7dee25a07f
get model and file type from bin file
100 lines
3.1 KiB
Go
100 lines
3.1 KiB
Go
package llm
|
|
|
|
import (
|
|
"context"
|
|
"fmt"
|
|
"log"
|
|
"os"
|
|
|
|
"github.com/pbnjay/memory"
|
|
|
|
"github.com/jmorganca/ollama/api"
|
|
)
|
|
|
|
type LLM interface {
|
|
Predict(context.Context, []int, string, func(api.GenerateResponse)) error
|
|
Embedding(context.Context, string) ([]float64, error)
|
|
Encode(context.Context, string) ([]int, error)
|
|
Decode(context.Context, []int) (string, error)
|
|
SetOptions(api.Options)
|
|
Close()
|
|
Ping(context.Context) error
|
|
}
|
|
|
|
func New(model string, adapters []string, opts api.Options) (LLM, error) {
|
|
if _, err := os.Stat(model); err != nil {
|
|
return nil, err
|
|
}
|
|
|
|
f, err := os.Open(model)
|
|
if err != nil {
|
|
return nil, err
|
|
}
|
|
defer f.Close()
|
|
|
|
ggml, err := DecodeGGML(f)
|
|
if err != nil {
|
|
return nil, err
|
|
}
|
|
|
|
switch ggml.FileType() {
|
|
case "Q8_0":
|
|
if ggml.Name() != "gguf" && opts.NumGPU != 0 {
|
|
// GGML Q8_0 do not support Metal API and will
|
|
// cause the runner to segmentation fault so disable GPU
|
|
log.Printf("WARNING: GPU disabled for F32, Q5_0, Q5_1, and Q8_0")
|
|
opts.NumGPU = 0
|
|
}
|
|
case "F32", "Q5_0", "Q5_1":
|
|
if opts.NumGPU != 0 {
|
|
// F32, Q5_0, Q5_1, and Q8_0 do not support Metal API and will
|
|
// cause the runner to segmentation fault so disable GPU
|
|
log.Printf("WARNING: GPU disabled for F32, Q5_0, Q5_1, and Q8_0")
|
|
opts.NumGPU = 0
|
|
}
|
|
}
|
|
|
|
totalResidentMemory := memory.TotalMemory()
|
|
switch ggml.ModelType() {
|
|
case "3B", "7B":
|
|
if ggml.FileType() == "F16" && totalResidentMemory < 16*1024*1024 {
|
|
return nil, fmt.Errorf("F16 model requires at least 16GB of memory")
|
|
} else if totalResidentMemory < 8*1024*1024 {
|
|
return nil, fmt.Errorf("model requires at least 8GB of memory")
|
|
}
|
|
case "13B":
|
|
if ggml.FileType() == "F16" && totalResidentMemory < 32*1024*1024 {
|
|
return nil, fmt.Errorf("F16 model requires at least 32GB of memory")
|
|
} else if totalResidentMemory < 16*1024*1024 {
|
|
return nil, fmt.Errorf("model requires at least 16GB of memory")
|
|
}
|
|
case "30B", "34B", "40B":
|
|
if ggml.FileType() == "F16" && totalResidentMemory < 64*1024*1024 {
|
|
return nil, fmt.Errorf("F16 model requires at least 64GB of memory")
|
|
} else if totalResidentMemory < 32*1024*1024 {
|
|
return nil, fmt.Errorf("model requires at least 32GB of memory")
|
|
}
|
|
case "65B", "70B":
|
|
if ggml.FileType() == "F16" && totalResidentMemory < 128*1024*1024 {
|
|
return nil, fmt.Errorf("F16 model requires at least 128GB of memory")
|
|
} else if totalResidentMemory < 64*1024*1024 {
|
|
return nil, fmt.Errorf("model requires at least 64GB of memory")
|
|
}
|
|
case "180B":
|
|
if ggml.FileType() == "F16" && totalResidentMemory < 512*1024*1024 {
|
|
return nil, fmt.Errorf("F16 model requires at least 512GB of memory")
|
|
} else if totalResidentMemory < 128*1024*1024 {
|
|
return nil, fmt.Errorf("model requires at least 128GB of memory")
|
|
}
|
|
}
|
|
|
|
switch ggml.Name() {
|
|
case "gguf":
|
|
opts.NumGQA = 0 // TODO: remove this when llama.cpp runners differ enough to need separate newLlama functions
|
|
return newLlama(model, adapters, ggufRunner(), opts)
|
|
case "ggml", "ggmf", "ggjt", "ggla":
|
|
return newLlama(model, adapters, ggmlRunner(), opts)
|
|
default:
|
|
return nil, fmt.Errorf("unknown ggml type: %s", ggml.ModelFamily())
|
|
}
|
|
}
|