)
+else()
+target_link_libraries(rpc-server PRIVATE ggml llama)
+endif()

backend/cpp/llama/prepare.sh

@@ -17,4 +17,7 @@ cp -rfv llama.cpp/examples/llava/clip.h llama.cpp/examples/grpc-server/clip.h
 cp -rfv llama.cpp/examples/llava/llava.cpp llama.cpp/examples/grpc-server/llava.cpp
 echo '#include "llama.h"' > llama.cpp/examples/grpc-server/llava.h
 cat llama.cpp/examples/llava/llava.h >> llama.cpp/examples/grpc-server/llava.h
-cp -rfv llama.cpp/examples/llava/clip.cpp llama.cpp/examples/grpc-server/clip.cpp
+cp -rfv llama.cpp/examples/llava/clip.cpp llama.cpp/examples/grpc-server/clip.cpp
+
+# https://github.com/ggerganov/llama.cpp/issues/8665
+cp -rfv CMakeLists.txt.rpc-8662 llama.cpp/examples/rpc/CMakeLists.txt

backend/python/autogptq/requirements.txt

@@ -1,6 +1,6 @@
 accelerate
 auto-gptq==0.7.1
-grpcio==1.65.0
+grpcio==1.65.1
 protobuf
 torch
 certifi

backend/python/bark/requirements.txt

@@ -1,6 +1,6 @@
 accelerate
 bark==0.1.5
-grpcio==1.65.0
+grpcio==1.65.1
 protobuf
 certifi
 transformers

backend/python/common/template/requirements.txt

@@ -1,2 +1,2 @@
-grpcio==1.65.0
+grpcio==1.65.1
 protobuf

backend/python/coqui/requirements.txt

@@ -1,6 +1,6 @@
 accelerate
 TTS==0.22.0
-grpcio==1.65.0
+grpcio==1.65.1
 protobuf
 certifi
 transformers

backend/python/diffusers/requirements.txt

@@ -3,7 +3,7 @@ accelerate
 compel
 peft
 diffusers
-grpcio==1.65.0
+grpcio==1.65.1
 opencv-python
 pillow
 protobuf

backend/python/exllama2/requirements.txt

@@ -1,5 +1,5 @@
 accelerate
-grpcio==1.65.0
+grpcio==1.65.1
 protobuf
 certifi
 torch

backend/python/mamba/requirements.txt

@@ -1,6 +1,6 @@
 causal-conv1d==1.4.0
 mamba-ssm==2.2.2
-grpcio==1.65.0
+grpcio==1.65.1
 protobuf
 certifi
 transformers

backend/python/openvoice/requirements-intel.txt

@@ -2,7 +2,7 @@
 intel-extension-for-pytorch
 torch
 optimum[openvino]
-grpcio==1.64.1
+grpcio==1.65.1
 protobuf
 librosa==0.9.1
 faster-whisper==1.0.3

backend/python/openvoice/requirements.txt

@@ -1,4 +1,4 @@
-grpcio==1.65.0
+grpcio==1.65.1
 protobuf
 librosa
 faster-whisper

backend/python/parler-tts/requirements.txt

@@ -1,5 +1,5 @@
 accelerate
-grpcio==1.65.0
+grpcio==1.65.1
 protobuf
 torch
 git+https://github.com/huggingface/parler-tts.git@10016fb0300c0dc31a0fb70e26f3affee7b62f16

backend/python/rerankers/requirements.txt

@@ -1,6 +1,6 @@
 accelerate
 rerankers[transformers]
-grpcio==1.65.0
+grpcio==1.65.1
 protobuf
 certifi
 transformers

backend/python/sentencetransformers/requirements-intel.txt

@@ -2,4 +2,4 @@
 intel-extension-for-pytorch
 torch
 optimum[openvino]
-setuptools==70.3.0 # https://github.com/mudler/LocalAI/issues/2406
+setuptools==69.5.1 # https://github.com/mudler/LocalAI/issues/2406

backend/python/sentencetransformers/requirements.txt

@@ -1,6 +1,6 @@
 accelerate
 sentence-transformers==3.0.1
 transformers
-grpcio==1.65.0
+grpcio==1.65.1
 protobuf
 certifi

backend/python/transformers-musicgen/requirements-intel.txt

@@ -2,4 +2,4 @@
 intel-extension-for-pytorch
 torch
 optimum[openvino]
-setuptools==70.3.0 # https://github.com/mudler/LocalAI/issues/2406
+setuptools==69.5.1 # https://github.com/mudler/LocalAI/issues/2406

backend/python/transformers-musicgen/requirements.txt

@@ -1,6 +1,6 @@
 accelerate
 transformers
-grpcio==1.65.0
+grpcio==1.65.1
 protobuf
 torch
 scipy==1.14.0

backend/python/transformers/requirements-intel.txt

@@ -2,4 +2,3 @@
 intel-extension-for-pytorch
 torch
 optimum[openvino]
-setuptools==70.3.0 # https://github.com/mudler/LocalAI/issues/2406

backend/python/transformers/requirements.txt

@@ -1,9 +1,9 @@
 accelerate
 transformers
-grpcio==1.65.0
+grpcio==1.65.1
 protobuf
 torch
 certifi
 intel-extension-for-transformers
 bitsandbytes
-setuptools==70.3.0 # https://github.com/mudler/LocalAI/issues/2406
+setuptools==69.5.1 # https://github.com/mudler/LocalAI/issues/2406

backend/python/vall-e-x/requirements.txt

@@ -1,4 +1,4 @@
 accelerate
-grpcio==1.65.0
+grpcio==1.65.1
 protobuf
 certifi

backend/python/vllm/requirements.txt

@@ -1,6 +1,6 @@
 accelerate
 vllm
-grpcio==1.65.0
+grpcio==1.65.1
 protobuf
 certifi
 transformers

core/http/views/p2p.html

@@ -16,7 +16,16 @@
                 </a> 
             </h2> 
             <h5 class="mb-4 text-justify">LocalAI uses P2P technologies to enable distribution of work between peers. It is possible to share an instance with Federation and/or split the weights of a model across peers (only available with llama.cpp models). You can now share computational resources between your devices or your friends!</h5>
-            
+            <!-- Warning box if p2p token is empty and p2p is enabled -->
+            {{ if and .IsP2PEnabled (eq .P2PToken "") }}
+            <div class="bg-red-500 p-4 rounded-lg shadow-lg mb-12 text-left">
+                <p class="text-xl font-semibold text-white"> <i class="fa-solid fa-exclamation-triangle"></i> Warning: P2P mode is disabled or no token was specified</p>
+                <p class="mb-4">You have to enable P2P mode by starting LocalAI with <code>--p2p</code>. Please restart the server with <code>--p2p</code> to generate a new token automatically that can be used to automatically discover other nodes. If you already have a token specify it with <code>export TOKEN=".."</code> <a href="https://localai.io/features/distribute/" target="_blank">
+                    Check out the documentation for more information.
+                </a> </p>
+            </div>
+            {{ else }}
+
             <!-- Federation Box -->
             <div class="bg-gray-800 p-6 rounded-lg shadow-lg mb-12 text-left">
 
@@ -128,7 +137,8 @@
                     </div>
                 </div>
             </div>
-            <!-- Llama.cpp Box END -->       
+            <!-- Llama.cpp Box END -->    
+            {{ end }}   
         </div>
     </div>
 

docs/content/docs/features/distributed_inferencing.md

@@ -5,17 +5,65 @@ weight = 15
 url = "/features/distribute/"
 +++
 
+
+This functionality enables LocalAI to distribute inference requests across multiple worker nodes, improving efficiency and performance. Nodes are automatically discovered and connect via p2p by using a shared token which makes sure the communication is secure and private between the nodes of the network.
+
+LocalAI supports two modes of distributed inferencing via p2p:
+
+- **Federated Mode**: Requests are shared between the cluster and routed to a single worker node in the network based on the load balancer's decision.
+- **Worker Mode** (aka "model sharding" or "splitting weights"): Requests are processed by all the workers which contributes to the final inference result (by sharing the model weights).
+
+## Usage
+
+Starting LocalAI with `--p2p` generates a shared token for connecting multiple instances: and that's all you need to create AI clusters, eliminating the need for intricate network setups. 
+
+Simply navigate to the "Swarm" section in the WebUI and follow the on-screen instructions.
+
+For fully shared instances, initiate LocalAI with --p2p --federated and adhere to the Swarm section's guidance. This feature, while still experimental, offers a tech preview quality experience.
+
+### Federated mode
+
+Federated mode allows to launch multiple LocalAI instances and connect them together in a federated network. This mode is useful when you want to distribute the load of the inference across multiple nodes, but you want to have a single point of entry for the API. In the Swarm section of the WebUI, you can see the instructions to connect multiple instances together.
+
+![346663124-1d2324fd-8b55-4fa2-9856-721a467969c2](https://github.com/user-attachments/assets/19ebd44a-20ff-412c-b92f-cfb8efbe4b21)
+
+To start a LocalAI server in federated mode, run:
+
+```bash
+local-ai run --p2p --federated
+```
+
+This will generate a token that you can use to connect other LocalAI instances to the network or others can use to join the network. If you already have a token, you can specify it using the `TOKEN` environment variable.
+
+To start a load balanced server that routes the requests to the network, run with the `TOKEN`:
+
+```bash
+local-ai federated
+```
+
+To see all the available options, run `local-ai federated --help`.
+
+The instructions are displayed in the "Swarm" section of the WebUI, guiding you through the process of connecting multiple instances.
+
+### Workers mode
+
 {{% alert note %}}
 This feature is available exclusively with llama-cpp compatible models.
 
 This feature was introduced in [LocalAI pull request #2324](https://github.com/mudler/LocalAI/pull/2324) and is based on the upstream work in [llama.cpp pull request #6829](https://github.com/ggerganov/llama.cpp/pull/6829).
 {{% /alert %}}
 
-This functionality enables LocalAI to distribute inference requests across multiple worker nodes, improving efficiency and performance.
+To connect multiple workers to a single LocalAI instance, start first a server in p2p mode:
 
-## Usage
+```bash
+local-ai run --p2p
+```
 
-### Starting Workers
+And navigate the WebUI to the "Swarm" section to see the instructions to connect multiple workers to the network.
+
+![346663124-1d2324fd-8b55-4fa2-9856-721a467969c2](https://github.com/user-attachments/assets/b8cadddf-a467-49cf-a1ed-8850de95366d)
+
+### Without P2P
 
 To start workers for distributing the computational load, run:
 
@@ -23,48 +71,27 @@ To start workers for distributing the computational load, run:
 local-ai worker llama-cpp-rpc <listening_address> <listening_port>
 ```
 
-Alternatively, you can build the RPC server following the llama.cpp [README](https://github.com/ggerganov/llama.cpp/blob/master/examples/rpc/README.md), which is compatible with LocalAI.
-
-### Starting LocalAI
-
-To start the LocalAI server, which handles API requests, specify the worker addresses using the `LLAMACPP_GRPC_SERVERS` environment variable:
+And you can specify the address of the workers when starting LocalAI with the `LLAMACPP_GRPC_SERVERS` environment variable:
 
 ```bash
 LLAMACPP_GRPC_SERVERS="address1:port,address2:port" local-ai run
 ```
-
 The workload on the LocalAI server will then be distributed across the specified nodes.
 
-## Peer-to-Peer Networking
+Alternatively, you can build the RPC workers/server following the llama.cpp [README](https://github.com/ggerganov/llama.cpp/blob/master/examples/rpc/README.md), which is compatible with LocalAI.
 
-![output](https://github.com/mudler/LocalAI/assets/2420543/8ca277cf-c208-4562-8929-808b2324b584)
+## Manual example (worker)
 
-Workers can also connect to each other in a peer-to-peer network, distributing the workload in a decentralized manner.
-
-A shared token between the server and the workers is required for communication within the peer-to-peer network. This feature supports both local network (using mDNS discovery) and DHT for communication across different networks.
-
-The token is automatically generated when starting the server with the `--p2p` flag. Workers can be started with the token using `local-ai worker p2p-llama-cpp-rpc` and specifying the token via the environment variable `TOKEN` or with the `--token` argument.
-
-A network is established between the server and workers using DHT and mDNS discovery protocols. The llama.cpp RPC server is automatically started and exposed to the peer-to-peer network, allowing the API server to connect.
-
-When the HTTP server starts, it discovers workers in the network and creates port forwards to the local service. Llama.cpp is configured to use these services. For more details on the implementation, refer to [LocalAI pull request #2343](https://github.com/mudler/LocalAI/pull/2343).
-
-### Usage
+Use the WebUI to guide you in the process of starting new workers. This example shows the manual steps to highlight the process.
 
 1. Start the server with `--p2p`:
 
 ```bash
 ./local-ai run --p2p
-# 1:02AM INF loading environment variables from file envFile=.env
-# 1:02AM INF Setting logging to info
-# 1:02AM INF P2P mode enabled
-# 1:02AM INF No token provided, generating one
-# 1:02AM INF Generated Token:
-# XXXXXXXXXXX
-# 1:02AM INF Press a button to proceed
+# Get the token in the Swarm section of the WebUI
 ```
 
-Copy the displayed token and press Enter.
+Copy the token from the WebUI or via API call (e.g., `curl http://localhost:8000/p2p/token`) and save it for later use.
 
 To reuse the same token later, restart the server with `--p2ptoken` or `P2P_TOKEN`.
 
@@ -93,12 +120,14 @@ The server logs should indicate that new workers are being discovered.
 
 3. Start inference as usual on the server initiated in step 1.
 
+![output](https://github.com/mudler/LocalAI/assets/2420543/8ca277cf-c208-4562-8929-808b2324b584)
+
 ## Notes
 
 - If running in p2p mode with container images, make sure you start the container with `--net host` or `network_mode: host` in the docker-compose file.
 - Only a single model is supported currently.
 - Ensure the server detects new workers before starting inference. Currently, additional workers cannot be added once inference has begun.
-
+- For more details on the implementation, refer to [LocalAI pull request #2343](https://github.com/mudler/LocalAI/pull/2343)
 
 ## Environment Variables
 

docs/data/version.json

@@ -1,3 +1,3 @@
 {
-  "version": "v2.18.1"
+  "version": "v2.19.1"
 }

examples/chainlit/requirements.txt

@@ -1,6 +1,6 @@
-llama_index==0.10.55
+llama_index==0.10.56
 requests==2.32.3
-weaviate_client==4.6.5
+weaviate_client==4.6.7
 transformers
 torch
 chainlit

examples/functions/requirements.txt

@@ -1,2 +1,2 @@
-langchain==0.2.8
-openai==1.35.13
+langchain==0.2.10
+openai==1.37.0

examples/langchain-chroma/requirements.txt

@@ -1,4 +1,4 @@
-langchain==0.2.8
-openai==1.35.13
+langchain==0.2.10
+openai==1.37.0
 chromadb==0.5.4
-llama-index==0.10.55
+llama-index==0.10.56

examples/langchain/langchainpy-localai-example/requirements.txt

@@ -10,21 +10,21 @@ debugpy==1.8.2
 frozenlist==1.4.1
 greenlet==3.0.3
 idna==3.7
-langchain==0.2.8
-langchain-community==0.2.7
+langchain==0.2.10
+langchain-community==0.2.9
 marshmallow==3.21.3
 marshmallow-enum==1.5.1
 multidict==6.0.5
 mypy-extensions==1.0.0
 numexpr==2.10.1
-numpy==1.26.4
-openai==1.35.13
+numpy==2.0.1
+openai==1.37.0
 openapi-schema-pydantic==1.2.4
 packaging>=23.2
 pydantic==2.8.2
 PyYAML==6.0.1
 requests==2.32.3
-SQLAlchemy==2.0.30
+SQLAlchemy==2.0.31
 tenacity==8.5.0
 tqdm==4.66.4
 typing-inspect==0.9.0

gallery/index.yaml

@@ -24,6 +24,33 @@
     - filename: DeepSeek-Coder-V2-Lite-Instruct-Q4_K_M.gguf
       sha256: 50ec78036433265965ed1afd0667c00c71c12aa70bcf383be462cb8e159db6c0
       uri: huggingface://LoneStriker/DeepSeek-Coder-V2-Lite-Instruct-GGUF/DeepSeek-Coder-V2-Lite-Instruct-Q4_K_M.gguf
+- name: "archangel_sft_pythia2-8b"
+  url: "github:mudler/LocalAI/gallery/tuluv2.yaml@master"
+  icon: https://gist.github.com/assets/29318529/fe2d8391-dbd1-4b7e-9dc4-7cb97e55bc06
+  license: apache-2.0
+  urls:
+    - https://huggingface.co/ContextualAI/archangel_sft_pythia2-8b
+    - https://huggingface.co/RichardErkhov/ContextualAI_-_archangel_sft_pythia2-8b-gguf
+    - https://github.com/ContextualAI/HALOs
+  description: |
+    datasets:
+    - stanfordnlp/SHP
+    - Anthropic/hh-rlhf
+    - OpenAssistant/oasst1
+
+    This repo contains the model checkpoints for:
+    - model family pythia2-8b
+    - optimized with the loss SFT
+    - aligned using the SHP, Anthropic HH and Open Assistant datasets.
+
+    Please refer to our [code repository](https://github.com/ContextualAI/HALOs) or [blog](https://contextual.ai/better-cheaper-faster-llm-alignment-with-kto/) which contains intructions for training your own HALOs and links to our model cards.
+  overrides:
+    parameters:
+      model: archangel_sft_pythia2-8b.Q4_K_M.gguf
+  files:
+    - filename: archangel_sft_pythia2-8b.Q4_K_M.gguf
+      sha256: a47782c55ef2b39b19644213720a599d9849511a73c9ebb0c1de749383c0a0f8
+      uri: huggingface://RichardErkhov/ContextualAI_-_archangel_sft_pythia2-8b-gguf/archangel_sft_pythia2-8b.Q4_K_M.gguf
 - &qwen2
   ## Start QWEN2
   url: "github:mudler/LocalAI/gallery/chatml.yaml@master"
@@ -220,6 +247,36 @@
     - filename: Qwen2-Wukong-7B-Q4_K_M.gguf
       sha256: 6b8ca6649c33fc84d4892ebcff1214f0b34697aced784f0d6d32e284a15943ad
       uri: huggingface://bartowski/Qwen2-Wukong-7B-GGUF/Qwen2-Wukong-7B-Q4_K_M.gguf
+- !!merge <<: *qwen2
+  name: "calme-2.8-qwen2-7b"
+  icon: https://huggingface.co/MaziyarPanahi/calme-2.8-qwen2-7b/resolve/main/qwen2-fine-tunes-maziyar-panahi.webp
+  urls:
+    - https://huggingface.co/MaziyarPanahi/calme-2.8-qwen2-7b
+    - https://huggingface.co/MaziyarPanahi/calme-2.8-qwen2-7b-GGUF
+  description: |
+    This is a fine-tuned version of the Qwen/Qwen2-7B model. It aims to improve the base model across all benchmarks.
+  overrides:
+    parameters:
+      model: Qwen2-7B-Instruct-v0.8.Q4_K_M.gguf
+  files:
+    - filename: Qwen2-7B-Instruct-v0.8.Q4_K_M.gguf
+      sha256: 8c1b3efe9fa6ae1b37942ef26473cb4e0aed0f8038b60d4b61e5bffb61e49b7e
+      uri: huggingface://MaziyarPanahi/calme-2.8-qwen2-7b-GGUF/Qwen2-7B-Instruct-v0.8.Q4_K_M.gguf
+- !!merge <<: *qwen2
+  name: "stellardong-72b-i1"
+  icon: https://huggingface.co/smelborp/StellarDong-72b/resolve/main/stellardong.png
+  urls:
+    - https://huggingface.co/smelborp/StellarDong-72b
+    - https://huggingface.co/mradermacher/StellarDong-72b-i1-GGUF
+  description: |
+    Magnum + Nova = you won't believe how stellar this dong is!!
+  overrides:
+    parameters:
+      model: StellarDong-72b.i1-Q4_K_M.gguf
+  files:
+    - filename: StellarDong-72b.i1-Q4_K_M.gguf
+      sha256: 4c5012f0a034f40a044904891343ade2594f29c28a8a9d8052916de4dc5a61df
+      uri: huggingface://mradermacher/StellarDong-72b-i1-GGUF/StellarDong-72b.i1-Q4_K_M.gguf
 - &mistral03
   ## START Mistral
   url: "github:mudler/LocalAI/gallery/mistral-0.3.yaml@master"
@@ -3146,6 +3203,89 @@
     - filename: L3-8B-Celeste-v1-Q4_K_M.gguf
       sha256: ed5277719965fb6bbcce7d16742e3bac4a8d5b8f52133261a3402a480cd65317
       uri: huggingface://bartowski/L3-8B-Celeste-v1-GGUF/L3-8B-Celeste-v1-Q4_K_M.gguf
+- !!merge <<: *llama3
+  name: "l3-8b-celeste-v1.2"
+  icon: https://cdn-uploads.huggingface.co/production/uploads/630cf5d14ca0a22768bbe10c/Zv__LDTO-nHvpuxPcCgUU.webp
+  urls:
+    - https://huggingface.co/mudler/L3-8B-Celeste-V1.2-Q4_K_M-GGUF
+  description: |
+    Trained on LLaMA 3 8B Instruct at 8K context using Reddit Writing Prompts, Opus 15K Instruct an c2 logs cleaned.
+
+    This is a roleplay model any instruction following capabilities outside roleplay contexts are coincidental.
+  overrides:
+    parameters:
+      model: l3-8b-celeste-v1.2-q4_k_m.gguf
+  files:
+    - filename: l3-8b-celeste-v1.2-q4_k_m.gguf
+      sha256: 7752204c0e9f627ff5726eb69bb6114974cafbc934a993ad019abfba62002783
+      uri: huggingface://mudler/L3-8B-Celeste-V1.2-Q4_K_M-GGUF/l3-8b-celeste-v1.2-q4_k_m.gguf
+- !!merge <<: *llama3
+  name: "llama-3-tulu-2-8b-i1"
+  icon: https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu-v2/Tulu%20V2%20banner.png
+  urls:
+    - https://huggingface.co/allenai/llama-3-tulu-2-8b
+    - https://huggingface.co/mradermacher/llama-3-tulu-2-8b-i1-GGUF
+  description: |
+    Tulu is a series of language models that are trained to act as helpful assistants. Llama 3 Tulu V2 8B is a fine-tuned version of Llama 3 that was trained on a mix of publicly available, synthetic and human datasets.
+  overrides:
+    parameters:
+      model: llama-3-tulu-2-8b.i1-Q4_K_M.gguf
+  files:
+    - filename: llama-3-tulu-2-8b.i1-Q4_K_M.gguf
+      sha256: f859c22bfa64f461e9ffd973dc7ad6a78bb98b1dda6f49abfa416a4022b7e333
+      uri: huggingface://mradermacher/llama-3-tulu-2-8b-i1-GGUF/llama-3-tulu-2-8b.i1-Q4_K_M.gguf
+- !!merge <<: *llama3
+  name: "llama-3-tulu-2-dpo-70b-i1"
+  icon: https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu-v2/Tulu%20V2%20banner.png
+  urls:
+    - https://huggingface.co/allenai/llama-3-tulu-2-dpo-70b
+    - https://huggingface.co/mradermacher/llama-3-tulu-2-dpo-70b-i1-GGUF
+  description: |
+    Tulu is a series of language models that are trained to act as helpful assistants. Llama 3 Tulu V2 8B is a fine-tuned version of Llama 3 that was trained on a mix of publicly available, synthetic and human datasets.
+  overrides:
+    parameters:
+      model: llama-3-tulu-2-dpo-70b.i1-Q4_K_M.gguf
+  files:
+    - filename: llama-3-tulu-2-dpo-70b.i1-Q4_K_M.gguf
+      sha256: fc309bbdf1e2bdced954c4c8dc1f9a885c547017ee5e750bfde645af89e3d3a5
+      uri: huggingface://mradermacher/llama-3-tulu-2-dpo-70b-i1-GGUF/llama-3-tulu-2-dpo-70b.i1-Q4_K_M.gguf
+- !!merge <<: *llama3
+  license: cc-by-nc-4.0
+  name: "suzume-llama-3-8b-multilingual-orpo-borda-top25"
+  icon: https://cdn-uploads.huggingface.co/production/uploads/64b63f8ad57e02621dc93c8b/kWQSu02YfgYdUQqv4s5lq.png
+  urls:
+    - https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top25
+    - https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf
+  description: |
+    This is Suzume ORPO, an ORPO trained fine-tune of the lightblue/suzume-llama-3-8B-multilingual model using our lightblue/mitsu dataset.
+
+    We have trained several versions of this model using ORPO and so recommend that you use the best performing model from our tests, lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half.
+
+    Note that this model has a non-commerical license as we used the Command R and Command R+ models to generate our training data for this model (lightblue/mitsu).
+
+    We are currently working on a developing a commerically usable model, so stay tuned for that!
+  overrides:
+    parameters:
+      model: suzume-llama-3-8B-multilingual-orpo-borda-top25.Q4_K_M.gguf
+  files:
+    - filename: suzume-llama-3-8B-multilingual-orpo-borda-top25.Q4_K_M.gguf
+      sha256: ef75a02c5f38e14a8873c7989188dac6974851b4654279fe1921d2c8018cc388
+      uri: huggingface://RichardErkhov/lightblue_-_suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf/suzume-llama-3-8B-multilingual-orpo-borda-top25.Q4_K_M.gguf
+- !!merge <<: *llama3
+  name: "calme-2.4-llama3-70b"
+  icon: https://huggingface.co/MaziyarPanahi/calme-2.4-llama3-70b/resolve/main/llama-3-merges.webp
+  urls:
+    - https://huggingface.co/MaziyarPanahi/calme-2.4-llama3-70b
+    - https://huggingface.co/mradermacher/calme-2.4-llama3-70b-GGUF
+  description: |
+    This model is a fine-tune (DPO) of meta-llama/Meta-Llama-3-70B-Instruct model.
+  overrides:
+    parameters:
+      model: calme-2.4-llama3-70b.Q4_K_M.gguf
+  files:
+    - filename: calme-2.4-llama3-70b.Q4_K_M.gguf
+      sha256: 0b44ac8a88395dfc60f1b9d3cfffc0ffef74ec0a302e610ef91fc787187568f2
+      uri: huggingface://mradermacher/calme-2.4-llama3-70b-GGUF/calme-2.4-llama3-70b.Q4_K_M.gguf
 - &command-R
   ### START Command-r
   url: "github:mudler/LocalAI/gallery/command-r.yaml@master"
@@ -3405,7 +3545,23 @@
     - filename: phillama-3.8b-v0.1.Q4_K_M.gguf
       sha256: da537d352b7aae54bbad0d2cff3e3a1b0e1dc1e1d25bec3aae1d05cf4faee7a2
       uri: huggingface://RichardErkhov/raincandy-u_-_phillama-3.8b-v0.1-gguf/phillama-3.8b-v0.1.Q4_K_M.gguf
+- !!merge <<: *llama3
+  name: "calme-2.3-phi3-4b"
+  icon: https://huggingface.co/MaziyarPanahi/calme-2.1-phi3-4b/resolve/main/phi-3-instruct.webp
+  urls:
+    - https://huggingface.co/MaziyarPanahi/calme-2.3-phi3-4b
+    - https://huggingface.co/MaziyarPanahi/calme-2.3-phi3-4b-GGUF
+  description: |
+    MaziyarPanahi/calme-2.1-phi3-4b
 
+    This model is a fine-tune (DPO) of microsoft/Phi-3-mini-4k-instruct model.
+  overrides:
+    parameters:
+      model: Phi-3-mini-4k-instruct-v0.3.Q4_K_M.gguf
+  files:
+    - filename: Phi-3-mini-4k-instruct-v0.3.Q4_K_M.gguf
+      sha256: 3a23e1052369c080afb925882bd814cbea5ec859894655a7434c3d49e43a6127
+      uri: huggingface://MaziyarPanahi/calme-2.3-phi3-4b-GGUF/Phi-3-mini-4k-instruct-v0.3.Q4_K_M.gguf
 - &hermes-2-pro-mistral
   ### START Hermes
   url: "github:mudler/LocalAI/gallery/hermes-2-pro-mistral.yaml@master"

gallery/tuluv2.yaml

@@ -0,0 +1,43 @@
+---
+name: "tuluv2"
+
+config_file: |
+  mmap: true
+  template:
+    chat_message: |
+      <|{{ .RoleName }}|>
+      {{ if .FunctionCall -}}
+      Function call:
+      {{ else if eq .RoleName "tool" -}}
+      Function response:
+      {{ end -}}
+      {{ if .Content -}}
+      {{.Content }}
+      {{ end -}}
+      {{ if .FunctionCall -}}
+      {{toJson .FunctionCall}}
+      {{ end -}}
+    function: |
+      <|{{ .RoleName }}|>
+      {{ if .FunctionCall -}}
+      Function call:
+      {{ else if eq .RoleName "tool" -}}
+      Function response:
+      {{ end -}}
+      {{ if .Content -}}
+      {{.Content }}
+      {{ end -}}
+      {{ if .FunctionCall -}}
+      {{toJson .FunctionCall}}
+      {{ end -}}
+    chat: |
+      {{.Input -}}
+      <|assistant|>
+    completion: |
+      {{.Input}}
+  context_size: 4096
+  f16: true
+  stopwords:
+  - '<|im_end|>'
+  - '<dummy32000>'
+  - '<|endoftext|>'

pkg/model/initializers.go

@@ -212,7 +212,7 @@ func selectGRPCProcess(backend, assetDir string, f16 bool) string {
 					grpcProcess = p
 					foundCUDA = true
 				} else {
-					log.Info().Msgf("GPU device found but no CUDA backend present")
+					log.Debug().Msgf("Nvidia GPU device found, no embedded CUDA variant found. You can ignore this message if you are using container with CUDA support")
 				}
 			}
 			if strings.Contains(gpu.String(), "amd") {
@@ -222,7 +222,7 @@ func selectGRPCProcess(backend, assetDir string, f16 bool) string {
 					grpcProcess = p
 					foundAMDGPU = true
 				} else {
-					log.Info().Msgf("GPU device found but no HIPBLAS backend present")
+					log.Debug().Msgf("AMD GPU device found, no embedded HIPBLAS variant found. You can ignore this message if you are using container with HIPBLAS support")
 				}
 			}
 			if strings.Contains(gpu.String(), "intel") {
@@ -236,7 +236,7 @@ func selectGRPCProcess(backend, assetDir string, f16 bool) string {
 					grpcProcess = p
 					foundIntelGPU = true
 				} else {
-					log.Info().Msgf("GPU device found but no Intel backend present")
+					log.Debug().Msgf("Intel GPU device found, no embedded SYCL variant found. You can ignore this message if you are using container with SYCL support")
 				}
 			}
 		}