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transformers: correctly load automodels (#1643)

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* backends(transformers): use AutoModel with LLM types

* examples: animagine-xl

* Add codellama examples
This commit is contained in:
Ettore Di Giacinto 2024-01-26 00:13:21 +01:00 committed by GitHub
parent 3733250b3c
commit cb7512734d
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
27 changed files with 1144 additions and 569 deletions
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2
api/backend/options.go
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@ -63,6 +63,8 @@ func gRPCModelOpts(c config.Config) *pb.ModelOptions {
F16Memory: c.F16,
MLock: c.MMlock,
RopeFreqBase: c.RopeFreqBase,
RopeScaling: c.RopeScaling,
Type: c.ModelType,
RopeFreqScale: c.RopeFreqScale,
NUMA: c.NUMA,
Embeddings: c.Embeddings,

4
api/config/config.go
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@ -128,7 +128,9 @@ type LLMConfig struct {
Quantization string `yaml:"quantization"`
MMProj string `yaml:"mmproj"`
RopeScaling string `yaml:"rope_scaling"`
RopeScaling string `yaml:"rope_scaling"`
ModelType string `yaml:"type"`
YarnExtFactor float32 `yaml:"yarn_ext_factor"`
YarnAttnFactor float32 `yaml:"yarn_attn_factor"`
YarnBetaFast float32 `yaml:"yarn_beta_fast"`

2
backend/backend.proto
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@ -134,6 +134,8 @@ message ModelOptions {
float YarnAttnFactor = 45;
float YarnBetaFast = 46;
float YarnBetaSlow = 47;
string Type = 49;
}
message Result {

457
backend/backend_grpc.pb.go Normal file
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@ -0,0 +1,457 @@
// Code generated by protoc-gen-go-grpc. DO NOT EDIT.
// versions:
// - protoc-gen-go-grpc v1.2.0
// - protoc v4.23.4
// source: backend/backend.proto
package proto
import (
context "context"
grpc "google.golang.org/grpc"
codes "google.golang.org/grpc/codes"
status "google.golang.org/grpc/status"
)
// This is a compile-time assertion to ensure that this generated file
// is compatible with the grpc package it is being compiled against.
// Requires gRPC-Go v1.32.0 or later.
const _ = grpc.SupportPackageIsVersion7
// BackendClient is the client API for Backend service.
//
// For semantics around ctx use and closing/ending streaming RPCs, please refer to https://pkg.go.dev/google.golang.org/grpc/?tab=doc#ClientConn.NewStream.
type BackendClient interface {
Health(ctx context.Context, in *HealthMessage, opts ...grpc.CallOption) (*Reply, error)
Predict(ctx context.Context, in *PredictOptions, opts ...grpc.CallOption) (*Reply, error)
LoadModel(ctx context.Context, in *ModelOptions, opts ...grpc.CallOption) (*Result, error)
PredictStream(ctx context.Context, in *PredictOptions, opts ...grpc.CallOption) (Backend_PredictStreamClient, error)
Embedding(ctx context.Context, in *PredictOptions, opts ...grpc.CallOption) (*EmbeddingResult, error)
GenerateImage(ctx context.Context, in *GenerateImageRequest, opts ...grpc.CallOption) (*Result, error)
AudioTranscription(ctx context.Context, in *TranscriptRequest, opts ...grpc.CallOption) (*TranscriptResult, error)
TTS(ctx context.Context, in *TTSRequest, opts ...grpc.CallOption) (*Result, error)
TokenizeString(ctx context.Context, in *PredictOptions, opts ...grpc.CallOption) (*TokenizationResponse, error)
Status(ctx context.Context, in *HealthMessage, opts ...grpc.CallOption) (*StatusResponse, error)
}
type backendClient struct {
cc grpc.ClientConnInterface
}
func NewBackendClient(cc grpc.ClientConnInterface) BackendClient {
return &backendClient{cc}
}
func (c *backendClient) Health(ctx context.Context, in *HealthMessage, opts ...grpc.CallOption) (*Reply, error) {
out := new(Reply)
err := c.cc.Invoke(ctx, "/backend.Backend/Health", in, out, opts...)
if err != nil {
return nil, err
}
return out, nil
}
func (c *backendClient) Predict(ctx context.Context, in *PredictOptions, opts ...grpc.CallOption) (*Reply, error) {
out := new(Reply)
err := c.cc.Invoke(ctx, "/backend.Backend/Predict", in, out, opts...)
if err != nil {
return nil, err
}
return out, nil
}
func (c *backendClient) LoadModel(ctx context.Context, in *ModelOptions, opts ...grpc.CallOption) (*Result, error) {
out := new(Result)
err := c.cc.Invoke(ctx, "/backend.Backend/LoadModel", in, out, opts...)
if err != nil {
return nil, err
}
return out, nil
}
func (c *backendClient) PredictStream(ctx context.Context, in *PredictOptions, opts ...grpc.CallOption) (Backend_PredictStreamClient, error) {
stream, err := c.cc.NewStream(ctx, &Backend_ServiceDesc.Streams[0], "/backend.Backend/PredictStream", opts...)
if err != nil {
return nil, err
}
x := &backendPredictStreamClient{stream}
if err := x.ClientStream.SendMsg(in); err != nil {
return nil, err
}
if err := x.ClientStream.CloseSend(); err != nil {
return nil, err
}
return x, nil
}
type Backend_PredictStreamClient interface {
Recv() (*Reply, error)
grpc.ClientStream
}
type backendPredictStreamClient struct {
grpc.ClientStream
}
func (x *backendPredictStreamClient) Recv() (*Reply, error) {
m := new(Reply)
if err := x.ClientStream.RecvMsg(m); err != nil {
return nil, err
}
return m, nil
}
func (c *backendClient) Embedding(ctx context.Context, in *PredictOptions, opts ...grpc.CallOption) (*EmbeddingResult, error) {
out := new(EmbeddingResult)
err := c.cc.Invoke(ctx, "/backend.Backend/Embedding", in, out, opts...)
if err != nil {
return nil, err
}
return out, nil
}
func (c *backendClient) GenerateImage(ctx context.Context, in *GenerateImageRequest, opts ...grpc.CallOption) (*Result, error) {
out := new(Result)
err := c.cc.Invoke(ctx, "/backend.Backend/GenerateImage", in, out, opts...)
if err != nil {
return nil, err
}
return out, nil
}
func (c *backendClient) AudioTranscription(ctx context.Context, in *TranscriptRequest, opts ...grpc.CallOption) (*TranscriptResult, error) {
out := new(TranscriptResult)
err := c.cc.Invoke(ctx, "/backend.Backend/AudioTranscription", in, out, opts...)
if err != nil {
return nil, err
}
return out, nil
}
func (c *backendClient) TTS(ctx context.Context, in *TTSRequest, opts ...grpc.CallOption) (*Result, error) {
out := new(Result)
err := c.cc.Invoke(ctx, "/backend.Backend/TTS", in, out, opts...)
if err != nil {
return nil, err
}
return out, nil
}
func (c *backendClient) TokenizeString(ctx context.Context, in *PredictOptions, opts ...grpc.CallOption) (*TokenizationResponse, error) {
out := new(TokenizationResponse)
err := c.cc.Invoke(ctx, "/backend.Backend/TokenizeString", in, out, opts...)
if err != nil {
return nil, err
}
return out, nil
}
func (c *backendClient) Status(ctx context.Context, in *HealthMessage, opts ...grpc.CallOption) (*StatusResponse, error) {
out := new(StatusResponse)
err := c.cc.Invoke(ctx, "/backend.Backend/Status", in, out, opts...)
if err != nil {
return nil, err
}
return out, nil
}
// BackendServer is the server API for Backend service.
// All implementations must embed UnimplementedBackendServer
// for forward compatibility
type BackendServer interface {
Health(context.Context, *HealthMessage) (*Reply, error)
Predict(context.Context, *PredictOptions) (*Reply, error)
LoadModel(context.Context, *ModelOptions) (*Result, error)
PredictStream(*PredictOptions, Backend_PredictStreamServer) error
Embedding(context.Context, *PredictOptions) (*EmbeddingResult, error)
GenerateImage(context.Context, *GenerateImageRequest) (*Result, error)
AudioTranscription(context.Context, *TranscriptRequest) (*TranscriptResult, error)
TTS(context.Context, *TTSRequest) (*Result, error)
TokenizeString(context.Context, *PredictOptions) (*TokenizationResponse, error)
Status(context.Context, *HealthMessage) (*StatusResponse, error)
mustEmbedUnimplementedBackendServer()
}
// UnimplementedBackendServer must be embedded to have forward compatible implementations.
type UnimplementedBackendServer struct {
}
func (UnimplementedBackendServer) Health(context.Context, *HealthMessage) (*Reply, error) {
return nil, status.Errorf(codes.Unimplemented, "method Health not implemented")
}
func (UnimplementedBackendServer) Predict(context.Context, *PredictOptions) (*Reply, error) {
return nil, status.Errorf(codes.Unimplemented, "method Predict not implemented")
}
func (UnimplementedBackendServer) LoadModel(context.Context, *ModelOptions) (*Result, error) {
return nil, status.Errorf(codes.Unimplemented, "method LoadModel not implemented")
}
func (UnimplementedBackendServer) PredictStream(*PredictOptions, Backend_PredictStreamServer) error {
return status.Errorf(codes.Unimplemented, "method PredictStream not implemented")
}
func (UnimplementedBackendServer) Embedding(context.Context, *PredictOptions) (*EmbeddingResult, error) {
return nil, status.Errorf(codes.Unimplemented, "method Embedding not implemented")
}
func (UnimplementedBackendServer) GenerateImage(context.Context, *GenerateImageRequest) (*Result, error) {
return nil, status.Errorf(codes.Unimplemented, "method GenerateImage not implemented")
}
func (UnimplementedBackendServer) AudioTranscription(context.Context, *TranscriptRequest) (*TranscriptResult, error) {
return nil, status.Errorf(codes.Unimplemented, "method AudioTranscription not implemented")
}
func (UnimplementedBackendServer) TTS(context.Context, *TTSRequest) (*Result, error) {
return nil, status.Errorf(codes.Unimplemented, "method TTS not implemented")
}
func (UnimplementedBackendServer) TokenizeString(context.Context, *PredictOptions) (*TokenizationResponse, error) {
return nil, status.Errorf(codes.Unimplemented, "method TokenizeString not implemented")
}
func (UnimplementedBackendServer) Status(context.Context, *HealthMessage) (*StatusResponse, error) {
return nil, status.Errorf(codes.Unimplemented, "method Status not implemented")
}
func (UnimplementedBackendServer) mustEmbedUnimplementedBackendServer() {}
// UnsafeBackendServer may be embedded to opt out of forward compatibility for this service.
// Use of this interface is not recommended, as added methods to BackendServer will
// result in compilation errors.
type UnsafeBackendServer interface {
mustEmbedUnimplementedBackendServer()
}
func RegisterBackendServer(s grpc.ServiceRegistrar, srv BackendServer) {
s.RegisterService(&Backend_ServiceDesc, srv)
}
func _Backend_Health_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) {
in := new(HealthMessage)
if err := dec(in); err != nil {
return nil, err
}
if interceptor == nil {
return srv.(BackendServer).Health(ctx, in)
}
info := &grpc.UnaryServerInfo{
Server: srv,
FullMethod: "/backend.Backend/Health",
}
handler := func(ctx context.Context, req interface{}) (interface{}, error) {
return srv.(BackendServer).Health(ctx, req.(*HealthMessage))
}
return interceptor(ctx, in, info, handler)
}
func _Backend_Predict_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) {
in := new(PredictOptions)
if err := dec(in); err != nil {
return nil, err
}
if interceptor == nil {
return srv.(BackendServer).Predict(ctx, in)
}
info := &grpc.UnaryServerInfo{
Server: srv,
FullMethod: "/backend.Backend/Predict",
}
handler := func(ctx context.Context, req interface{}) (interface{}, error) {
return srv.(BackendServer).Predict(ctx, req.(*PredictOptions))
}
return interceptor(ctx, in, info, handler)
}
func _Backend_LoadModel_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) {
in := new(ModelOptions)
if err := dec(in); err != nil {
return nil, err
}
if interceptor == nil {
return srv.(BackendServer).LoadModel(ctx, in)
}
info := &grpc.UnaryServerInfo{
Server: srv,
FullMethod: "/backend.Backend/LoadModel",
}
handler := func(ctx context.Context, req interface{}) (interface{}, error) {
return srv.(BackendServer).LoadModel(ctx, req.(*ModelOptions))
}
return interceptor(ctx, in, info, handler)
}
func _Backend_PredictStream_Handler(srv interface{}, stream grpc.ServerStream) error {
m := new(PredictOptions)
if err := stream.RecvMsg(m); err != nil {
return err
}
return srv.(BackendServer).PredictStream(m, &backendPredictStreamServer{stream})
}
type Backend_PredictStreamServer interface {
Send(*Reply) error
grpc.ServerStream
}
type backendPredictStreamServer struct {
grpc.ServerStream
}
func (x *backendPredictStreamServer) Send(m *Reply) error {
return x.ServerStream.SendMsg(m)
}
func _Backend_Embedding_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) {
in := new(PredictOptions)
if err := dec(in); err != nil {
return nil, err
}
if interceptor == nil {
return srv.(BackendServer).Embedding(ctx, in)
}
info := &grpc.UnaryServerInfo{
Server: srv,
FullMethod: "/backend.Backend/Embedding",
}
handler := func(ctx context.Context, req interface{}) (interface{}, error) {
return srv.(BackendServer).Embedding(ctx, req.(*PredictOptions))
}
return interceptor(ctx, in, info, handler)
}
func _Backend_GenerateImage_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) {
in := new(GenerateImageRequest)
if err := dec(in); err != nil {
return nil, err
}
if interceptor == nil {
return srv.(BackendServer).GenerateImage(ctx, in)
}
info := &grpc.UnaryServerInfo{
Server: srv,
FullMethod: "/backend.Backend/GenerateImage",
}
handler := func(ctx context.Context, req interface{}) (interface{}, error) {
return srv.(BackendServer).GenerateImage(ctx, req.(*GenerateImageRequest))
}
return interceptor(ctx, in, info, handler)
}
func _Backend_AudioTranscription_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) {
in := new(TranscriptRequest)
if err := dec(in); err != nil {
return nil, err
}
if interceptor == nil {
return srv.(BackendServer).AudioTranscription(ctx, in)
}
info := &grpc.UnaryServerInfo{
Server: srv,
FullMethod: "/backend.Backend/AudioTranscription",
}
handler := func(ctx context.Context, req interface{}) (interface{}, error) {
return srv.(BackendServer).AudioTranscription(ctx, req.(*TranscriptRequest))
}
return interceptor(ctx, in, info, handler)
}
func _Backend_TTS_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) {
in := new(TTSRequest)
if err := dec(in); err != nil {
return nil, err
}
if interceptor == nil {
return srv.(BackendServer).TTS(ctx, in)
}
info := &grpc.UnaryServerInfo{
Server: srv,
FullMethod: "/backend.Backend/TTS",
}
handler := func(ctx context.Context, req interface{}) (interface{}, error) {
return srv.(BackendServer).TTS(ctx, req.(*TTSRequest))
}
return interceptor(ctx, in, info, handler)
}
func _Backend_TokenizeString_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) {
in := new(PredictOptions)
if err := dec(in); err != nil {
return nil, err
}
if interceptor == nil {
return srv.(BackendServer).TokenizeString(ctx, in)
}
info := &grpc.UnaryServerInfo{
Server: srv,
FullMethod: "/backend.Backend/TokenizeString",
}
handler := func(ctx context.Context, req interface{}) (interface{}, error) {
return srv.(BackendServer).TokenizeString(ctx, req.(*PredictOptions))
}
return interceptor(ctx, in, info, handler)
}
func _Backend_Status_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) {
in := new(HealthMessage)
if err := dec(in); err != nil {
return nil, err
}
if interceptor == nil {
return srv.(BackendServer).Status(ctx, in)
}
info := &grpc.UnaryServerInfo{
Server: srv,
FullMethod: "/backend.Backend/Status",
}
handler := func(ctx context.Context, req interface{}) (interface{}, error) {
return srv.(BackendServer).Status(ctx, req.(*HealthMessage))
}
return interceptor(ctx, in, info, handler)
}
// Backend_ServiceDesc is the grpc.ServiceDesc for Backend service.
// It's only intended for direct use with grpc.RegisterService,
// and not to be introspected or modified (even as a copy)
var Backend_ServiceDesc = grpc.ServiceDesc{
ServiceName: "backend.Backend",
HandlerType: (*BackendServer)(nil),
Methods: []grpc.MethodDesc{
{
MethodName: "Health",
Handler: _Backend_Health_Handler,
},
{
MethodName: "Predict",
Handler: _Backend_Predict_Handler,
},
{
MethodName: "LoadModel",
Handler: _Backend_LoadModel_Handler,
},
{
MethodName: "Embedding",
Handler: _Backend_Embedding_Handler,
},
{
MethodName: "GenerateImage",
Handler: _Backend_GenerateImage_Handler,
},
{
MethodName: "AudioTranscription",
Handler: _Backend_AudioTranscription_Handler,
},
{
MethodName: "TTS",
Handler: _Backend_TTS_Handler,
},
{
MethodName: "TokenizeString",
Handler: _Backend_TokenizeString_Handler,
},
{
MethodName: "Status",
Handler: _Backend_Status_Handler,
},
},
Streams: []grpc.StreamDesc{
{
StreamName: "PredictStream",
Handler: _Backend_PredictStream_Handler,
ServerStreams: true,
},
},
Metadata: "backend/backend.proto",
}

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backend/python/autogptq/backend_pb2.py
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backend/python/bark/backend_pb2.py
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backend/python/diffusers/backend_pb2.py
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backend/python/exllama/backend_pb2.py
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backend/python/exllama2/backend_pb2.py
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backend/python/mamba/backend_pb2.py
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backend/python/petals/backend_pb2.py
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backend/python/transformers/backend_pb2.py
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22
backend/python/transformers/transformers_server.py
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@ -16,7 +16,7 @@ import backend_pb2_grpc
import grpc
import torch
import torch.cuda
from transformers import AutoTokenizer, AutoModel
from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM, set_seed
_ONE_DAY_IN_SECONDS = 60 * 60 * 24
@ -68,12 +68,19 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
"""
model_name = request.Model
try:
self.model = AutoModel.from_pretrained(model_name, trust_remote_code=True) # trust_remote_code is needed to use the encode method with embeddings models like jinai-v2
if request.Type == "AutoModelForCausalLM":
self.model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
else:
self.model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.CUDA = False
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:
@ -94,6 +101,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
An EmbeddingResult object that contains the calculated embeddings.
"""
set_seed(request.Seed)
# Tokenize input
max_length = 512
if request.Tokens != 0:
@ -120,6 +128,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
Returns:
backend_pb2.Reply: The predict result.
"""
set_seed(request.Seed)
if request.TopP == 0:
request.TopP = 0.9
@ -127,10 +136,13 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
if request.Tokens > 0:
max_tokens = request.Tokens
inputs = self.tokenizer.tokenizer(request.Prompt, return_tensors="pt").input_ids
outputs = self.model.generate(inputs,max_tokens=max_tokens, temperature=request.Temperature, top_p=request.TopP)
inputs = self.tokenizer(request.Prompt, return_tensors="pt").input_ids
if self.CUDA:
inputs = inputs.to("cuda")
generated_text = self.tokenizer.tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
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, "")

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2
docs/content/docs/getting-started/customize-model.md
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@ -26,6 +26,8 @@ Here's an example to initiate the **phi-2** model:
docker run -p 8080:8080 localai/localai:{{< version >}}-ffmpeg-core https://gist.githubusercontent.com/mudler/ad601a0488b497b69ec549150d9edd18/raw/a8a8869ef1bb7e3830bf5c0bae29a0cce991ff8d/phi-2.yaml
```
You can also check all the embedded models configurations [here](https://github.com/mudler/LocalAI/tree/master/embedded/models).
{{% alert icon="" %}}
The model configurations used in the quickstart are accessible here: [https://github.com/mudler/LocalAI/tree/master/embedded/models](https://github.com/mudler/LocalAI/tree/master/embedded/models). Contributions are welcome; please feel free to submit a Pull Request.

15
docs/content/docs/getting-started/quickstart.md
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@ -40,7 +40,7 @@ There are different categories of models: [LLMs]({{%relref "docs/features/text-g
{{% alert icon="💡" %}}
To customize the models, see [Model customization]({{%relref "docs/getting-started/customize-model" %}}). For more model configurations, visit the [Examples Section](https://github.com/mudler/LocalAI/tree/master/examples/configurations).
To customize the models, see [Model customization]({{%relref "docs/getting-started/customize-model" %}}). For more model configurations, visit the [Examples Section](https://github.com/mudler/LocalAI/tree/master/examples/configurations) and the configurations for the models below is available [here](https://github.com/mudler/LocalAI/tree/master/embedded/models).
{{% /alert %}}
{{< tabs tabTotal="3" >}}
@ -64,6 +64,10 @@ To customize the models, see [Model customization]({{%relref "docs/getting-start
| [tinyllama-chat](https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v0.3-GGUF) [original model](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.3) | [LLM]({{%relref "docs/features/text-generation" %}}) | ```docker run -ti -p 8080:8080 localai/localai:{{< version >}}-ffmpeg-core tinyllama-chat``` |
| [dolphin-2.5-mixtral-8x7b](https://huggingface.co/TheBloke/dolphin-2.5-mixtral-8x7b-GGUF) | [LLM]({{%relref "docs/features/text-generation" %}}) | ```docker run -ti -p 8080:8080 localai/localai:{{< version >}}-ffmpeg-core dolphin-2.5-mixtral-8x7b``` |
| 🐍 [mamba](https://github.com/state-spaces/mamba) | [LLM]({{%relref "docs/features/text-generation" %}}) | GPU-only |
| animagine-xl | [Text to Image]({{%relref "docs/features/image-generation" %}}) | GPU-only |
| transformers-tinyllama | [LLM]({{%relref "docs/features/text-generation" %}}) | GPU-only |
| [codellama-7b](https://huggingface.co/codellama/CodeLlama-7b-hf) (with transformers) | [LLM]({{%relref "docs/features/text-generation" %}}) | GPU-only |
| [codellama-7b-gguf](https://huggingface.co/TheBloke/CodeLlama-7B-GGUF) (with llama.cpp) | [LLM]({{%relref "docs/features/text-generation" %}}) | ```docker run -ti -p 8080:8080 localai/localai:{{< version >}}-ffmpeg-core codellama-7b-gguf``` |
{{% /tab %}}
{{% tab tabName="GPU (CUDA 11)" %}}
@ -86,7 +90,10 @@ To customize the models, see [Model customization]({{%relref "docs/getting-start
| [tinyllama-chat](https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v0.3-GGUF) [original model](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.3) | [LLM]({{%relref "docs/features/text-generation" %}}) | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda11-core tinyllama-chat``` |
| [dolphin-2.5-mixtral-8x7b](https://huggingface.co/TheBloke/dolphin-2.5-mixtral-8x7b-GGUF) | [LLM]({{%relref "docs/features/text-generation" %}}) | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda11-core dolphin-2.5-mixtral-8x7b``` |
| 🐍 [mamba](https://github.com/state-spaces/mamba) | [LLM]({{%relref "docs/features/text-generation" %}}) | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda11 mamba-chat``` |
| animagine-xl | [Text to Image]({{%relref "docs/features/image-generation" %}}) | ```docker run -ti -p 8080:8080 -e COMPEL=0 --gpus all localai/localai:{{< version >}}-cublas-cuda11 animagine-xl``` |
| transformers-tinyllama | [LLM]({{%relref "docs/features/text-generation" %}}) | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda11 transformers-tinyllama``` |
| [codellama-7b](https://huggingface.co/codellama/CodeLlama-7b-hf) | [LLM]({{%relref "docs/features/text-generation" %}}) | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda11 codellama-7b``` |
| [codellama-7b-gguf](https://huggingface.co/TheBloke/CodeLlama-7B-GGUF) | [LLM]({{%relref "docs/features/text-generation" %}}) | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda11-core codellama-7b-gguf``` |
{{% /tab %}}
@ -110,6 +117,10 @@ To customize the models, see [Model customization]({{%relref "docs/getting-start
| [tinyllama-chat](https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v0.3-GGUF) [original model](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.3) | [LLM]({{%relref "docs/features/text-generation" %}}) | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda12-core tinyllama-chat``` |
| [dolphin-2.5-mixtral-8x7b](https://huggingface.co/TheBloke/dolphin-2.5-mixtral-8x7b-GGUF) | [LLM]({{%relref "docs/features/text-generation" %}}) | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda12-core dolphin-2.5-mixtral-8x7b``` |
| 🐍 [mamba](https://github.com/state-spaces/mamba) | [LLM]({{%relref "docs/features/text-generation" %}}) | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda12 mamba-chat``` |
| animagine-xl | [Text to Image]({{%relref "docs/features/image-generation" %}}) | GPU-only | ```docker run -ti -p 8080:8080 -e COMPEL=0 --gpus all localai/localai:{{< version >}}-cublas-cuda12 animagine-xl``` |
| transformers-tinyllama | [LLM]({{%relref "docs/features/text-generation" %}}) | GPU-only | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda12 transformers-tinyllama``` |
| [codellama-7b](https://huggingface.co/codellama/CodeLlama-7b-hf) | [LLM]({{%relref "docs/features/text-generation" %}}) | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda12 codellama-7b``` |
| [codellama-7b-gguf](https://huggingface.co/TheBloke/CodeLlama-7B-GGUF) | [LLM]({{%relref "docs/features/text-generation" %}}) | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda12-core codellama-7b-gguf``` |
{{% /tab %}}
{{< /tabs >}}

17
embedded/models/animagine-xl.yaml Normal file
View File

@ -0,0 +1,17 @@
name: animagine-xl
parameters:
model: Linaqruf/animagine-xl
backend: diffusers
f16: true
diffusers:
scheduler_type: euler_a
usage: |
curl http://localhost:8080/v1/images/generations \
-H "Content-Type: application/json" \
-d '{
"prompt": "<positive prompt>|<negative prompt>",
"model": "animagine-xl",
"step": 51,
"size": "1024x1024"
}'

16
embedded/models/codellama-7b-gguf.yaml Normal file
View File

@ -0,0 +1,16 @@
name: codellama-7b-gguf
backend: transformers
parameters:
model: huggingface://TheBloke/CodeLlama-7B-GGUF/codellama-7b.Q4_K_M.gguf
temperature: 0.2
top_k: 40
seed: -1
top_p: 0.95
context_size: 4096
f16: true
gpu_layers: 90
usage: |
curl http://localhost:8080/v1/completions -H "Content-Type: application/json" -d '{
"model": "codellama-7b-gguf",
"prompt": "import socket\n\ndef ping_exponential_backoff(host: str):"
}'

14
embedded/models/codellama-7b.yaml Normal file
View File

@ -0,0 +1,14 @@
name: codellama-7b
backend: transformers
parameters:
model: codellama/CodeLlama-7b-hf
temperature: 0.2
top_k: 40
seed: -1
top_p: 0.95
usage: |
curl http://localhost:8080/v1/completions -H "Content-Type: application/json" -d '{
"model": "codellama-7b",
"prompt": "import socket\n\ndef ping_exponential_backoff(host: str):"
}'

2
embedded/models/dolphin-2.5-mixtral-8x7b.yaml
View File

@ -1,7 +1,7 @@
name: dolphin-mixtral-8x7b
mmap: true
parameters:
model: huggingface://TheBloke/dolphin-2.5-mixtral-8x7b-GGUF/blob/main/dolphin-2.5-mixtral-8x7b.Q2_K.gguf
model: huggingface://TheBloke/dolphin-2.5-mixtral-8x7b-GGUF/dolphin-2.5-mixtral-8x7b.Q2_K.gguf
temperature: 0.2
top_k: 40
top_p: 0.95

32
embedded/models/transformers-tinyllama.yaml Normal file
View File

@ -0,0 +1,32 @@
name: tinyllama-chat
backend: transformers
type: AutoModelForCausalLM
parameters:
model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
temperature: 0.2
top_k: 40
seed: -1
top_p: 0.95
max_tokens: 4096
template:
chat_message: |
<|im_start|>{{if eq .RoleName "assistant"}}assistant{{else if eq .RoleName "system"}}system{{else if eq .RoleName "user"}}user{{end}}
{{if .Content}}{{.Content}}{{end}}<|im_end|>
chat: |
{{.Input}}
<|im_start|>assistant
completion: |
{{.Input}}
stopwords:
- <|im_end|>
usage: |
curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
"model": "tinyllama-chat",
"messages": [{"role": "user", "content": "Say this is a test!"}],
"temperature": 0.7
}'

268
pkg/grpc/proto/backend.pb.go
View File

@ -1,7 +1,7 @@
// Code generated by protoc-gen-go. DO NOT EDIT.
// versions:
// protoc-gen-go v1.28.1
// protoc v3.6.1
// protoc-gen-go v1.26.0
// protoc v4.23.4
// source: backend.proto
package proto
@ -567,8 +567,7 @@ type ModelOptions struct {
CLIPSubfolder string `protobuf:"bytes,32,opt,name=CLIPSubfolder,proto3" json:"CLIPSubfolder,omitempty"`
CLIPSkip int32 `protobuf:"varint,33,opt,name=CLIPSkip,proto3" json:"CLIPSkip,omitempty"`
ControlNet string `protobuf:"bytes,48,opt,name=ControlNet,proto3" json:"ControlNet,omitempty"`
// RWKV
Tokenizer string `protobuf:"bytes,34,opt,name=Tokenizer,proto3" json:"Tokenizer,omitempty"`
Tokenizer string `protobuf:"bytes,34,opt,name=Tokenizer,proto3" json:"Tokenizer,omitempty"`
// LLM (llama.cpp)
LoraBase string `protobuf:"bytes,35,opt,name=LoraBase,proto3" json:"LoraBase,omitempty"`
LoraAdapter string `protobuf:"bytes,36,opt,name=LoraAdapter,proto3" json:"LoraAdapter,omitempty"`
@ -584,6 +583,7 @@ type ModelOptions struct {
YarnAttnFactor float32 `protobuf:"fixed32,45,opt,name=YarnAttnFactor,proto3" json:"YarnAttnFactor,omitempty"`
YarnBetaFast float32 `protobuf:"fixed32,46,opt,name=YarnBetaFast,proto3" json:"YarnBetaFast,omitempty"`
YarnBetaSlow float32 `protobuf:"fixed32,47,opt,name=YarnBetaSlow,proto3" json:"YarnBetaSlow,omitempty"`
Type string `protobuf:"bytes,49,opt,name=Type,proto3" json:"Type,omitempty"`
}
func (x *ModelOptions) Reset() {
@ -954,6 +954,13 @@ func (x *ModelOptions) GetYarnBetaSlow() float32 {
return 0
}
func (x *ModelOptions) GetType() string {
if x != nil {
return x.Type
}
return ""
}
type Result struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
@ -1696,7 +1703,7 @@ var file_backend_proto_rawDesc = []byte{
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@ -1789,131 +1796,132 @@ var file_backend_proto_rawDesc = []byte{
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}
var (

2
pkg/grpc/proto/backend_grpc.pb.go
View File

@ -1,7 +1,7 @@
// Code generated by protoc-gen-go-grpc. DO NOT EDIT.
// versions:
// - protoc-gen-go-grpc v1.2.0
// - protoc v3.6.1
// - protoc v4.23.4
// source: backend.proto
package proto