4cba75efc5
* remove tmp directories created by previous servers * clean up on server stop * Update routes.go * Update server/routes.go Co-authored-by: Jeffrey Morgan <jmorganca@gmail.com> * create top-level temp ollama dir * check file exists before creating --------- Co-authored-by: Jeffrey Morgan <jmorganca@gmail.com> Co-authored-by: Michael Yang <mxyng@pm.me>
100 lines
3.1 KiB
Go
100 lines
3.1 KiB
Go
package llm
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import (
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"context"
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"fmt"
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"log"
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"os"
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"github.com/pbnjay/memory"
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"github.com/jmorganca/ollama/api"
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)
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type LLM interface {
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Predict(context.Context, []int, string, func(api.GenerateResponse)) error
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Embedding(context.Context, string) ([]float64, error)
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Encode(context.Context, string) ([]int, error)
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Decode(context.Context, []int) (string, error)
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SetOptions(api.Options)
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Close()
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Ping(context.Context) error
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}
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func New(workDir, model string, adapters []string, opts api.Options) (LLM, error) {
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if _, err := os.Stat(model); err != nil {
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return nil, err
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}
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f, err := os.Open(model)
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if err != nil {
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return nil, err
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}
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defer f.Close()
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ggml, err := DecodeGGML(f)
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if err != nil {
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return nil, err
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}
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switch ggml.FileType() {
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case "Q8_0":
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if ggml.Name() != "gguf" && opts.NumGPU != 0 {
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// GGML Q8_0 do not support Metal API and will
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// cause the runner to segmentation fault so disable GPU
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log.Printf("WARNING: GPU disabled for F32, Q5_0, Q5_1, and Q8_0")
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opts.NumGPU = 0
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}
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case "F32", "Q5_0", "Q5_1":
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if opts.NumGPU != 0 {
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// F32, Q5_0, Q5_1, and Q8_0 do not support Metal API and will
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// cause the runner to segmentation fault so disable GPU
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log.Printf("WARNING: GPU disabled for F32, Q5_0, Q5_1, and Q8_0")
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opts.NumGPU = 0
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}
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}
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totalResidentMemory := memory.TotalMemory()
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switch ggml.ModelType() {
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case "3B", "7B":
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if ggml.FileType() == "F16" && totalResidentMemory < 16*1024*1024 {
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return nil, fmt.Errorf("F16 model requires at least 16GB of memory")
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} else if totalResidentMemory < 8*1024*1024 {
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return nil, fmt.Errorf("model requires at least 8GB of memory")
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}
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case "13B":
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if ggml.FileType() == "F16" && totalResidentMemory < 32*1024*1024 {
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return nil, fmt.Errorf("F16 model requires at least 32GB of memory")
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} else if totalResidentMemory < 16*1024*1024 {
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return nil, fmt.Errorf("model requires at least 16GB of memory")
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}
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case "30B", "34B", "40B":
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if ggml.FileType() == "F16" && totalResidentMemory < 64*1024*1024 {
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return nil, fmt.Errorf("F16 model requires at least 64GB of memory")
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} else if totalResidentMemory < 32*1024*1024 {
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return nil, fmt.Errorf("model requires at least 32GB of memory")
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}
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case "65B", "70B":
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if ggml.FileType() == "F16" && totalResidentMemory < 128*1024*1024 {
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return nil, fmt.Errorf("F16 model requires at least 128GB of memory")
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} else if totalResidentMemory < 64*1024*1024 {
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return nil, fmt.Errorf("model requires at least 64GB of memory")
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}
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case "180B":
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if ggml.FileType() == "F16" && totalResidentMemory < 512*1024*1024 {
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return nil, fmt.Errorf("F16 model requires at least 512GB of memory")
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} else if totalResidentMemory < 128*1024*1024 {
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return nil, fmt.Errorf("model requires at least 128GB of memory")
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}
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}
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switch ggml.Name() {
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case "gguf":
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opts.NumGQA = 0 // TODO: remove this when llama.cpp runners differ enough to need separate newLlama functions
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return newLlama(model, adapters, chooseRunners(workDir, "gguf"), opts)
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case "ggml", "ggmf", "ggjt", "ggla":
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return newLlama(model, adapters, chooseRunners(workDir, "ggml"), opts)
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default:
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return nil, fmt.Errorf("unknown ggml type: %s", ggml.ModelFamily())
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}
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}
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