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docs_updat
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v2.10.0
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3882130911 |
11
Makefile
11
Makefile
@@ -4,11 +4,11 @@ GOVET=$(GOCMD) vet
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BINARY_NAME=local-ai
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# llama.cpp versions
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GOLLAMA_VERSION?=aeba71ee842819da681ea537e78846dc75949ac0
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GOLLAMA_VERSION?=6a8041ef6b46d4712afc3ae791d1c2d73da0ad1c
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GOLLAMA_STABLE_VERSION?=50cee7712066d9e38306eccadcfbb44ea87df4b7
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CPPLLAMA_VERSION?=19885d205e768579ab090d1e99281cae58c21b54
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CPPLLAMA_VERSION?=d84c48505f60bcd358b82a751d40418c4d235643
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# gpt4all version
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GPT4ALL_REPO?=https://github.com/nomic-ai/gpt4all
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@@ -25,7 +25,7 @@ WHISPER_CPP_VERSION?=37a709f6558c6d9783199e2b8cbb136e1c41d346
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BERT_VERSION?=6abe312cded14042f6b7c3cd8edf082713334a4d
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# go-piper version
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PIPER_VERSION?=d6b6275ba037dabdba4a8b65dfdf6b2a73a67f07
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PIPER_VERSION?=9d0100873a7dbb0824dfea40e8cec70a1b110759
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# stablediffusion version
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STABLEDIFFUSION_VERSION?=362df9da29f882dbf09ade61972d16a1f53c3485
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@@ -462,9 +462,6 @@ backend-assets/grpc/llama: backend-assets/grpc sources/go-llama/libbinding.a
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CGO_LDFLAGS="$(CGO_LDFLAGS)" C_INCLUDE_PATH=$(CURDIR)/sources/go-llama LIBRARY_PATH=$(CURDIR)/sources/go-llama \
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$(GOCMD) build -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o backend-assets/grpc/llama ./backend/go/llm/llama/
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# TODO: every binary should have its own folder instead, so can have different implementations
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ifeq ($(BUILD_TYPE),metal)
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cp backend/cpp/llama/llama.cpp/ggml-metal.metal backend-assets/grpc/
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endif
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## BACKEND CPP LLAMA START
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# Sets the variables in case it has to build the gRPC locally.
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@@ -494,7 +491,7 @@ backend-assets/grpc/llama-cpp: backend-assets/grpc backend/cpp/llama/grpc-server
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cp -rfv backend/cpp/llama/grpc-server backend-assets/grpc/llama-cpp
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# TODO: every binary should have its own folder instead, so can have different metal implementations
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ifeq ($(BUILD_TYPE),metal)
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cp backend/cpp/llama/llama.cpp/build/bin/ggml-metal.metal backend-assets/grpc/
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cp backend/cpp/llama/llama.cpp/build/bin/default.metallib backend-assets/grpc/
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endif
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backend-assets/grpc/llama-ggml: backend-assets/grpc sources/go-llama-ggml/libbinding.a
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@@ -191,6 +191,7 @@ message TTSRequest {
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string text = 1;
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string model = 2;
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string dst = 3;
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string voice = 4;
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}
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message TokenizationResponse {
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@@ -18,6 +18,7 @@ else ifeq ($(BUILD_TYPE),clblas)
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# If it's hipblas we do have also to set CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++
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else ifeq ($(BUILD_TYPE),hipblas)
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CMAKE_ARGS+=-DLLAMA_HIPBLAS=ON
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# If it's OSX, DO NOT embed the metal library - -DLLAMA_METAL_EMBED_LIBRARY=ON requires further investigation
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endif
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ifeq ($(BUILD_TYPE),sycl_f16)
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File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@@ -30,6 +30,7 @@ dependencies:
|
||||
- async-timeout==4.0.3
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- attrs==23.1.0
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||||
- bark==0.1.5
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||||
- bitsandbytes==0.43.0
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||||
- boto3==1.28.61
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||||
- botocore==1.31.61
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||||
- certifi==2023.7.22
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||||
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File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@@ -23,7 +23,7 @@ if XPU:
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from intel_extension_for_transformers.transformers.modeling import AutoModelForCausalLM
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from transformers import AutoTokenizer, AutoModel, set_seed
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else:
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from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM, set_seed
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from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM, set_seed, BitsAndBytesConfig
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_ONE_DAY_IN_SECONDS = 60 * 60 * 24
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@@ -75,18 +75,50 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
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A Result object that contains the result of the LoadModel operation.
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"""
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model_name = request.Model
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compute = "auto"
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if request.F16Memory == True:
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compute=torch.bfloat16
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|
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self.CUDA = request.CUDA
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|
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device_map="cpu"
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quantization = None
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if self.CUDA:
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if request.Device:
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device_map=request.Device
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else:
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device_map="cuda:0"
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if request.Quantization == "bnb_4bit":
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quantization = BitsAndBytesConfig(
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load_in_4bit = True,
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bnb_4bit_compute_dtype = compute,
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bnb_4bit_quant_type = "nf4",
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bnb_4bit_use_double_quant = True,
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load_in_8bit = False,
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)
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elif request.Quantization == "bnb_8bit":
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quantization = BitsAndBytesConfig(
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load_in_4bit=False,
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bnb_4bit_compute_dtype = None,
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load_in_8bit=True,
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)
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try:
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if request.Type == "AutoModelForCausalLM":
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if XPU:
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if quantization == "xpu_4bit":
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xpu_4bit = True
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self.model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=request.TrustRemoteCode,
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device_map="xpu", load_in_4bit=True)
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device_map="xpu", load_in_4bit=xpu_4bit)
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||||
else:
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||||
self.model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=request.TrustRemoteCode)
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self.model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=request.TrustRemoteCode, use_safetensors=True, quantization_config=quantization, device_map=device_map, torch_dtype=compute)
|
||||
else:
|
||||
self.model = AutoModel.from_pretrained(model_name, trust_remote_code=request.TrustRemoteCode)
|
||||
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
|
||||
self.CUDA = False
|
||||
self.model = AutoModel.from_pretrained(model_name, trust_remote_code=request.TrustRemoteCode, use_safetensors=True, quantization_config=quantization, device_map=device_map, torch_dtype=compute)
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(model_name, use_safetensors=True)
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||||
self.XPU = False
|
||||
|
||||
if XPU:
|
||||
@@ -97,13 +129,6 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
|
||||
except Exception as err:
|
||||
print("Not using XPU:", err, file=sys.stderr)
|
||||
|
||||
if request.CUDA or torch.cuda.is_available():
|
||||
try:
|
||||
print("Loading model", model_name, "to CUDA.", file=sys.stderr)
|
||||
self.model = self.model.to("cuda")
|
||||
self.CUDA = True
|
||||
except Exception as err:
|
||||
print("Not using CUDA:", err, file=sys.stderr)
|
||||
except Exception as err:
|
||||
return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
|
||||
# Implement your logic here for the LoadModel service
|
||||
@@ -130,13 +155,17 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
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||||
encoded_input = self.tokenizer(request.Embeddings, padding=True, truncation=True, max_length=max_length, return_tensors="pt")
|
||||
|
||||
# Create word embeddings
|
||||
model_output = self.model(**encoded_input)
|
||||
if self.CUDA:
|
||||
encoded_input = encoded_input.to("cuda")
|
||||
|
||||
with torch.no_grad():
|
||||
model_output = self.model(**encoded_input)
|
||||
|
||||
# Pool to get sentence embeddings; i.e. generate one 1024 vector for the entire sentence
|
||||
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']).detach().numpy()
|
||||
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
|
||||
print("Calculated embeddings for: " + request.Embeddings, file=sys.stderr)
|
||||
print("Embeddings:", sentence_embeddings, file=sys.stderr)
|
||||
return backend_pb2.EmbeddingResult(embeddings=sentence_embeddings)
|
||||
return backend_pb2.EmbeddingResult(embeddings=sentence_embeddings[0])
|
||||
|
||||
def Predict(self, request, context):
|
||||
"""
|
||||
@@ -163,12 +192,8 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
|
||||
if XPU:
|
||||
inputs = inputs.to("xpu")
|
||||
|
||||
outputs = self.model.generate(inputs,max_new_tokens=max_tokens, temperature=request.Temperature, top_p=request.TopP)
|
||||
|
||||
generated_text = self.tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
|
||||
# Remove prompt from response if present
|
||||
if request.Prompt in generated_text:
|
||||
generated_text = generated_text.replace(request.Prompt, "")
|
||||
outputs = self.model.generate(inputs,max_new_tokens=max_tokens, temperature=request.Temperature, top_p=request.TopP, do_sample=True, pad_token_id=self.tokenizer.eos_token_id)
|
||||
generated_text = self.tokenizer.batch_decode(outputs[:, inputs.shape[1]:], skip_special_tokens=True)[0]
|
||||
|
||||
return backend_pb2.Reply(message=bytes(generated_text, encoding='utf-8'))
|
||||
|
||||
|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@@ -10,10 +10,6 @@ import (
|
||||
)
|
||||
|
||||
func ModelEmbedding(s string, tokens []int, loader *model.ModelLoader, backendConfig config.BackendConfig, appConfig *config.ApplicationConfig) (func() ([]float32, error), error) {
|
||||
if !backendConfig.Embeddings {
|
||||
return nil, fmt.Errorf("endpoint disabled for this model by API configuration")
|
||||
}
|
||||
|
||||
modelFile := backendConfig.Model
|
||||
|
||||
grpcOpts := gRPCModelOpts(backendConfig)
|
||||
|
||||
@@ -29,7 +29,7 @@ func generateUniqueFileName(dir, baseName, ext string) string {
|
||||
}
|
||||
}
|
||||
|
||||
func ModelTTS(backend, text, modelFile string, loader *model.ModelLoader, appConfig *config.ApplicationConfig, backendConfig config.BackendConfig) (string, *proto.Result, error) {
|
||||
func ModelTTS(backend, text, modelFile, voice string, loader *model.ModelLoader, appConfig *config.ApplicationConfig, backendConfig config.BackendConfig) (string, *proto.Result, error) {
|
||||
bb := backend
|
||||
if bb == "" {
|
||||
bb = model.PiperBackend
|
||||
@@ -44,12 +44,12 @@ func ModelTTS(backend, text, modelFile string, loader *model.ModelLoader, appCon
|
||||
model.WithAssetDir(appConfig.AssetsDestination),
|
||||
model.WithLoadGRPCLoadModelOpts(grpcOpts),
|
||||
})
|
||||
piperModel, err := loader.BackendLoader(opts...)
|
||||
ttsModel, err := loader.BackendLoader(opts...)
|
||||
if err != nil {
|
||||
return "", nil, err
|
||||
}
|
||||
|
||||
if piperModel == nil {
|
||||
if ttsModel == nil {
|
||||
return "", nil, fmt.Errorf("could not load piper model")
|
||||
}
|
||||
|
||||
@@ -57,25 +57,31 @@ func ModelTTS(backend, text, modelFile string, loader *model.ModelLoader, appCon
|
||||
return "", nil, fmt.Errorf("failed creating audio directory: %s", err)
|
||||
}
|
||||
|
||||
fileName := generateUniqueFileName(appConfig.AudioDir, "piper", ".wav")
|
||||
fileName := generateUniqueFileName(appConfig.AudioDir, "tts", ".wav")
|
||||
filePath := filepath.Join(appConfig.AudioDir, fileName)
|
||||
|
||||
// If the model file is not empty, we pass it joined with the model path
|
||||
modelPath := ""
|
||||
if modelFile != "" {
|
||||
if bb != model.TransformersMusicGen {
|
||||
modelPath = filepath.Join(loader.ModelPath, modelFile)
|
||||
if err := utils.VerifyPath(modelPath, appConfig.ModelPath); err != nil {
|
||||
// If the model file is not empty, we pass it joined with the model path
|
||||
// Checking first that it exists and is not outside ModelPath
|
||||
// TODO: we should actually first check if the modelFile is looking like
|
||||
// a FS path
|
||||
mp := filepath.Join(loader.ModelPath, modelFile)
|
||||
if _, err := os.Stat(mp); err == nil {
|
||||
if err := utils.VerifyPath(mp, appConfig.ModelPath); err != nil {
|
||||
return "", nil, err
|
||||
}
|
||||
modelPath = mp
|
||||
} else {
|
||||
modelPath = modelFile
|
||||
}
|
||||
}
|
||||
|
||||
res, err := piperModel.TTS(context.Background(), &proto.TTSRequest{
|
||||
res, err := ttsModel.TTS(context.Background(), &proto.TTSRequest{
|
||||
Text: text,
|
||||
Model: modelPath,
|
||||
Voice: voice,
|
||||
Dst: filePath,
|
||||
})
|
||||
|
||||
|
||||
@@ -6,6 +6,7 @@ import (
|
||||
"os"
|
||||
"strings"
|
||||
|
||||
"github.com/go-skynet/LocalAI/core/http/endpoints/elevenlabs"
|
||||
"github.com/go-skynet/LocalAI/core/http/endpoints/localai"
|
||||
"github.com/go-skynet/LocalAI/core/http/endpoints/openai"
|
||||
|
||||
@@ -21,6 +22,24 @@ import (
|
||||
"github.com/gofiber/fiber/v2/middleware/recover"
|
||||
)
|
||||
|
||||
func readAuthHeader(c *fiber.Ctx) string {
|
||||
authHeader := c.Get("Authorization")
|
||||
|
||||
// elevenlabs
|
||||
xApiKey := c.Get("xi-api-key")
|
||||
if xApiKey != "" {
|
||||
authHeader = "Bearer " + xApiKey
|
||||
}
|
||||
|
||||
// anthropic
|
||||
xApiKey = c.Get("x-api-key")
|
||||
if xApiKey != "" {
|
||||
authHeader = "Bearer " + xApiKey
|
||||
}
|
||||
|
||||
return authHeader
|
||||
}
|
||||
|
||||
func App(cl *config.BackendConfigLoader, ml *model.ModelLoader, appConfig *config.ApplicationConfig) (*fiber.App, error) {
|
||||
// Return errors as JSON responses
|
||||
app := fiber.New(fiber.Config{
|
||||
@@ -94,10 +113,12 @@ func App(cl *config.BackendConfigLoader, ml *model.ModelLoader, appConfig *confi
|
||||
return c.Next()
|
||||
}
|
||||
|
||||
authHeader := c.Get("Authorization")
|
||||
authHeader := readAuthHeader(c)
|
||||
if authHeader == "" {
|
||||
return c.Status(fiber.StatusUnauthorized).JSON(fiber.Map{"message": "Authorization header missing"})
|
||||
}
|
||||
|
||||
// If it's a bearer token
|
||||
authHeaderParts := strings.Split(authHeader, " ")
|
||||
if len(authHeaderParts) != 2 || authHeaderParts[0] != "Bearer" {
|
||||
return c.Status(fiber.StatusUnauthorized).JSON(fiber.Map{"message": "Invalid Authorization header format"})
|
||||
@@ -111,7 +132,6 @@ func App(cl *config.BackendConfigLoader, ml *model.ModelLoader, appConfig *confi
|
||||
}
|
||||
|
||||
return c.Status(fiber.StatusUnauthorized).JSON(fiber.Map{"message": "Invalid API key"})
|
||||
|
||||
}
|
||||
|
||||
if appConfig.CORS {
|
||||
@@ -147,6 +167,11 @@ func App(cl *config.BackendConfigLoader, ml *model.ModelLoader, appConfig *confi
|
||||
app.Get("/models/jobs/:uuid", auth, modelGalleryEndpointService.GetOpStatusEndpoint())
|
||||
app.Get("/models/jobs", auth, modelGalleryEndpointService.GetAllStatusEndpoint())
|
||||
|
||||
app.Post("/tts", auth, localai.TTSEndpoint(cl, ml, appConfig))
|
||||
|
||||
// Elevenlabs
|
||||
app.Post("/v1/text-to-speech/:voice-id", auth, elevenlabs.TTSEndpoint(cl, ml, appConfig))
|
||||
|
||||
// openAI compatible API endpoint
|
||||
|
||||
// chat
|
||||
@@ -181,7 +206,7 @@ func App(cl *config.BackendConfigLoader, ml *model.ModelLoader, appConfig *confi
|
||||
|
||||
// audio
|
||||
app.Post("/v1/audio/transcriptions", auth, openai.TranscriptEndpoint(cl, ml, appConfig))
|
||||
app.Post("/tts", auth, localai.TTSEndpoint(cl, ml, appConfig))
|
||||
app.Post("/v1/audio/speech", auth, localai.TTSEndpoint(cl, ml, appConfig))
|
||||
|
||||
// images
|
||||
app.Post("/v1/images/generations", auth, openai.ImageEndpoint(cl, ml, appConfig))
|
||||
|
||||
55
core/http/endpoints/elevenlabs/tts.go
Normal file
55
core/http/endpoints/elevenlabs/tts.go
Normal file
@@ -0,0 +1,55 @@
|
||||
package elevenlabs
|
||||
|
||||
import (
|
||||
"github.com/go-skynet/LocalAI/core/backend"
|
||||
"github.com/go-skynet/LocalAI/core/config"
|
||||
fiberContext "github.com/go-skynet/LocalAI/core/http/ctx"
|
||||
"github.com/go-skynet/LocalAI/pkg/model"
|
||||
|
||||
"github.com/go-skynet/LocalAI/core/schema"
|
||||
"github.com/gofiber/fiber/v2"
|
||||
"github.com/rs/zerolog/log"
|
||||
)
|
||||
|
||||
func TTSEndpoint(cl *config.BackendConfigLoader, ml *model.ModelLoader, appConfig *config.ApplicationConfig) func(c *fiber.Ctx) error {
|
||||
return func(c *fiber.Ctx) error {
|
||||
|
||||
input := new(schema.ElevenLabsTTSRequest)
|
||||
voiceID := c.Params("voice-id")
|
||||
|
||||
// Get input data from the request body
|
||||
if err := c.BodyParser(input); err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
modelFile, err := fiberContext.ModelFromContext(c, ml, input.ModelID, false)
|
||||
if err != nil {
|
||||
modelFile = input.ModelID
|
||||
log.Warn().Msgf("Model not found in context: %s", input.ModelID)
|
||||
}
|
||||
|
||||
cfg, err := cl.LoadBackendConfigFileByName(modelFile, appConfig.ModelPath,
|
||||
config.LoadOptionDebug(appConfig.Debug),
|
||||
config.LoadOptionThreads(appConfig.Threads),
|
||||
config.LoadOptionContextSize(appConfig.ContextSize),
|
||||
config.LoadOptionF16(appConfig.F16),
|
||||
)
|
||||
if err != nil {
|
||||
modelFile = input.ModelID
|
||||
log.Warn().Msgf("Model not found in context: %s", input.ModelID)
|
||||
} else {
|
||||
if input.ModelID != "" {
|
||||
modelFile = input.ModelID
|
||||
} else {
|
||||
modelFile = cfg.Model
|
||||
}
|
||||
}
|
||||
log.Debug().Msgf("Request for model: %s", modelFile)
|
||||
|
||||
filePath, _, err := backend.ModelTTS(cfg.Backend, input.Text, modelFile, voiceID, ml, appConfig, *cfg)
|
||||
if err != nil {
|
||||
return err
|
||||
}
|
||||
return c.Download(filePath)
|
||||
}
|
||||
}
|
||||
@@ -46,7 +46,7 @@ func TTSEndpoint(cl *config.BackendConfigLoader, ml *model.ModelLoader, appConfi
|
||||
cfg.Backend = input.Backend
|
||||
}
|
||||
|
||||
filePath, _, err := backend.ModelTTS(cfg.Backend, input.Input, modelFile, ml, appConfig, *cfg)
|
||||
filePath, _, err := backend.ModelTTS(cfg.Backend, input.Input, modelFile, input.Voice, ml, appConfig, *cfg)
|
||||
if err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
6
core/schema/elevenlabs.go
Normal file
6
core/schema/elevenlabs.go
Normal file
@@ -0,0 +1,6 @@
|
||||
package schema
|
||||
|
||||
type ElevenLabsTTSRequest struct {
|
||||
Text string `json:"text" yaml:"text"`
|
||||
ModelID string `json:"model_id" yaml:"model_id"`
|
||||
}
|
||||
@@ -17,5 +17,6 @@ type BackendMonitorResponse struct {
|
||||
type TTSRequest struct {
|
||||
Model string `json:"model" yaml:"model"`
|
||||
Input string `json:"input" yaml:"input"`
|
||||
Voice string `json:"voice" yaml:"voice"`
|
||||
Backend string `json:"backend" yaml:"backend"`
|
||||
}
|
||||
|
||||
@@ -272,3 +272,56 @@ curl http://localhost:8080/v1/completions -H "Content-Type: application/json" -d
|
||||
"temperature": 0.1, "top_p": 0.1
|
||||
}'
|
||||
```
|
||||
|
||||
### Transformers
|
||||
|
||||
[Transformers](https://huggingface.co/docs/transformers/index) is a State-of-the-art Machine Learning library for PyTorch, TensorFlow, and JAX.
|
||||
|
||||
LocalAI has a built-in integration with Transformers, and it can be used to run models.
|
||||
|
||||
This is an extra backend - in the container images (the `extra` images already contains python dependencies for Transformers) is already available and there is nothing to do for the setup.
|
||||
|
||||
#### Setup
|
||||
|
||||
Create a YAML file for the model you want to use with `transformers`.
|
||||
|
||||
To setup a model, you need to just specify the model name in the YAML config file:
|
||||
```yaml
|
||||
name: transformers
|
||||
backend: transformers
|
||||
parameters:
|
||||
model: "facebook/opt-125m"
|
||||
type: AutoModelForCausalLM
|
||||
quantization: bnb_4bit # One of: bnb_8bit, bnb_4bit, xpu_4bit (optional)
|
||||
```
|
||||
|
||||
The backend will automatically download the required files in order to run the model.
|
||||
|
||||
#### Parameters
|
||||
|
||||
##### Type
|
||||
|
||||
| Type | Description |
|
||||
| --- | --- |
|
||||
| `AutoModelForCausalLM` | `AutoModelForCausalLM` is a model that can be used to generate sequences. |
|
||||
| N/A | Defaults to `AutoModel` |
|
||||
|
||||
|
||||
##### Quantization
|
||||
|
||||
| Quantization | Description |
|
||||
| --- | --- |
|
||||
| `bnb_8bit` | 8-bit quantization |
|
||||
| `bnb_4bit` | 4-bit quantization |
|
||||
| `xpu_4bit` | 4-bit quantization for Intel XPUs |
|
||||
|
||||
#### Usage
|
||||
|
||||
Use the `completions` endpoint by specifying the `transformers` model:
|
||||
```
|
||||
curl http://localhost:8080/v1/completions -H "Content-Type: application/json" -d '{
|
||||
"model": "transformers",
|
||||
"prompt": "Hello, my name is",
|
||||
"temperature": 0.1, "top_p": 0.1
|
||||
}'
|
||||
```
|
||||
@@ -6,7 +6,13 @@ weight = 11
|
||||
url = "/features/text-to-audio/"
|
||||
+++
|
||||
|
||||
The `/tts` endpoint can be used to generate speech from text.
|
||||
## API Compatibility
|
||||
|
||||
The LocalAI TTS API is compatible with the [OpenAI TTS API](https://platform.openai.com/docs/guides/text-to-speech) and the [Elevenlabs](https://api.elevenlabs.io/docs) API.
|
||||
|
||||
## LocalAI API
|
||||
|
||||
The `/tts` endpoint can also be used to generate speech from text.
|
||||
|
||||
## Usage
|
||||
|
||||
|
||||
10
main.go
10
main.go
@@ -50,7 +50,7 @@ func main() {
|
||||
app := &cli.App{
|
||||
Name: "LocalAI",
|
||||
Version: internal.PrintableVersion(),
|
||||
Usage: "OpenAI compatible API for running LLaMA/GPT models locally on CPU with consumer grade hardware.",
|
||||
Usage: "OpenAI, OSS alternative. Drop-in compatible API for running LLM, GPT and genAI models locally on CPU, GPUs with consumer grade hardware. Supported server endpoints: OpenAI, Elevenlabs",
|
||||
Flags: []cli.Flag{
|
||||
&cli.BoolFlag{
|
||||
Name: "f16",
|
||||
@@ -394,6 +394,12 @@ For a list of compatible model, check out: https://localai.io/model-compatibilit
|
||||
Usage: "Model name to run the TTS",
|
||||
Required: true,
|
||||
},
|
||||
&cli.StringFlag{
|
||||
Name: "voice",
|
||||
Aliases: []string{"v"},
|
||||
Usage: "Voice name to run the TTS (optional)",
|
||||
Required: true,
|
||||
},
|
||||
&cli.StringFlag{
|
||||
Name: "output-file",
|
||||
Aliases: []string{"o"},
|
||||
@@ -427,7 +433,7 @@ For a list of compatible model, check out: https://localai.io/model-compatibilit
|
||||
|
||||
defer ml.StopAllGRPC()
|
||||
|
||||
filePath, _, err := backend.ModelTTS(backendOption, text, modelOption, ml, opts, config.BackendConfig{})
|
||||
filePath, _, err := backend.ModelTTS(backendOption, text, modelOption, ctx.String("voice"), ml, opts, config.BackendConfig{})
|
||||
if err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
// Code generated by protoc-gen-go. DO NOT EDIT.
|
||||
// versions:
|
||||
// protoc-gen-go v1.26.0
|
||||
// protoc v4.25.3
|
||||
// protoc v4.23.4
|
||||
// source: backend.proto
|
||||
|
||||
package proto
|
||||
@@ -1436,6 +1436,7 @@ type TTSRequest struct {
|
||||
Text string `protobuf:"bytes,1,opt,name=text,proto3" json:"text,omitempty"`
|
||||
Model string `protobuf:"bytes,2,opt,name=model,proto3" json:"model,omitempty"`
|
||||
Dst string `protobuf:"bytes,3,opt,name=dst,proto3" json:"dst,omitempty"`
|
||||
Voice string `protobuf:"bytes,4,opt,name=voice,proto3" json:"voice,omitempty"`
|
||||
}
|
||||
|
||||
func (x *TTSRequest) Reset() {
|
||||
@@ -1491,6 +1492,13 @@ func (x *TTSRequest) GetDst() string {
|
||||
return ""
|
||||
}
|
||||
|
||||
func (x *TTSRequest) GetVoice() string {
|
||||
if x != nil {
|
||||
return x.Voice
|
||||
}
|
||||
return ""
|
||||
}
|
||||
|
||||
type TokenizationResponse struct {
|
||||
state protoimpl.MessageState
|
||||
sizeCache protoimpl.SizeCache
|
||||
@@ -1896,84 +1904,86 @@ var file_backend_proto_rawDesc = []byte{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
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|
||||
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||||
}
|
||||
|
||||
var (
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
// Code generated by protoc-gen-go-grpc. DO NOT EDIT.
|
||||
// versions:
|
||||
// - protoc-gen-go-grpc v1.2.0
|
||||
// - protoc v4.25.3
|
||||
// - protoc v4.23.4
|
||||
// source: backend.proto
|
||||
|
||||
package proto
|
||||
|
||||
Reference in New Issue
Block a user