client updates
This commit is contained in:
Jeffrey Morgan
parent
6292f4b64c
commit
fd962a36e5
21 changed files with 198 additions and 3137 deletions
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@ -1,14 +1,13 @@
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package api
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import (
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"bufio"
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"bytes"
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"context"
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"encoding/json"
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"fmt"
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"io"
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"net/http"
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"github.com/jmorganca/ollama/signature"
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)
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type Client struct {
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@ -36,7 +35,7 @@ func checkError(resp *http.Response, body []byte) error {
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return apiError
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}
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func (c *Client) do(ctx context.Context, method string, path string, stream bool, reqData any, respData any) error {
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func (c *Client) stream(ctx context.Context, method string, path string, reqData any, callback func (data []byte)) error {
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var reqBody io.Reader
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var data []byte
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var err error
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@ -55,17 +54,50 @@ func (c *Client) do(ctx context.Context, method string, path string, stream bool
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return err
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}
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if c.PrivateKey != nil {
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s := signature.SignatureData{
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Method: method,
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Path: url,
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Data: data,
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req.Header.Set("Content-Type", "application/json")
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req.Header.Set("Accept", "application/json")
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for k, v := range c.Headers {
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req.Header[k] = v
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}
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authHeader, err := signature.SignAuthData(s, c.PrivateKey)
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res, err := c.HTTP.Do(req)
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if err != nil {
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return err
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}
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req.Header.Set("Authorization", authHeader)
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defer res.Body.Close()
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reader := bufio.NewReader(res.Body)
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for {
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line, err := reader.ReadBytes('\n')
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if err != nil {
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break
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}
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callback(bytes.TrimSuffix(line, []byte("\n")))
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}
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return nil
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}
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func (c *Client) do(ctx context.Context, method string, path string, reqData any, respData any) error {
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var reqBody io.Reader
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var data []byte
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var err error
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if reqData != nil {
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data, err = json.Marshal(reqData)
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if err != nil {
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return err
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}
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reqBody = bytes.NewReader(data)
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}
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url := fmt.Sprintf("%s%s", c.URL, path)
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req, err := http.NewRequestWithContext(ctx, method, url, reqBody)
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if err != nil {
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return err
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}
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req.Header.Set("Content-Type", "application/json")
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@ -97,3 +129,14 @@ func (c *Client) do(ctx context.Context, method string, path string, stream bool
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}
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return nil
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}
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func (c *Client) Generate(ctx context.Context, req *GenerateRequest, callback func(token string)) (*GenerateResponse, error) {
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var res GenerateResponse
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if err := c.stream(ctx, http.MethodPost, "/api/generate", req, func(token []byte) {
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callback(string(token))
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}); err != nil {
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return nil, err
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}
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return &res, nil
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}
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113
cmd/cmd.go
113
cmd/cmd.go
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@ -2,7 +2,6 @@ package cmd
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import (
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"context"
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"fmt"
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"log"
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"net"
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"net/http"
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@ -10,14 +9,59 @@ import (
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"path"
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"time"
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"github.com/spf13/cobra"
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"github.com/jmorganca/ollama/api"
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"github.com/jmorganca/ollama/server"
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"github.com/spf13/cobra"
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)
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func NewAPIClient(cmd *cobra.Command) (*api.Client, error) {
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var rawKey []byte
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func sockpath() string {
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home, err := os.UserHomeDir()
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if err != nil {
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panic(err)
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}
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return path.Join(home, ".ollama", "ollama.sock")
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}
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func running() bool {
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// Set a timeout duration
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timeout := time.Second
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// Dial the unix socket
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conn, err := net.DialTimeout("unix", sockpath(), timeout)
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if err != nil {
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return false
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}
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if conn != nil {
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defer conn.Close()
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}
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return true
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}
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func serve() error {
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sp := sockpath()
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if err := os.MkdirAll(path.Dir(sp), 0o700); err != nil {
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return err
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}
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if err := os.RemoveAll(sp); err != nil {
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return err
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}
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ln, err := net.Listen("unix", sp)
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if err != nil {
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return err
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}
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if err := os.Chmod(sp, 0o700); err != nil {
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return err
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}
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return server.Serve(ln)
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}
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func NewAPIClient() (*api.Client, error) {
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var err error
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home, err := os.UserHomeDir()
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@ -31,16 +75,6 @@ func NewAPIClient(cmd *cobra.Command) (*api.Client, error) {
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Timeout: 10 * time.Second,
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}
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k, _ := cmd.Flags().GetString("key")
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if k != "" {
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fn := path.Join(home, ".ollama/keys/", k)
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rawKey, err = os.ReadFile(fn)
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if err != nil {
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return nil, err
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}
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}
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return &api.Client{
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URL: "http://localhost",
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HTTP: http.Client{
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@ -50,7 +84,6 @@ func NewAPIClient(cmd *cobra.Command) (*api.Client, error) {
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},
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},
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},
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PrivateKey: rawKey,
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}, nil
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}
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@ -69,28 +102,12 @@ func NewCLI() *cobra.Command {
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},
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}
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rootCmd.PersistentFlags().StringP("key", "k", "", "Private key to use for authenticating")
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cobra.EnableCommandSorting = false
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modelsCmd := &cobra.Command{
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Use: "models",
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Args: cobra.MaximumNArgs(1),
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Short: "List models",
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Long: "List the models",
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RunE: func(cmd *cobra.Command, args []string) error {
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client, err := NewAPIClient(cmd)
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if err != nil {
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return err
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}
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fmt.Printf("client = %q\n", client)
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return nil
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},
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}
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runCmd := &cobra.Command{
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Use: "run",
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Short: "Run a model and submit prompts.",
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Use: "run MODEL",
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Short: "Run a model",
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Args: cobra.ExactArgs(1),
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RunE: func(cmd *cobra.Command,args []string) error {
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return nil
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},
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@ -101,35 +118,11 @@ func NewCLI() *cobra.Command {
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Aliases: []string{"start"},
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Short: "Start ollama",
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RunE: func(cmd *cobra.Command, args []string) error {
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home, err := os.UserHomeDir()
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if err != nil {
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return err
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}
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socket := path.Join(home, ".ollama", "ollama.sock")
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if err := os.MkdirAll(path.Dir(socket), 0o700); err != nil {
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return err
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}
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if err := os.RemoveAll(socket); err != nil {
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return err
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}
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ln, err := net.Listen("unix", socket)
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if err != nil {
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return err
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}
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if err := os.Chmod(socket, 0o700); err != nil {
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return err
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}
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return server.Serve(ln)
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return serve()
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},
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}
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rootCmd.AddCommand(
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modelsCmd,
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serveCmd,
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runCmd,
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)
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2
go.mod
2
go.mod
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@ -5,7 +5,6 @@ go 1.20
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require (
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github.com/gin-gonic/gin v1.9.1
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github.com/spf13/cobra v1.7.0
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golang.org/x/crypto v0.10.0
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)
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require (
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@ -30,6 +29,7 @@ require (
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github.com/twitchyliquid64/golang-asm v0.15.1 // indirect
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github.com/ugorji/go/codec v1.2.11 // indirect
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golang.org/x/arch v0.3.0 // indirect
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golang.org/x/crypto v0.10.0 // indirect
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golang.org/x/net v0.10.0 // indirect
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golang.org/x/sys v0.9.0 // indirect
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golang.org/x/text v0.10.0 // indirect
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1
go.sum
1
go.sum
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@ -79,7 +79,6 @@ golang.org/x/sys v0.0.0-20220704084225-05e143d24a9e/go.mod h1:oPkhp1MJrh7nUepCBc
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golang.org/x/sys v0.6.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
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golang.org/x/sys v0.9.0 h1:KS/R3tvhPqvJvwcKfnBHJwwthS11LRhmM5D59eEXa0s=
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golang.org/x/sys v0.9.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
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golang.org/x/term v0.9.0 h1:GRRCnKYhdQrD8kfRAdQ6Zcw1P0OcELxGLKJvtjVMZ28=
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golang.org/x/text v0.10.0 h1:UpjohKhiEgNc0CSauXmwYftY1+LlaC75SJwh0SgCX58=
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golang.org/x/text v0.10.0/go.mod h1:TvPlkZtksWOMsz7fbANvkp4WM8x/WCo/om8BMLbz+aE=
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golang.org/x/xerrors v0.0.0-20191204190536-9bdfabe68543/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
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@ -55,78 +55,14 @@ void sigint_handler(int signo) {
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}
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#endif
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int get_embeddings(void *params_ptr, void *state_pr, float *res_embeddings) {
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gpt_params *params_p = (gpt_params *)params_ptr;
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llama_context *ctx = (llama_context *)state_pr;
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gpt_params params = *params_p;
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if (params.seed <= 0) {
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params.seed = time(NULL);
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}
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std::mt19937 rng(params.seed);
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llama_init_backend(params.numa);
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int n_past = 0;
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// Add a space in front of the first character to match OG llama tokenizer
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// behavior
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params.prompt.insert(0, 1, ' ');
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// tokenize the prompt
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auto embd_inp = ::llama_tokenize(ctx, params.prompt, true);
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// determine newline token
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auto llama_token_newline = ::llama_tokenize(ctx, "\n", false);
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if (embd_inp.size() > 0) {
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if (llama_eval(ctx, embd_inp.data(), embd_inp.size(), n_past,
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params.n_threads)) {
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fprintf(stderr, "%s : failed to eval\n", __func__);
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return 1;
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}
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}
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const int n_embd = llama_n_embd(ctx);
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const auto embeddings = llama_get_embeddings(ctx);
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for (int i = 0; i < n_embd; i++) {
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res_embeddings[i] = embeddings[i];
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}
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return 0;
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}
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int get_token_embeddings(void *params_ptr, void *state_pr, int *tokens,
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int tokenSize, float *res_embeddings) {
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gpt_params *params_p = (gpt_params *)params_ptr;
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llama_context *ctx = (llama_context *)state_pr;
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gpt_params params = *params_p;
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for (int i = 0; i < tokenSize; i++) {
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auto token_str = llama_token_to_str(ctx, tokens[i]);
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if (token_str == nullptr) {
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continue;
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}
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std::vector<std::string> my_vector;
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std::string str_token(token_str); // create a new std::string from the char*
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params_p->prompt += str_token;
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}
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return get_embeddings(params_ptr, state_pr, res_embeddings);
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}
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int eval(void *params_ptr, void *state_pr, char *text) {
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gpt_params *params_p = (gpt_params *)params_ptr;
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llama_context *ctx = (llama_context *)state_pr;
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int eval(void *p, void *c, char *text) {
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gpt_params *params = (gpt_params *)params;
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llama_context *ctx = (llama_context *)ctx;
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auto n_past = 0;
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auto last_n_tokens_data =
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std::vector<llama_token>(params_p->repeat_last_n, 0);
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auto last_n_tokens_data = std::vector<llama_token>(params->repeat_last_n, 0);
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auto tokens = std::vector<llama_token>(params_p->n_ctx);
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auto tokens = std::vector<llama_token>(params->n_ctx);
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auto n_prompt_tokens =
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llama_tokenize(ctx, text, tokens.data(), tokens.size(), true);
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@ -135,26 +71,22 @@ int eval(void *params_ptr, void *state_pr, char *text) {
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return 1;
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}
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// evaluate prompt
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return llama_eval(ctx, tokens.data(), n_prompt_tokens, n_past,
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params_p->n_threads);
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params->n_threads);
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}
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int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug) {
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gpt_params *params_p = (gpt_params *)params_ptr;
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llama_context *ctx = (llama_context *)state_pr;
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gpt_params params = *params_p;
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int llama_predict(void *p, void *c, char *result, bool debug) {
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gpt_params *params = (gpt_params *)params;
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llama_context *ctx = (llama_context *)ctx;
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const int n_ctx = llama_n_ctx(ctx);
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if (params.seed <= 0) {
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params.seed = time(NULL);
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if (params->seed <= 0) {
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params->seed = time(NULL);
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}
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std::mt19937 rng(params.seed);
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std::string path_session = params.path_prompt_cache;
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std::mt19937 rng(params->seed);
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std::string path_session = params->path_prompt_cache;
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std::vector<llama_token> session_tokens;
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if (!path_session.empty()) {
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@ -177,7 +109,7 @@ int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug) {
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return 1;
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}
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session_tokens.resize(n_token_count_out);
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llama_set_rng_seed(ctx, params.seed);
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llama_set_rng_seed(ctx, params->seed);
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if (debug) {
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fprintf(stderr, "%s: loaded a session with prompt size of %d tokens\n",
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__func__, (int)session_tokens.size());
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@ -191,12 +123,12 @@ int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug) {
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}
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std::vector<llama_token> embd_inp;
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if (!params.prompt.empty() || session_tokens.empty()) {
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if (!params->prompt.empty() || session_tokens.empty()) {
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// Add a space in front of the first character to match OG llama tokenizer
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// behavior
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params.prompt.insert(0, 1, ' ');
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params->prompt.insert(0, 1, ' ');
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embd_inp = ::llama_tokenize(ctx, params.prompt, true);
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embd_inp = ::llama_tokenize(ctx, params->prompt, true);
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} else {
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embd_inp = session_tokens;
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}
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@ -212,7 +144,7 @@ int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug) {
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n_matching_session_tokens++;
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}
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if (debug) {
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if (params.prompt.empty() &&
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if (params->prompt.empty() &&
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n_matching_session_tokens == embd_inp.size()) {
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fprintf(stderr, "%s: using full prompt from session file\n", __func__);
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} else if (n_matching_session_tokens >= embd_inp.size()) {
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@ -237,8 +169,8 @@ int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug) {
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session_tokens.resize(embd_inp.size() - 1);
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}
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// number of tokens to keep when resetting context
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if (params.n_keep < 0 || params.n_keep > (int)embd_inp.size()) {
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params.n_keep = (int)embd_inp.size();
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if (params->n_keep < 0 || params->n_keep > (int)embd_inp.size()) {
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params->n_keep = (int)embd_inp.size();
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}
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// determine newline token
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@ -251,7 +183,7 @@ int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug) {
|
|||
bool need_to_save_session =
|
||||
!path_session.empty() && n_matching_session_tokens < embd_inp.size();
|
||||
int n_past = 0;
|
||||
int n_remain = params.n_predict;
|
||||
int n_remain = params->n_predict;
|
||||
int n_consumed = 0;
|
||||
int n_session_consumed = 0;
|
||||
|
||||
|
|
@ -263,7 +195,7 @@ int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug) {
|
|||
const std::vector<llama_token> tmp = {
|
||||
llama_token_bos(),
|
||||
};
|
||||
llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
|
||||
llama_eval(ctx, tmp.data(), tmp.size(), 0, params->n_threads);
|
||||
llama_reset_timings(ctx);
|
||||
}
|
||||
|
||||
|
|
@ -276,10 +208,10 @@ int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug) {
|
|||
// - take half of the last (n_ctx - n_keep) tokens and recompute the
|
||||
// logits in batches
|
||||
if (n_past + (int)embd.size() > n_ctx) {
|
||||
const int n_left = n_past - params.n_keep;
|
||||
const int n_left = n_past - params->n_keep;
|
||||
|
||||
// always keep the first token - BOS
|
||||
n_past = std::max(1, params.n_keep);
|
||||
n_past = std::max(1, params->n_keep);
|
||||
|
||||
// insert n_left/2 tokens at the start of embd from last_n_tokens
|
||||
embd.insert(embd.begin(),
|
||||
|
|
@ -288,14 +220,6 @@ int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug) {
|
|||
|
||||
// stop saving session if we run out of context
|
||||
path_session.clear();
|
||||
|
||||
// printf("\n---\n");
|
||||
// printf("resetting: '");
|
||||
// for (int i = 0; i < (int) embd.size(); i++) {
|
||||
// printf("%s", llama_token_to_str(ctx, embd[i]));
|
||||
// }
|
||||
// printf("'\n");
|
||||
// printf("\n---\n");
|
||||
}
|
||||
|
||||
// try to reuse a matching prefix from the loaded session instead of
|
||||
|
|
@ -324,15 +248,17 @@ int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug) {
|
|||
// evaluate tokens in batches
|
||||
// embd is typically prepared beforehand to fit within a batch, but not
|
||||
// always
|
||||
for (int i = 0; i < (int)embd.size(); i += params.n_batch) {
|
||||
for (int i = 0; i < (int)embd.size(); i += params->n_batch) {
|
||||
int n_eval = (int)embd.size() - i;
|
||||
if (n_eval > params.n_batch) {
|
||||
n_eval = params.n_batch;
|
||||
if (n_eval > params->n_batch) {
|
||||
n_eval = params->n_batch;
|
||||
}
|
||||
if (llama_eval(ctx, &embd[i], n_eval, n_past, params.n_threads)) {
|
||||
|
||||
if (llama_eval(ctx, &embd[i], n_eval, n_past, params->n_threads)) {
|
||||
fprintf(stderr, "%s : failed to eval\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
||||
n_past += n_eval;
|
||||
}
|
||||
|
||||
|
|
@ -346,26 +272,26 @@ int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug) {
|
|||
|
||||
if ((int)embd_inp.size() <= n_consumed) {
|
||||
// out of user input, sample next token
|
||||
const float temp = params.temp;
|
||||
const float temp = params->temp;
|
||||
const int32_t top_k =
|
||||
params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
|
||||
const float top_p = params.top_p;
|
||||
const float tfs_z = params.tfs_z;
|
||||
const float typical_p = params.typical_p;
|
||||
params->top_k <= 0 ? llama_n_vocab(ctx) : params->top_k;
|
||||
const float top_p = params->top_p;
|
||||
const float tfs_z = params->tfs_z;
|
||||
const float typical_p = params->typical_p;
|
||||
const int32_t repeat_last_n =
|
||||
params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
|
||||
const float repeat_penalty = params.repeat_penalty;
|
||||
const float alpha_presence = params.presence_penalty;
|
||||
const float alpha_frequency = params.frequency_penalty;
|
||||
const int mirostat = params.mirostat;
|
||||
const float mirostat_tau = params.mirostat_tau;
|
||||
const float mirostat_eta = params.mirostat_eta;
|
||||
const bool penalize_nl = params.penalize_nl;
|
||||
params->repeat_last_n < 0 ? n_ctx : params->repeat_last_n;
|
||||
const float repeat_penalty = params->repeat_penalty;
|
||||
const float alpha_presence = params->presence_penalty;
|
||||
const float alpha_frequency = params->frequency_penalty;
|
||||
const int mirostat = params->mirostat;
|
||||
const float mirostat_tau = params->mirostat_tau;
|
||||
const float mirostat_eta = params->mirostat_eta;
|
||||
const bool penalize_nl = params->penalize_nl;
|
||||
|
||||
// optionally save the session on first sample (for faster prompt loading
|
||||
// next time)
|
||||
if (!path_session.empty() && need_to_save_session &&
|
||||
!params.prompt_cache_ro) {
|
||||
!params->prompt_cache_ro) {
|
||||
need_to_save_session = false;
|
||||
llama_save_session_file(ctx, path_session.c_str(),
|
||||
session_tokens.data(), session_tokens.size());
|
||||
|
|
@ -378,8 +304,8 @@ int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug) {
|
|||
auto n_vocab = llama_n_vocab(ctx);
|
||||
|
||||
// Apply params.logit_bias map
|
||||
for (auto it = params.logit_bias.begin(); it != params.logit_bias.end();
|
||||
it++) {
|
||||
for (auto it = params->logit_bias.begin();
|
||||
it != params->logit_bias.end(); it++) {
|
||||
logits[it->first] += it->second;
|
||||
}
|
||||
|
||||
|
|
@ -435,7 +361,6 @@ int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug) {
|
|||
id = llama_sample_token(ctx, &candidates_p);
|
||||
}
|
||||
}
|
||||
// printf("`%d`", candidates_p.size);
|
||||
|
||||
last_n_tokens.erase(last_n_tokens.begin());
|
||||
last_n_tokens.push_back(id);
|
||||
|
|
@ -450,7 +375,7 @@ int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug) {
|
|||
// call the token callback, no need to check if one is actually
|
||||
// registered, that will be handled on the Go side.
|
||||
auto token_str = llama_token_to_str(ctx, id);
|
||||
if (!tokenCallback(state_pr, (char *)token_str)) {
|
||||
if (!tokenCallback(ctx, (char *)token_str)) {
|
||||
break;
|
||||
}
|
||||
} else {
|
||||
|
|
@ -461,7 +386,7 @@ int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug) {
|
|||
last_n_tokens.erase(last_n_tokens.begin());
|
||||
last_n_tokens.push_back(embd_inp[n_consumed]);
|
||||
++n_consumed;
|
||||
if ((int)embd.size() >= params.n_batch) {
|
||||
if ((int)embd.size() >= params->n_batch) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
|
@ -472,13 +397,13 @@ int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug) {
|
|||
}
|
||||
|
||||
// check for stop prompt
|
||||
if (params.antiprompt.size()) {
|
||||
if (params->antiprompt.size()) {
|
||||
std::string last_output;
|
||||
for (auto id : last_n_tokens) {
|
||||
last_output += llama_token_to_str(ctx, id);
|
||||
}
|
||||
// Check if each of the reverse prompts appears at the end of the output.
|
||||
for (std::string &antiprompt : params.antiprompt) {
|
||||
for (std::string &antiprompt : params->antiprompt) {
|
||||
// size_t extra_padding = params.interactive ? 0 : 2;
|
||||
size_t extra_padding = 2;
|
||||
size_t search_start_pos =
|
||||
|
|
@ -501,8 +426,8 @@ int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug) {
|
|||
}
|
||||
}
|
||||
|
||||
if (!path_session.empty() && params.prompt_cache_all &&
|
||||
!params.prompt_cache_ro) {
|
||||
if (!path_session.empty() && params->prompt_cache_all &&
|
||||
!params->prompt_cache_ro) {
|
||||
if (debug) {
|
||||
fprintf(stderr, "\n%s: saving final output to session file '%s'\n",
|
||||
__func__, path_session.c_str());
|
||||
|
|
@ -525,68 +450,8 @@ end:
|
|||
return 0;
|
||||
}
|
||||
|
||||
void llama_binding_free_model(void *state_ptr) {
|
||||
llama_context *ctx = (llama_context *)state_ptr;
|
||||
llama_free(ctx);
|
||||
}
|
||||
|
||||
void llama_free_params(void *params_ptr) {
|
||||
gpt_params *params = (gpt_params *)params_ptr;
|
||||
delete params;
|
||||
}
|
||||
|
||||
std::vector<std::string> create_vector(const char **strings, int count) {
|
||||
std::vector<std::string> *vec = new std::vector<std::string>;
|
||||
for (int i = 0; i < count; i++) {
|
||||
vec->push_back(std::string(strings[i]));
|
||||
}
|
||||
return *vec;
|
||||
}
|
||||
|
||||
void delete_vector(std::vector<std::string> *vec) { delete vec; }
|
||||
|
||||
int load_state(void *ctx, char *statefile, char *modes) {
|
||||
llama_context *state = (llama_context *)ctx;
|
||||
const llama_context *constState = static_cast<const llama_context *>(state);
|
||||
const size_t state_size = llama_get_state_size(state);
|
||||
uint8_t *state_mem = new uint8_t[state_size];
|
||||
|
||||
{
|
||||
FILE *fp_read = fopen(statefile, modes);
|
||||
if (state_size != llama_get_state_size(constState)) {
|
||||
fprintf(stderr, "\n%s : failed to validate state size\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
||||
const size_t ret = fread(state_mem, 1, state_size, fp_read);
|
||||
if (ret != state_size) {
|
||||
fprintf(stderr, "\n%s : failed to read state\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
||||
llama_set_state_data(
|
||||
state, state_mem); // could also read directly from memory mapped file
|
||||
fclose(fp_read);
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
void save_state(void *ctx, char *dst, char *modes) {
|
||||
llama_context *state = (llama_context *)ctx;
|
||||
|
||||
const size_t state_size = llama_get_state_size(state);
|
||||
uint8_t *state_mem = new uint8_t[state_size];
|
||||
|
||||
// Save state (rng, logits, embedding and kv_cache) to file
|
||||
{
|
||||
FILE *fp_write = fopen(dst, modes);
|
||||
llama_copy_state_data(
|
||||
state, state_mem); // could also copy directly to memory mapped file
|
||||
fwrite(state_mem, 1, state_size, fp_write);
|
||||
fclose(fp_write);
|
||||
}
|
||||
}
|
||||
void llama_binding_free_model(void *ctx) { llama_free((llama_context *)ctx); }
|
||||
void llama_free_params(void *params) { delete (gpt_params *)params; }
|
||||
|
||||
void *llama_allocate_params(
|
||||
const char *prompt, int seed, int threads, int tokens, int top_k,
|
||||
|
|
@ -640,9 +505,13 @@ void *llama_allocate_params(
|
|||
if (ignore_eos) {
|
||||
params->logit_bias[llama_token_eos()] = -INFINITY;
|
||||
}
|
||||
|
||||
if (antiprompt_count > 0) {
|
||||
params->antiprompt = create_vector(antiprompt, antiprompt_count);
|
||||
for (int i = 0; i < antiprompt_count; i++) {
|
||||
params->antiprompt.push_back(std::string(antiprompt[i]));
|
||||
}
|
||||
}
|
||||
|
||||
params->tfs_z = tfs_z;
|
||||
params->typical_p = typical_p;
|
||||
params->presence_penalty = presence_penalty;
|
||||
|
|
@ -650,6 +519,7 @@ void *llama_allocate_params(
|
|||
params->mirostat_eta = mirostat_eta;
|
||||
params->mirostat_tau = mirostat_tau;
|
||||
params->penalize_nl = penalize_nl;
|
||||
|
||||
std::stringstream ss(logit_bias);
|
||||
llama_token key;
|
||||
char sign;
|
||||
|
|
@ -669,7 +539,6 @@ void *load_model(const char *fname, int n_ctx, int n_seed, bool memory_f16,
|
|||
bool mlock, bool embeddings, bool mmap, bool low_vram,
|
||||
bool vocab_only, int n_gpu_layers, int n_batch,
|
||||
const char *maingpu, const char *tensorsplit, bool numa) {
|
||||
// load the model
|
||||
auto lparams = llama_context_default_params();
|
||||
|
||||
lparams.n_ctx = n_ctx;
|
||||
|
|
@ -706,25 +575,11 @@ void *load_model(const char *fname, int n_ctx, int n_seed, bool memory_f16,
|
|||
lparams.n_batch = n_batch;
|
||||
|
||||
llama_init_backend(numa);
|
||||
void *res = nullptr;
|
||||
try {
|
||||
llama_model *model = llama_load_model_from_file(fname, lparams);
|
||||
if (model == NULL) {
|
||||
fprintf(stderr, "error: failed to load model \n");
|
||||
return res;
|
||||
|
||||
struct llama_model *model = llama_load_model_from_file(fname, lparams);
|
||||
if (!model) {
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
llama_context *lctx = llama_new_context_with_model(model, lparams);
|
||||
if (lctx == NULL) {
|
||||
fprintf(stderr, "error: failed to create context with model \n");
|
||||
llama_free_model(model);
|
||||
return res;
|
||||
}
|
||||
|
||||
} catch (std::runtime_error &e) {
|
||||
fprintf(stderr, "failed %s", e.what());
|
||||
return res;
|
||||
}
|
||||
|
||||
return res;
|
||||
return llama_new_context_with_model(model, lparams);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -30,22 +30,13 @@ extern "C" {
|
|||
|
||||
extern unsigned char tokenCallback(void *, char *);
|
||||
|
||||
int load_state(void *ctx, char *statefile, char *modes);
|
||||
|
||||
int eval(void *params_ptr, void *ctx, char *text);
|
||||
|
||||
void save_state(void *ctx, char *dst, char *modes);
|
||||
int eval(void *p, void *c, char *text);
|
||||
|
||||
void *load_model(const char *fname, int n_ctx, int n_seed, bool memory_f16,
|
||||
bool mlock, bool embeddings, bool mmap, bool low_vram,
|
||||
bool vocab_only, int n_gpu, int n_batch, const char *maingpu,
|
||||
const char *tensorsplit, bool numa);
|
||||
|
||||
int get_embeddings(void *params_ptr, void *state_pr, float *res_embeddings);
|
||||
|
||||
int get_token_embeddings(void *params_ptr, void *state_pr, int *tokens,
|
||||
int tokenSize, float *res_embeddings);
|
||||
|
||||
void *llama_allocate_params(
|
||||
const char *prompt, int seed, int threads, int tokens, int top_k,
|
||||
float top_p, float temp, float repeat_penalty, int repeat_last_n,
|
||||
|
|
@ -59,13 +50,11 @@ void *llama_allocate_params(
|
|||
|
||||
void llama_free_params(void *params_ptr);
|
||||
|
||||
void llama_binding_free_model(void *state);
|
||||
void llama_binding_free_model(void *ctx);
|
||||
|
||||
int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
|
||||
std::vector<std::string> create_vector(const char **strings, int count);
|
||||
void delete_vector(std::vector<std::string> *vec);
|
||||
#endif
|
||||
|
|
|
|||
126
llama/llama.go
126
llama/llama.go
|
|
@ -31,135 +31,35 @@ package llama
|
|||
import "C"
|
||||
import (
|
||||
"fmt"
|
||||
"os"
|
||||
"strings"
|
||||
"sync"
|
||||
"unsafe"
|
||||
)
|
||||
|
||||
type LLama struct {
|
||||
state unsafe.Pointer
|
||||
ctx unsafe.Pointer
|
||||
embeddings bool
|
||||
contextSize int
|
||||
}
|
||||
|
||||
func New(model string, opts ...ModelOption) (*LLama, error) {
|
||||
mo := NewModelOptions(opts...)
|
||||
|
||||
// TODO: free this pointer
|
||||
modelPath := C.CString(model)
|
||||
result := C.load_model(modelPath, C.int(mo.ContextSize), C.int(mo.Seed), C.bool(mo.F16Memory), C.bool(mo.MLock), C.bool(mo.Embeddings), C.bool(mo.MMap), C.bool(mo.LowVRAM), C.bool(mo.VocabOnly), C.int(mo.NGPULayers), C.int(mo.NBatch), C.CString(mo.MainGPU), C.CString(mo.TensorSplit), C.bool(mo.NUMA))
|
||||
if result == nil {
|
||||
|
||||
ctx := C.load_model(modelPath, C.int(mo.ContextSize), C.int(mo.Seed), C.bool(mo.F16Memory), C.bool(mo.MLock), C.bool(mo.Embeddings), C.bool(mo.MMap), C.bool(mo.LowVRAM), C.bool(mo.VocabOnly), C.int(mo.NGPULayers), C.int(mo.NBatch), C.CString(mo.MainGPU), C.CString(mo.TensorSplit), C.bool(mo.NUMA))
|
||||
if ctx == nil {
|
||||
return nil, fmt.Errorf("failed loading model")
|
||||
}
|
||||
|
||||
ll := &LLama{state: result, contextSize: mo.ContextSize, embeddings: mo.Embeddings}
|
||||
ll := &LLama{ctx: ctx, contextSize: mo.ContextSize, embeddings: mo.Embeddings}
|
||||
|
||||
return ll, nil
|
||||
}
|
||||
|
||||
func (l *LLama) Free() {
|
||||
C.llama_binding_free_model(l.state)
|
||||
}
|
||||
|
||||
func (l *LLama) LoadState(state string) error {
|
||||
d := C.CString(state)
|
||||
w := C.CString("rb")
|
||||
|
||||
result := C.load_state(l.state, d, w)
|
||||
if result != 0 {
|
||||
return fmt.Errorf("error while loading state")
|
||||
}
|
||||
|
||||
return nil
|
||||
}
|
||||
|
||||
func (l *LLama) SaveState(dst string) error {
|
||||
d := C.CString(dst)
|
||||
w := C.CString("wb")
|
||||
|
||||
C.save_state(l.state, d, w)
|
||||
|
||||
_, err := os.Stat(dst)
|
||||
return err
|
||||
}
|
||||
|
||||
// Token Embeddings
|
||||
func (l *LLama) TokenEmbeddings(tokens []int, opts ...PredictOption) ([]float32, error) {
|
||||
if !l.embeddings {
|
||||
return []float32{}, fmt.Errorf("model loaded without embeddings")
|
||||
}
|
||||
|
||||
po := NewPredictOptions(opts...)
|
||||
|
||||
outSize := po.Tokens
|
||||
if po.Tokens == 0 {
|
||||
outSize = 9999999
|
||||
}
|
||||
|
||||
floats := make([]float32, outSize)
|
||||
|
||||
myArray := (*C.int)(C.malloc(C.size_t(len(tokens)) * C.sizeof_int))
|
||||
|
||||
// Copy the values from the Go slice to the C array
|
||||
for i, v := range tokens {
|
||||
(*[1<<31 - 1]int32)(unsafe.Pointer(myArray))[i] = int32(v)
|
||||
}
|
||||
|
||||
params := C.llama_allocate_params(C.CString(""), C.int(po.Seed), C.int(po.Threads), C.int(po.Tokens), C.int(po.TopK),
|
||||
C.float(po.TopP), C.float(po.Temperature), C.float(po.Penalty), C.int(po.Repeat),
|
||||
C.bool(po.IgnoreEOS), C.bool(po.F16KV),
|
||||
C.int(po.Batch), C.int(po.NKeep), nil, C.int(0),
|
||||
C.float(po.TailFreeSamplingZ), C.float(po.TypicalP), C.float(po.FrequencyPenalty), C.float(po.PresencePenalty),
|
||||
C.int(po.Mirostat), C.float(po.MirostatETA), C.float(po.MirostatTAU), C.bool(po.PenalizeNL), C.CString(po.LogitBias),
|
||||
C.CString(po.PathPromptCache), C.bool(po.PromptCacheAll), C.bool(po.MLock), C.bool(po.MMap),
|
||||
C.CString(po.MainGPU), C.CString(po.TensorSplit),
|
||||
C.bool(po.PromptCacheRO),
|
||||
)
|
||||
ret := C.get_token_embeddings(params, l.state, myArray, C.int(len(tokens)), (*C.float)(&floats[0]))
|
||||
if ret != 0 {
|
||||
return floats, fmt.Errorf("embedding inference failed")
|
||||
}
|
||||
return floats, nil
|
||||
}
|
||||
|
||||
// Embeddings
|
||||
func (l *LLama) Embeddings(text string, opts ...PredictOption) ([]float32, error) {
|
||||
if !l.embeddings {
|
||||
return []float32{}, fmt.Errorf("model loaded without embeddings")
|
||||
}
|
||||
|
||||
po := NewPredictOptions(opts...)
|
||||
|
||||
input := C.CString(text)
|
||||
if po.Tokens == 0 {
|
||||
po.Tokens = 99999999
|
||||
}
|
||||
floats := make([]float32, po.Tokens)
|
||||
reverseCount := len(po.StopPrompts)
|
||||
reversePrompt := make([]*C.char, reverseCount)
|
||||
var pass **C.char
|
||||
for i, s := range po.StopPrompts {
|
||||
cs := C.CString(s)
|
||||
reversePrompt[i] = cs
|
||||
pass = &reversePrompt[0]
|
||||
}
|
||||
|
||||
params := C.llama_allocate_params(input, C.int(po.Seed), C.int(po.Threads), C.int(po.Tokens), C.int(po.TopK),
|
||||
C.float(po.TopP), C.float(po.Temperature), C.float(po.Penalty), C.int(po.Repeat),
|
||||
C.bool(po.IgnoreEOS), C.bool(po.F16KV),
|
||||
C.int(po.Batch), C.int(po.NKeep), pass, C.int(reverseCount),
|
||||
C.float(po.TailFreeSamplingZ), C.float(po.TypicalP), C.float(po.FrequencyPenalty), C.float(po.PresencePenalty),
|
||||
C.int(po.Mirostat), C.float(po.MirostatETA), C.float(po.MirostatTAU), C.bool(po.PenalizeNL), C.CString(po.LogitBias),
|
||||
C.CString(po.PathPromptCache), C.bool(po.PromptCacheAll), C.bool(po.MLock), C.bool(po.MMap),
|
||||
C.CString(po.MainGPU), C.CString(po.TensorSplit),
|
||||
C.bool(po.PromptCacheRO),
|
||||
)
|
||||
|
||||
ret := C.get_embeddings(params, l.state, (*C.float)(&floats[0]))
|
||||
if ret != 0 {
|
||||
return floats, fmt.Errorf("embedding inference failed")
|
||||
}
|
||||
|
||||
return floats, nil
|
||||
C.llama_binding_free_model(l.ctx)
|
||||
}
|
||||
|
||||
func (l *LLama) Eval(text string, opts ...PredictOption) error {
|
||||
|
|
@ -189,7 +89,7 @@ func (l *LLama) Eval(text string, opts ...PredictOption) error {
|
|||
C.CString(po.MainGPU), C.CString(po.TensorSplit),
|
||||
C.bool(po.PromptCacheRO),
|
||||
)
|
||||
ret := C.eval(params, l.state, input)
|
||||
ret := C.eval(params, l.ctx, input)
|
||||
if ret != 0 {
|
||||
return fmt.Errorf("inference failed")
|
||||
}
|
||||
|
|
@ -203,7 +103,7 @@ func (l *LLama) Predict(text string, opts ...PredictOption) (string, error) {
|
|||
po := NewPredictOptions(opts...)
|
||||
|
||||
if po.TokenCallback != nil {
|
||||
setCallback(l.state, po.TokenCallback)
|
||||
setCallback(l.ctx, po.TokenCallback)
|
||||
}
|
||||
|
||||
input := C.CString(text)
|
||||
|
|
@ -231,7 +131,7 @@ func (l *LLama) Predict(text string, opts ...PredictOption) (string, error) {
|
|||
C.CString(po.MainGPU), C.CString(po.TensorSplit),
|
||||
C.bool(po.PromptCacheRO),
|
||||
)
|
||||
ret := C.llama_predict(params, l.state, (*C.char)(unsafe.Pointer(&out[0])), C.bool(po.DebugMode))
|
||||
ret := C.llama_predict(params, l.ctx, (*C.char)(unsafe.Pointer(&out[0])), C.bool(po.DebugMode))
|
||||
if ret != 0 {
|
||||
return "", fmt.Errorf("inference failed")
|
||||
}
|
||||
|
|
@ -248,7 +148,7 @@ func (l *LLama) Predict(text string, opts ...PredictOption) (string, error) {
|
|||
C.llama_free_params(params)
|
||||
|
||||
if po.TokenCallback != nil {
|
||||
setCallback(l.state, nil)
|
||||
setCallback(l.ctx, nil)
|
||||
}
|
||||
|
||||
return res, nil
|
||||
|
|
@ -268,7 +168,7 @@ func (l *LLama) Predict(text string, opts ...PredictOption) (string, error) {
|
|||
//
|
||||
// It is save to call this method while a prediction is running.
|
||||
func (l *LLama) SetTokenCallback(callback func(token string) bool) {
|
||||
setCallback(l.state, callback)
|
||||
setCallback(l.ctx, callback)
|
||||
}
|
||||
|
||||
var (
|
||||
|
|
|
|||
|
|
@ -1,39 +0,0 @@
|
|||
# Ollama Python bindings
|
||||
|
||||
```
|
||||
pip install ollama
|
||||
```
|
||||
|
||||
## Developing
|
||||
|
||||
Ollama is built using Python 3 and uses [Poetry](https://python-poetry.org/) to manage dependencies and build packages.
|
||||
|
||||
```
|
||||
pip install poetry
|
||||
```
|
||||
|
||||
Install ollama and its dependencies:
|
||||
|
||||
```
|
||||
poetry install --extras server --with dev
|
||||
```
|
||||
|
||||
Run ollama server:
|
||||
|
||||
```
|
||||
poetry run ollama server
|
||||
```
|
||||
|
||||
Update dependencies:
|
||||
|
||||
```
|
||||
poetry update --extras server --with dev
|
||||
poetry lock
|
||||
poetry export >requirements.txt
|
||||
```
|
||||
|
||||
Build binary package:
|
||||
|
||||
```
|
||||
poetry build
|
||||
```
|
||||
|
|
@ -1,9 +0,0 @@
|
|||
from ollama.model import models
|
||||
from ollama.engine import generate, load, unload
|
||||
|
||||
__all__ = [
|
||||
'models',
|
||||
'generate',
|
||||
'load',
|
||||
'unload',
|
||||
]
|
||||
|
|
@ -1,4 +0,0 @@
|
|||
from ollama.cmd import cli
|
||||
|
||||
if __name__ == '__main__':
|
||||
cli.main()
|
||||
|
|
@ -1,192 +0,0 @@
|
|||
import os
|
||||
import sys
|
||||
from argparse import ArgumentParser, HelpFormatter, PARSER
|
||||
from yaspin import yaspin
|
||||
|
||||
from ollama import model, engine
|
||||
from ollama.cmd import server
|
||||
|
||||
|
||||
class CustomHelpFormatter(HelpFormatter):
|
||||
"""
|
||||
This class is used to customize the way the argparse help text is displayed.
|
||||
We specifically override the _format_action method to exclude the line that
|
||||
shows all the subparser command options in the help text. This line is typically
|
||||
in the form "{serve,models,pull,run}".
|
||||
"""
|
||||
|
||||
def _format_action(self, action):
|
||||
# get the original help text
|
||||
parts = super()._format_action(action)
|
||||
if action.nargs == PARSER:
|
||||
# remove the unwanted first line
|
||||
parts = "\n".join(parts.split("\n")[1:])
|
||||
return parts
|
||||
|
||||
|
||||
def main():
|
||||
parser = ArgumentParser(
|
||||
description='Ollama: Run any large language model on any machine.',
|
||||
formatter_class=CustomHelpFormatter,
|
||||
)
|
||||
|
||||
# create models home if it doesn't exist
|
||||
os.makedirs(model.MODELS_CACHE_PATH, exist_ok=True)
|
||||
|
||||
subparsers = parser.add_subparsers(
|
||||
title='commands',
|
||||
)
|
||||
|
||||
list_parser = subparsers.add_parser(
|
||||
"models",
|
||||
description="List all available models stored locally.",
|
||||
help="List all available models stored locally.",
|
||||
)
|
||||
list_parser.set_defaults(fn=list_models)
|
||||
|
||||
search_parser = subparsers.add_parser(
|
||||
"search",
|
||||
description="Search for compatible models that Ollama can run.",
|
||||
help="Search for compatible models that Ollama can run. Usage: search [model]",
|
||||
)
|
||||
search_parser.add_argument(
|
||||
"query",
|
||||
nargs="?",
|
||||
help="Optional name of the model to search for.",
|
||||
)
|
||||
search_parser.set_defaults(fn=search)
|
||||
|
||||
pull_parser = subparsers.add_parser(
|
||||
"pull",
|
||||
description="Download a specified model from a remote source.",
|
||||
help="Download a specified model from a remote source. Usage: pull [model]",
|
||||
)
|
||||
pull_parser.add_argument("model", help="Name of the model to download.")
|
||||
pull_parser.set_defaults(fn=pull)
|
||||
|
||||
run_parser = subparsers.add_parser(
|
||||
"run",
|
||||
description="Run a model and submit prompts.",
|
||||
help="Run a model and submit prompts. Usage: run [model] [prompt]",
|
||||
)
|
||||
run_parser.add_argument("model", help="Name of the model to run.")
|
||||
run_parser.add_argument(
|
||||
"prompt",
|
||||
nargs="?",
|
||||
help="Optional prompt for the model, interactive mode enabled when not specified.",
|
||||
)
|
||||
run_parser.set_defaults(fn=run)
|
||||
|
||||
server.set_parser(
|
||||
subparsers.add_parser(
|
||||
"serve",
|
||||
description="Start a persistent server to interact with models via the API.",
|
||||
help="Start a persistent server to interact with models via the API.",
|
||||
)
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
args = vars(args)
|
||||
|
||||
try:
|
||||
fn = args.pop("fn")
|
||||
fn(**args)
|
||||
except KeyboardInterrupt:
|
||||
pass
|
||||
except KeyError:
|
||||
parser.print_help()
|
||||
except Exception as e:
|
||||
print(e)
|
||||
|
||||
|
||||
def list_models(*args, **kwargs):
|
||||
for m in model.models(*args, **kwargs):
|
||||
print(m)
|
||||
|
||||
|
||||
def generate(*args, **kwargs):
|
||||
if prompt := kwargs.get("prompt"):
|
||||
print(">>>", prompt, flush=True)
|
||||
generate_oneshot(*args, **kwargs)
|
||||
return
|
||||
|
||||
if sys.stdin.isatty():
|
||||
return generate_interactive(*args, **kwargs)
|
||||
|
||||
return generate_batch(*args, **kwargs)
|
||||
|
||||
|
||||
def generate_oneshot(*args, **kwargs):
|
||||
print(flush=True)
|
||||
|
||||
spinner = yaspin()
|
||||
spinner.start()
|
||||
spinner_running = True
|
||||
try:
|
||||
for output in engine.generate(model_name=kwargs.pop('model'), *args, **kwargs):
|
||||
choices = output.get("choices", [])
|
||||
if len(choices) > 0:
|
||||
if spinner_running:
|
||||
spinner.stop()
|
||||
spinner_running = False
|
||||
print("\r", end="") # move cursor back to beginning of line again
|
||||
print(choices[0].get("text", ""), end="", flush=True)
|
||||
except Exception:
|
||||
spinner.stop()
|
||||
raise
|
||||
|
||||
# end with a new line
|
||||
print(flush=True)
|
||||
print(flush=True)
|
||||
|
||||
|
||||
def generate_interactive(*args, **kwargs):
|
||||
while True:
|
||||
print(">>> ", end="", flush=True)
|
||||
line = next(sys.stdin)
|
||||
if not line:
|
||||
return
|
||||
|
||||
kwargs.update({"prompt": line})
|
||||
generate_oneshot(*args, **kwargs)
|
||||
|
||||
|
||||
def generate_batch(*args, **kwargs):
|
||||
for line in sys.stdin:
|
||||
print(">>> ", line, end="", flush=True)
|
||||
kwargs.update({"prompt": line})
|
||||
generate_oneshot(*args, **kwargs)
|
||||
|
||||
|
||||
def search(*args, **kwargs):
|
||||
try:
|
||||
model_names = model.search_directory(*args, **kwargs)
|
||||
if len(model_names) == 0:
|
||||
print("No models found.")
|
||||
return
|
||||
elif len(model_names) == 1:
|
||||
print(f"Found {len(model_names)} available model:")
|
||||
else:
|
||||
print(f"Found {len(model_names)} available models:")
|
||||
for model_name in model_names:
|
||||
print(model_name.lower())
|
||||
except Exception as e:
|
||||
print("Failed to fetch available models, check your network connection")
|
||||
|
||||
|
||||
def pull(*args, **kwargs):
|
||||
try:
|
||||
model.pull(model_name=kwargs.pop('model'), *args, **kwargs)
|
||||
print("Up to date.")
|
||||
except Exception as e:
|
||||
print(f"An error occurred: {e}")
|
||||
|
||||
|
||||
def run(*args, **kwargs):
|
||||
try:
|
||||
name = model.pull(model_name=kwargs.pop('model'), *args, **kwargs)
|
||||
kwargs.update({"model": name})
|
||||
print(f"Running {name}...")
|
||||
generate(*args, **kwargs)
|
||||
except Exception as e:
|
||||
print(f"An error occurred: {e}")
|
||||
|
|
@ -1,94 +0,0 @@
|
|||
import json
|
||||
import aiohttp_cors
|
||||
from aiohttp import web
|
||||
|
||||
from ollama import engine
|
||||
|
||||
|
||||
def set_parser(parser):
|
||||
parser.add_argument("--host", default="127.0.0.1")
|
||||
parser.add_argument("--port", default=7734)
|
||||
parser.set_defaults(fn=serve)
|
||||
|
||||
|
||||
def serve(*args, **kwargs):
|
||||
app = web.Application()
|
||||
|
||||
cors = aiohttp_cors.setup(
|
||||
app,
|
||||
defaults={
|
||||
"*": aiohttp_cors.ResourceOptions(
|
||||
allow_credentials=True,
|
||||
expose_headers="*",
|
||||
allow_headers="*",
|
||||
)
|
||||
},
|
||||
)
|
||||
|
||||
app.add_routes(
|
||||
[
|
||||
web.post("/load", load),
|
||||
web.post("/unload", unload),
|
||||
web.post("/generate", generate),
|
||||
]
|
||||
)
|
||||
|
||||
for route in app.router.routes():
|
||||
cors.add(route)
|
||||
|
||||
app.update(
|
||||
{
|
||||
"models": {},
|
||||
}
|
||||
)
|
||||
|
||||
web.run_app(app, **kwargs)
|
||||
|
||||
|
||||
async def load(request):
|
||||
body = await request.json()
|
||||
name = body.get("model")
|
||||
if not name:
|
||||
raise web.HTTPBadRequest()
|
||||
|
||||
kwargs = {
|
||||
"models": request.app.get("models"),
|
||||
}
|
||||
|
||||
engine.load(name, **kwargs)
|
||||
return web.Response()
|
||||
|
||||
|
||||
async def unload(request):
|
||||
body = await request.json()
|
||||
name = body.get("model")
|
||||
if not name:
|
||||
raise web.HTTPBadRequest()
|
||||
|
||||
engine.unload(name, models=request.app.get("models"))
|
||||
return web.Response()
|
||||
|
||||
|
||||
async def generate(request):
|
||||
body = await request.json()
|
||||
name = body.get("model")
|
||||
if not name:
|
||||
raise web.HTTPBadRequest()
|
||||
|
||||
prompt = body.get("prompt")
|
||||
if not prompt:
|
||||
raise web.HTTPBadRequest()
|
||||
|
||||
response = web.StreamResponse()
|
||||
await response.prepare(request)
|
||||
|
||||
kwargs = {
|
||||
"models": request.app.get("models"),
|
||||
}
|
||||
|
||||
for output in engine.generate(name, prompt, **kwargs):
|
||||
output = json.dumps(output).encode('utf-8')
|
||||
await response.write(output)
|
||||
await response.write(b"\n")
|
||||
|
||||
return response
|
||||
|
|
@ -1,121 +0,0 @@
|
|||
import os
|
||||
import sys
|
||||
from os import path
|
||||
from contextlib import contextmanager
|
||||
from thefuzz import process
|
||||
from llama_cpp import Llama
|
||||
from ctransformers import AutoModelForCausalLM
|
||||
|
||||
import ollama.prompt
|
||||
from ollama.model import MODELS_CACHE_PATH
|
||||
|
||||
|
||||
@contextmanager
|
||||
def suppress(file):
|
||||
original = os.dup(file.fileno())
|
||||
with open(os.devnull, "w") as devnull:
|
||||
os.dup2(devnull.fileno(), file.fileno())
|
||||
yield
|
||||
|
||||
os.dup2(original, file.fileno())
|
||||
|
||||
|
||||
def generate(model_name, prompt, models={}, *args, **kwargs):
|
||||
model = load(model_name, models=models)
|
||||
inputs = ollama.prompt.template(model_name, prompt)
|
||||
return model.generate(inputs, *args, **kwargs)
|
||||
|
||||
|
||||
def load(model_name, models={}):
|
||||
if not models.get(model_name, None):
|
||||
model_path = path.expanduser(model_name)
|
||||
if not path.exists(model_path):
|
||||
model_path = str(MODELS_CACHE_PATH / (model_name + ".bin"))
|
||||
|
||||
runners = {
|
||||
model_type: cls
|
||||
for cls in [LlamaCppRunner, CtransformerRunner]
|
||||
for model_type in cls.model_types()
|
||||
}
|
||||
|
||||
for match, _ in process.extract(model_path, runners.keys(), limit=len(runners)):
|
||||
try:
|
||||
model = runners.get(match)
|
||||
runner = model(model_path, match)
|
||||
models.update({model_name: runner})
|
||||
return runner
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
raise Exception("failed to load model", model_path, model_name)
|
||||
|
||||
|
||||
def unload(model_name, models={}):
|
||||
if model_name in models:
|
||||
models.pop(model_name)
|
||||
|
||||
|
||||
class LlamaCppRunner:
|
||||
def __init__(self, model_path, model_type):
|
||||
try:
|
||||
with suppress(sys.stderr), suppress(sys.stdout):
|
||||
self.model = Llama(model_path, verbose=False, n_gpu_layers=1, seed=-1)
|
||||
except Exception:
|
||||
raise Exception("Failed to load model", model_path, model_type)
|
||||
|
||||
@staticmethod
|
||||
def model_types():
|
||||
return [
|
||||
'llama',
|
||||
'orca',
|
||||
'vicuna',
|
||||
'ultralm',
|
||||
]
|
||||
|
||||
def generate(self, prompt, *args, **kwargs):
|
||||
if "max_tokens" not in kwargs:
|
||||
kwargs.update({"max_tokens": 512})
|
||||
|
||||
if "stop" not in kwargs:
|
||||
kwargs.update({"stop": ["Q:"]})
|
||||
|
||||
if "stream" not in kwargs:
|
||||
kwargs.update({"stream": True})
|
||||
|
||||
with suppress(sys.stderr):
|
||||
for output in self.model(prompt, *args, **kwargs):
|
||||
yield output
|
||||
|
||||
|
||||
class CtransformerRunner:
|
||||
def __init__(self, model_path, model_type):
|
||||
self.model = AutoModelForCausalLM.from_pretrained(
|
||||
model_path, model_type=model_type, local_files_only=True
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def model_types():
|
||||
return [
|
||||
'falcon',
|
||||
'mpt',
|
||||
'starcoder',
|
||||
]
|
||||
|
||||
def generate(self, prompt, *args, **kwargs):
|
||||
if "max_new_tokens" not in kwargs:
|
||||
kwargs.update({"max_new_tokens": 512})
|
||||
|
||||
if "stop" not in kwargs:
|
||||
kwargs.update({"stop": ["User"]})
|
||||
|
||||
if "stream" not in kwargs:
|
||||
kwargs.update({"stream": True})
|
||||
|
||||
for output in self.model(prompt, *args, **kwargs):
|
||||
yield {
|
||||
'choices': [
|
||||
{
|
||||
'text': output,
|
||||
},
|
||||
],
|
||||
}
|
||||
|
|
@ -1,157 +0,0 @@
|
|||
import requests
|
||||
import validators
|
||||
from pathlib import Path
|
||||
from os import path, walk
|
||||
from urllib.parse import urlsplit, urlunsplit
|
||||
from tqdm import tqdm
|
||||
|
||||
|
||||
MODELS_MANIFEST = 'https://ollama.ai/api/models'
|
||||
MODELS_CACHE_PATH = Path.home() / '.ollama' / 'models'
|
||||
|
||||
|
||||
def models(*args, **kwargs):
|
||||
for _, _, files in walk(MODELS_CACHE_PATH):
|
||||
for file in files:
|
||||
base, ext = path.splitext(file)
|
||||
if ext == '.bin':
|
||||
yield base
|
||||
|
||||
|
||||
# search the directory and return all models which contain the search term as a substring,
|
||||
# or all models if no search term is provided
|
||||
def search_directory(query):
|
||||
response = requests.get(MODELS_MANIFEST)
|
||||
response.raise_for_status()
|
||||
directory = response.json()
|
||||
model_names = []
|
||||
for model_info in directory:
|
||||
if not query or query.lower() in model_info.get('name', '').lower():
|
||||
model_names.append(model_info.get('name'))
|
||||
return model_names
|
||||
|
||||
|
||||
# get the url of the model from our curated directory
|
||||
def get_url_from_directory(model):
|
||||
response = requests.get(MODELS_MANIFEST)
|
||||
response.raise_for_status()
|
||||
directory = response.json()
|
||||
for model_info in directory:
|
||||
if model_info.get('name').lower() == model.lower():
|
||||
return model_info.get('url')
|
||||
return model
|
||||
|
||||
|
||||
def download_from_repo(url, file_name):
|
||||
parts = urlsplit(url)
|
||||
path_parts = parts.path.split('/tree/')
|
||||
|
||||
if len(path_parts) == 1:
|
||||
location = path_parts[0]
|
||||
branch = 'main'
|
||||
else:
|
||||
location, branch = path_parts
|
||||
|
||||
location = location.strip('/')
|
||||
if file_name == '':
|
||||
file_name = path.basename(location).lower()
|
||||
download_url = urlunsplit(
|
||||
(
|
||||
'https',
|
||||
parts.netloc,
|
||||
f'/api/models/{location}/tree/{branch}',
|
||||
parts.query,
|
||||
parts.fragment,
|
||||
)
|
||||
)
|
||||
response = requests.get(download_url)
|
||||
response.raise_for_status()
|
||||
json_response = response.json()
|
||||
|
||||
download_url, file_size = find_bin_file(json_response, location, branch)
|
||||
return download_file(download_url, file_name, file_size)
|
||||
|
||||
|
||||
def find_bin_file(json_response, location, branch):
|
||||
download_url = None
|
||||
file_size = 0
|
||||
for file_info in json_response:
|
||||
if file_info.get('type') == 'file' and file_info.get('path').endswith('.bin'):
|
||||
f_path = file_info.get('path')
|
||||
download_url = (
|
||||
f'https://huggingface.co/{location}/resolve/{branch}/{f_path}'
|
||||
)
|
||||
file_size = file_info.get('size')
|
||||
|
||||
if download_url is None:
|
||||
raise Exception('No model found')
|
||||
|
||||
return download_url, file_size
|
||||
|
||||
|
||||
def download_file(download_url, file_name, file_size):
|
||||
local_filename = MODELS_CACHE_PATH / str(file_name + '.bin')
|
||||
|
||||
first_byte = path.getsize(local_filename) if path.exists(local_filename) else 0
|
||||
|
||||
if first_byte >= file_size:
|
||||
return local_filename
|
||||
|
||||
print(f'Pulling {file_name}...')
|
||||
|
||||
header = {'Range': f'bytes={first_byte}-'} if first_byte != 0 else {}
|
||||
|
||||
response = requests.get(download_url, headers=header, stream=True)
|
||||
response.raise_for_status()
|
||||
|
||||
total_size = int(response.headers.get('content-length', 0)) + first_byte
|
||||
|
||||
with open(local_filename, 'ab' if first_byte else 'wb') as file, tqdm(
|
||||
total=total_size,
|
||||
unit='iB',
|
||||
unit_scale=True,
|
||||
unit_divisor=1024,
|
||||
initial=first_byte,
|
||||
ascii=' ==',
|
||||
bar_format='Downloading [{bar}] {percentage:3.2f}% {rate_fmt}{postfix}',
|
||||
) as bar:
|
||||
for data in response.iter_content(chunk_size=1024):
|
||||
size = file.write(data)
|
||||
bar.update(size)
|
||||
|
||||
return local_filename
|
||||
|
||||
|
||||
def pull(model_name, *args, **kwargs):
|
||||
# check the remote model location and see if it needs to be downloaded
|
||||
url = model_name
|
||||
file_name = ""
|
||||
if not validators.url(url) and not url.startswith('huggingface.co'):
|
||||
try:
|
||||
url = get_url_from_directory(model_name)
|
||||
except Exception as e:
|
||||
# may not have been able to check remote directory, return now
|
||||
return model_name
|
||||
if url is model_name:
|
||||
# this is not a model from our directory, so can't check remote
|
||||
maybe_existing_model_location = MODELS_CACHE_PATH / str(model_name + '.bin')
|
||||
if path.exists(model_name) or path.exists(maybe_existing_model_location):
|
||||
# a file on the filesystem is being specified
|
||||
return model_name
|
||||
raise Exception("unknown model")
|
||||
else:
|
||||
# this is a model from our directory, check remote
|
||||
file_name = model_name
|
||||
|
||||
if not (url.startswith('http://') or url.startswith('https://')):
|
||||
url = f'https://{url}'
|
||||
|
||||
if not validators.url(url):
|
||||
if model_name in models(MODELS_CACHE_PATH):
|
||||
# the model is already downloaded, and specified by name
|
||||
return model_name
|
||||
raise Exception(f'Unknown model {model_name}')
|
||||
|
||||
local_filename = download_from_repo(url, file_name)
|
||||
|
||||
return local_filename
|
||||
|
|
@ -1,12 +0,0 @@
|
|||
from os import path
|
||||
from difflib import get_close_matches
|
||||
from jinja2 import Environment, PackageLoader
|
||||
|
||||
|
||||
def template(name, prompt):
|
||||
environment = Environment(loader=PackageLoader(__name__, 'templates'))
|
||||
best_templates = get_close_matches(
|
||||
path.basename(name), environment.list_templates(), n=1, cutoff=0
|
||||
)
|
||||
template = environment.get_template(best_templates.pop())
|
||||
return template.render(prompt=prompt)
|
||||
1214
python/poetry.lock
generated
1214
python/poetry.lock
generated
File diff suppressed because it is too large
Load diff
|
|
@ -1,25 +0,0 @@
|
|||
[tool.poetry]
|
||||
name = "ollama"
|
||||
version = "0.0.9"
|
||||
description = "Run ai models locally"
|
||||
authors = ["ollama team"]
|
||||
readme = "README.md"
|
||||
packages = [{include = "ollama"}]
|
||||
scripts = {ollama = "ollama.cmd.cli:main"}
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = "^3.8"
|
||||
aiohttp = "^3.8.4"
|
||||
aiohttp-cors = "^0.7.0"
|
||||
jinja2 = "^3.1.2"
|
||||
requests = "^2.31.0"
|
||||
tqdm = "^4.65.0"
|
||||
validators = "^0.20.0"
|
||||
yaspin = "^2.3.0"
|
||||
llama-cpp-python = "^0.1.67"
|
||||
ctransformers = "^0.2.10"
|
||||
thefuzz = {version = "^0.19.0", extras = ["speedup"]}
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core"]
|
||||
build-backend = "poetry.core.masonry.api"
|
||||
|
|
@ -1,779 +0,0 @@
|
|||
aiohttp-cors==0.7.0 ; python_version >= "3.8" and python_version < "4.0" \
|
||||
--hash=sha256:0451ba59fdf6909d0e2cd21e4c0a43752bc0703d33fc78ae94d9d9321710193e \
|
||||
--hash=sha256:4d39c6d7100fd9764ed1caf8cebf0eb01bf5e3f24e2e073fda6234bc48b19f5d
|
||||
aiohttp==3.8.4 ; python_version >= "3.8" and python_version < "4.0" \
|
||||
--hash=sha256:03543dcf98a6619254b409be2d22b51f21ec66272be4ebda7b04e6412e4b2e14 \
|
||||
--hash=sha256:03baa76b730e4e15a45f81dfe29a8d910314143414e528737f8589ec60cf7391 \
|
||||
--hash=sha256:0a63f03189a6fa7c900226e3ef5ba4d3bd047e18f445e69adbd65af433add5a2 \
|
||||
--hash=sha256:10c8cefcff98fd9168cdd86c4da8b84baaa90bf2da2269c6161984e6737bf23e \
|
||||
--hash=sha256:147ae376f14b55f4f3c2b118b95be50a369b89b38a971e80a17c3fd623f280c9 \
|
||||
--hash=sha256:176a64b24c0935869d5bbc4c96e82f89f643bcdf08ec947701b9dbb3c956b7dd \
|
||||
--hash=sha256:17b79c2963db82086229012cff93ea55196ed31f6493bb1ccd2c62f1724324e4 \
|
||||
--hash=sha256:1a45865451439eb320784918617ba54b7a377e3501fb70402ab84d38c2cd891b \
|
||||
--hash=sha256:1b3ea7edd2d24538959c1c1abf97c744d879d4e541d38305f9bd7d9b10c9ec41 \
|
||||
--hash=sha256:22f6eab15b6db242499a16de87939a342f5a950ad0abaf1532038e2ce7d31567 \
|
||||
--hash=sha256:3032dcb1c35bc330134a5b8a5d4f68c1a87252dfc6e1262c65a7e30e62298275 \
|
||||
--hash=sha256:33587f26dcee66efb2fff3c177547bd0449ab7edf1b73a7f5dea1e38609a0c54 \
|
||||
--hash=sha256:34ce9f93a4a68d1272d26030655dd1b58ff727b3ed2a33d80ec433561b03d67a \
|
||||
--hash=sha256:3a80464982d41b1fbfe3154e440ba4904b71c1a53e9cd584098cd41efdb188ef \
|
||||
--hash=sha256:3b90467ebc3d9fa5b0f9b6489dfb2c304a1db7b9946fa92aa76a831b9d587e99 \
|
||||
--hash=sha256:3d89efa095ca7d442a6d0cbc755f9e08190ba40069b235c9886a8763b03785da \
|
||||
--hash=sha256:3d8ef1a630519a26d6760bc695842579cb09e373c5f227a21b67dc3eb16cfea4 \
|
||||
--hash=sha256:3f43255086fe25e36fd5ed8f2ee47477408a73ef00e804cb2b5cba4bf2ac7f5e \
|
||||
--hash=sha256:40653609b3bf50611356e6b6554e3a331f6879fa7116f3959b20e3528783e699 \
|
||||
--hash=sha256:41a86a69bb63bb2fc3dc9ad5ea9f10f1c9c8e282b471931be0268ddd09430b04 \
|
||||
--hash=sha256:493f5bc2f8307286b7799c6d899d388bbaa7dfa6c4caf4f97ef7521b9cb13719 \
|
||||
--hash=sha256:4a6cadebe132e90cefa77e45f2d2f1a4b2ce5c6b1bfc1656c1ddafcfe4ba8131 \
|
||||
--hash=sha256:4c745b109057e7e5f1848c689ee4fb3a016c8d4d92da52b312f8a509f83aa05e \
|
||||
--hash=sha256:4d347a172f866cd1d93126d9b239fcbe682acb39b48ee0873c73c933dd23bd0f \
|
||||
--hash=sha256:4dac314662f4e2aa5009977b652d9b8db7121b46c38f2073bfeed9f4049732cd \
|
||||
--hash=sha256:4ddaae3f3d32fc2cb4c53fab020b69a05c8ab1f02e0e59665c6f7a0d3a5be54f \
|
||||
--hash=sha256:5393fb786a9e23e4799fec788e7e735de18052f83682ce2dfcabaf1c00c2c08e \
|
||||
--hash=sha256:59f029a5f6e2d679296db7bee982bb3d20c088e52a2977e3175faf31d6fb75d1 \
|
||||
--hash=sha256:5a7bdf9e57126dc345b683c3632e8ba317c31d2a41acd5800c10640387d193ed \
|
||||
--hash=sha256:5b3f2e06a512e94722886c0827bee9807c86a9f698fac6b3aee841fab49bbfb4 \
|
||||
--hash=sha256:5ce45967538fb747370308d3145aa68a074bdecb4f3a300869590f725ced69c1 \
|
||||
--hash=sha256:5e14f25765a578a0a634d5f0cd1e2c3f53964553a00347998dfdf96b8137f777 \
|
||||
--hash=sha256:618c901dd3aad4ace71dfa0f5e82e88b46ef57e3239fc7027773cb6d4ed53531 \
|
||||
--hash=sha256:652b1bff4f15f6287550b4670546a2947f2a4575b6c6dff7760eafb22eacbf0b \
|
||||
--hash=sha256:6c08e8ed6fa3d477e501ec9db169bfac8140e830aa372d77e4a43084d8dd91ab \
|
||||
--hash=sha256:6ddb2a2026c3f6a68c3998a6c47ab6795e4127315d2e35a09997da21865757f8 \
|
||||
--hash=sha256:6e601588f2b502c93c30cd5a45bfc665faaf37bbe835b7cfd461753068232074 \
|
||||
--hash=sha256:6e74dd54f7239fcffe07913ff8b964e28b712f09846e20de78676ce2a3dc0bfc \
|
||||
--hash=sha256:7235604476a76ef249bd64cb8274ed24ccf6995c4a8b51a237005ee7a57e8643 \
|
||||
--hash=sha256:7ab43061a0c81198d88f39aaf90dae9a7744620978f7ef3e3708339b8ed2ef01 \
|
||||
--hash=sha256:7c7837fe8037e96b6dd5cfcf47263c1620a9d332a87ec06a6ca4564e56bd0f36 \
|
||||
--hash=sha256:80575ba9377c5171407a06d0196b2310b679dc752d02a1fcaa2bc20b235dbf24 \
|
||||
--hash=sha256:80a37fe8f7c1e6ce8f2d9c411676e4bc633a8462844e38f46156d07a7d401654 \
|
||||
--hash=sha256:8189c56eb0ddbb95bfadb8f60ea1b22fcfa659396ea36f6adcc521213cd7b44d \
|
||||
--hash=sha256:854f422ac44af92bfe172d8e73229c270dc09b96535e8a548f99c84f82dde241 \
|
||||
--hash=sha256:880e15bb6dad90549b43f796b391cfffd7af373f4646784795e20d92606b7a51 \
|
||||
--hash=sha256:8b631e26df63e52f7cce0cce6507b7a7f1bc9b0c501fcde69742130b32e8782f \
|
||||
--hash=sha256:8c29c77cc57e40f84acef9bfb904373a4e89a4e8b74e71aa8075c021ec9078c2 \
|
||||
--hash=sha256:91f6d540163f90bbaef9387e65f18f73ffd7c79f5225ac3d3f61df7b0d01ad15 \
|
||||
--hash=sha256:92c0cea74a2a81c4c76b62ea1cac163ecb20fb3ba3a75c909b9fa71b4ad493cf \
|
||||
--hash=sha256:9bcb89336efa095ea21b30f9e686763f2be4478f1b0a616969551982c4ee4c3b \
|
||||
--hash=sha256:a1f4689c9a1462f3df0a1f7e797791cd6b124ddbee2b570d34e7f38ade0e2c71 \
|
||||
--hash=sha256:a3fec6a4cb5551721cdd70473eb009d90935b4063acc5f40905d40ecfea23e05 \
|
||||
--hash=sha256:a5d794d1ae64e7753e405ba58e08fcfa73e3fad93ef9b7e31112ef3c9a0efb52 \
|
||||
--hash=sha256:a86d42d7cba1cec432d47ab13b6637bee393a10f664c425ea7b305d1301ca1a3 \
|
||||
--hash=sha256:adfbc22e87365a6e564c804c58fc44ff7727deea782d175c33602737b7feadb6 \
|
||||
--hash=sha256:aeb29c84bb53a84b1a81c6c09d24cf33bb8432cc5c39979021cc0f98c1292a1a \
|
||||
--hash=sha256:aede4df4eeb926c8fa70de46c340a1bc2c6079e1c40ccf7b0eae1313ffd33519 \
|
||||
--hash=sha256:b744c33b6f14ca26b7544e8d8aadff6b765a80ad6164fb1a430bbadd593dfb1a \
|
||||
--hash=sha256:b7a00a9ed8d6e725b55ef98b1b35c88013245f35f68b1b12c5cd4100dddac333 \
|
||||
--hash=sha256:bb96fa6b56bb536c42d6a4a87dfca570ff8e52de2d63cabebfd6fb67049c34b6 \
|
||||
--hash=sha256:bbcf1a76cf6f6dacf2c7f4d2ebd411438c275faa1dc0c68e46eb84eebd05dd7d \
|
||||
--hash=sha256:bca5f24726e2919de94f047739d0a4fc01372801a3672708260546aa2601bf57 \
|
||||
--hash=sha256:bf2e1a9162c1e441bf805a1fd166e249d574ca04e03b34f97e2928769e91ab5c \
|
||||
--hash=sha256:c4eb3b82ca349cf6fadcdc7abcc8b3a50ab74a62e9113ab7a8ebc268aad35bb9 \
|
||||
--hash=sha256:c6cc15d58053c76eacac5fa9152d7d84b8d67b3fde92709195cb984cfb3475ea \
|
||||
--hash=sha256:c6cd05ea06daca6ad6a4ca3ba7fe7dc5b5de063ff4daec6170ec0f9979f6c332 \
|
||||
--hash=sha256:c844fd628851c0bc309f3c801b3a3d58ce430b2ce5b359cd918a5a76d0b20cb5 \
|
||||
--hash=sha256:c9cb1565a7ad52e096a6988e2ee0397f72fe056dadf75d17fa6b5aebaea05622 \
|
||||
--hash=sha256:cab9401de3ea52b4b4c6971db5fb5c999bd4260898af972bf23de1c6b5dd9d71 \
|
||||
--hash=sha256:cd468460eefef601ece4428d3cf4562459157c0f6523db89365202c31b6daebb \
|
||||
--hash=sha256:d1e6a862b76f34395a985b3cd39a0d949ca80a70b6ebdea37d3ab39ceea6698a \
|
||||
--hash=sha256:d1f9282c5f2b5e241034a009779e7b2a1aa045f667ff521e7948ea9b56e0c5ff \
|
||||
--hash=sha256:d265f09a75a79a788237d7f9054f929ced2e69eb0bb79de3798c468d8a90f945 \
|
||||
--hash=sha256:db3fc6120bce9f446d13b1b834ea5b15341ca9ff3f335e4a951a6ead31105480 \
|
||||
--hash=sha256:dbf3a08a06b3f433013c143ebd72c15cac33d2914b8ea4bea7ac2c23578815d6 \
|
||||
--hash=sha256:de04b491d0e5007ee1b63a309956eaed959a49f5bb4e84b26c8f5d49de140fa9 \
|
||||
--hash=sha256:e4b09863aae0dc965c3ef36500d891a3ff495a2ea9ae9171e4519963c12ceefd \
|
||||
--hash=sha256:e595432ac259af2d4630008bf638873d69346372d38255774c0e286951e8b79f \
|
||||
--hash=sha256:e75b89ac3bd27d2d043b234aa7b734c38ba1b0e43f07787130a0ecac1e12228a \
|
||||
--hash=sha256:ea9eb976ffdd79d0e893869cfe179a8f60f152d42cb64622fca418cd9b18dc2a \
|
||||
--hash=sha256:eafb3e874816ebe2a92f5e155f17260034c8c341dad1df25672fb710627c6949 \
|
||||
--hash=sha256:ee3c36df21b5714d49fc4580247947aa64bcbe2939d1b77b4c8dcb8f6c9faecc \
|
||||
--hash=sha256:f352b62b45dff37b55ddd7b9c0c8672c4dd2eb9c0f9c11d395075a84e2c40f75 \
|
||||
--hash=sha256:fabb87dd8850ef0f7fe2b366d44b77d7e6fa2ea87861ab3844da99291e81e60f \
|
||||
--hash=sha256:fe11310ae1e4cd560035598c3f29d86cef39a83d244c7466f95c27ae04850f10 \
|
||||
--hash=sha256:fe7ba4a51f33ab275515f66b0a236bcde4fb5561498fe8f898d4e549b2e4509f
|
||||
aiosignal==1.3.1 ; python_version >= "3.8" and python_version < "4.0" \
|
||||
--hash=sha256:54cd96e15e1649b75d6c87526a6ff0b6c1b0dd3459f43d9ca11d48c339b68cfc \
|
||||
--hash=sha256:f8376fb07dd1e86a584e4fcdec80b36b7f81aac666ebc724e2c090300dd83b17
|
||||
async-timeout==4.0.2 ; python_version >= "3.8" and python_version < "4.0" \
|
||||
--hash=sha256:2163e1640ddb52b7a8c80d0a67a08587e5d245cc9c553a74a847056bc2976b15 \
|
||||
--hash=sha256:8ca1e4fcf50d07413d66d1a5e416e42cfdf5851c981d679a09851a6853383b3c
|
||||
attrs==23.1.0 ; python_version >= "3.8" and python_version < "4.0" \
|
||||
--hash=sha256:1f28b4522cdc2fb4256ac1a020c78acf9cba2c6b461ccd2c126f3aa8e8335d04 \
|
||||
--hash=sha256:6279836d581513a26f1bf235f9acd333bc9115683f14f7e8fae46c98fc50e015
|
||||
certifi==2023.5.7 ; python_version >= "3.8" and python_version < "4.0" \
|
||||
--hash=sha256:0f0d56dc5a6ad56fd4ba36484d6cc34451e1c6548c61daad8c320169f91eddc7 \
|
||||
--hash=sha256:c6c2e98f5c7869efca1f8916fed228dd91539f9f1b444c314c06eef02980c716
|
||||
charset-normalizer==3.1.0 ; python_version >= "3.8" and python_version < "4.0" \
|
||||
--hash=sha256:04afa6387e2b282cf78ff3dbce20f0cc071c12dc8f685bd40960cc68644cfea6 \
|
||||
--hash=sha256:04eefcee095f58eaabe6dc3cc2262f3bcd776d2c67005880894f447b3f2cb9c1 \
|
||||
--hash=sha256:0be65ccf618c1e7ac9b849c315cc2e8a8751d9cfdaa43027d4f6624bd587ab7e \
|
||||
--hash=sha256:0c95f12b74681e9ae127728f7e5409cbbef9cd914d5896ef238cc779b8152373 \
|
||||
--hash=sha256:0ca564606d2caafb0abe6d1b5311c2649e8071eb241b2d64e75a0d0065107e62 \
|
||||
--hash=sha256:10c93628d7497c81686e8e5e557aafa78f230cd9e77dd0c40032ef90c18f2230 \
|
||||
--hash=sha256:11d117e6c63e8f495412d37e7dc2e2fff09c34b2d09dbe2bee3c6229577818be \
|
||||
--hash=sha256:11d3bcb7be35e7b1bba2c23beedac81ee893ac9871d0ba79effc7fc01167db6c \
|
||||
--hash=sha256:12a2b561af122e3d94cdb97fe6fb2bb2b82cef0cdca131646fdb940a1eda04f0 \
|
||||
--hash=sha256:12d1a39aa6b8c6f6248bb54550efcc1c38ce0d8096a146638fd4738e42284448 \
|
||||
--hash=sha256:1435ae15108b1cb6fffbcea2af3d468683b7afed0169ad718451f8db5d1aff6f \
|
||||
--hash=sha256:1c60b9c202d00052183c9be85e5eaf18a4ada0a47d188a83c8f5c5b23252f649 \
|
||||
--hash=sha256:1e8fcdd8f672a1c4fc8d0bd3a2b576b152d2a349782d1eb0f6b8e52e9954731d \
|
||||
--hash=sha256:20064ead0717cf9a73a6d1e779b23d149b53daf971169289ed2ed43a71e8d3b0 \
|
||||
--hash=sha256:21fa558996782fc226b529fdd2ed7866c2c6ec91cee82735c98a197fae39f706 \
|
||||
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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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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
--hash=sha256:74db0052d985cf37fa111828d0dd230776ac99c740e1a758ad99094be4f1803d \
|
||||
--hash=sha256:75f2568b4189dda1c567339b48cba4ac7384accb9c2a7ed655cd86b04055c795 \
|
||||
--hash=sha256:78cacd03e79d009d95635e7d6ff12c21eb89b894c354bd2b2ed0b4763373693b \
|
||||
--hash=sha256:80d1543d58bd3d6c271b66abf454d437a438dff01c3e62fdbcd68f2a11310d4b \
|
||||
--hash=sha256:830d2948a5ec37c386d3170c483063798d7879037492540f10a475e3fd6f244b \
|
||||
--hash=sha256:891cf9b48776b5c61c700b55a598621fdb7b1e301a550365571e9624f270c203 \
|
||||
--hash=sha256:8f25e17ab3039b05f762b0a55ae0b3632b2e073d9c8fc88e89aca31a6198e88f \
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
--hash=sha256:b06f0d3bf045158d2fb8837c5785fe9ff9b8c93358be64461a1089f5da983137 \
|
||||
--hash=sha256:b116502087ce8a6b7a5f1814568ccbd0e9f6cfd99948aa59b0e241dc57cf739f \
|
||||
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|
||||
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|
||||
--hash=sha256:c36bcbc0d5174a80d6cccf43a0ecaca44e81d25be4b7f90f0ed7bcfbb5a00909 \
|
||||
--hash=sha256:c3af8e0f07399d3176b179f2e2634c3ce9c1301379a6b8c9c9aeecd481da494f \
|
||||
--hash=sha256:c84132a54c750fda57729d1e2599bb598f5fa0344085dbde5003ba429a4798c0 \
|
||||
--hash=sha256:cb7b2ab0188829593b9de646545175547a70d9a6e2b63bf2cd87a0a391599324 \
|
||||
--hash=sha256:cca4def576f47a09a943666b8f829606bcb17e2bc2d5911a46c8f8da45f56755 \
|
||||
--hash=sha256:cf6511efa4801b9b38dc5546d7547d5b5c6ef4b081c60b23e4d941d0eba9cbeb \
|
||||
--hash=sha256:d16fd5252f883eb074ca55cb622bc0bee49b979ae4e8639fff6ca3ff44f9f854 \
|
||||
--hash=sha256:d2686f91611f9e17f4548dbf050e75b079bbc2a82be565832bc8ea9047b61c8c \
|
||||
--hash=sha256:d7fc3fca01da18fbabe4625d64bb612b533533ed10045a2ac3dd194bfa656b60 \
|
||||
--hash=sha256:dd5653e67b149503c68c4018bf07e42eeed6b4e956b24c00ccdf93ac79cdff84 \
|
||||
--hash=sha256:de5695a6f1d8340b12a5d6d4484290ee74d61e467c39ff03b39e30df62cf83a0 \
|
||||
--hash=sha256:e0ac8959c929593fee38da1c2b64ee9778733cdf03c482c9ff1d508b6b593b2b \
|
||||
--hash=sha256:e1b25e3ad6c909f398df8921780d6a3d120d8c09466720226fc621605b6f92b1 \
|
||||
--hash=sha256:e633940f28c1e913615fd624fcdd72fdba807bf53ea6925d6a588e84e1151531 \
|
||||
--hash=sha256:e89df2958e5159b811af9ff0f92614dabf4ff617c03a4c1c6ff53bf1c399e0e1 \
|
||||
--hash=sha256:ea9f9c6034ea2d93d9147818f17c2a0860d41b71c38b9ce4d55f21b6f9165a11 \
|
||||
--hash=sha256:f645caaf0008bacf349875a974220f1f1da349c5dbe7c4ec93048cdc785a3326 \
|
||||
--hash=sha256:f8303414c7b03f794347ad062c0516cee0e15f7a612abd0ce1e25caf6ceb47df \
|
||||
--hash=sha256:fca62a8301b605b954ad2e9c3666f9d97f63872aa4efcae5492baca2056b74ab
|
||||
colorama==0.4.6 ; python_version >= "3.8" and python_version < "4.0" and platform_system == "Windows" \
|
||||
--hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \
|
||||
--hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6
|
||||
ctransformers==0.2.10 ; python_version >= "3.8" and python_version < "4.0" \
|
||||
--hash=sha256:912a80859bd252e2a389b4716d44b0663657148a85fbfbe6c5503a7ee69fd235 \
|
||||
--hash=sha256:d82a299690f1494fe93db136f71b1b9b2cd4ada535e2afa07aaee1d180117a2d
|
||||
decorator==5.1.1 ; python_version >= "3.8" and python_version < "4.0" \
|
||||
--hash=sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330 \
|
||||
--hash=sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186
|
||||
diskcache==5.6.1 ; python_version >= "3.8" and python_version < "4.0" \
|
||||
--hash=sha256:558c6a2d5d7c721bb00e40711803d6804850c9f76c426ed81ecc627fe9d2ce2d \
|
||||
--hash=sha256:e4c978532feff5814c4cc00fe1e11e40501985946643d73220d41ee7737c72c3
|
||||
filelock==3.12.2 ; python_version >= "3.8" and python_version < "4.0" \
|
||||
--hash=sha256:002740518d8aa59a26b0c76e10fb8c6e15eae825d34b6fdf670333fd7b938d81 \
|
||||
--hash=sha256:cbb791cdea2a72f23da6ac5b5269ab0a0d161e9ef0100e653b69049a7706d1ec
|
||||
frozenlist==1.3.3 ; python_version >= "3.8" and python_version < "4.0" \
|
||||
--hash=sha256:008a054b75d77c995ea26629ab3a0c0d7281341f2fa7e1e85fa6153ae29ae99c \
|
||||
--hash=sha256:02c9ac843e3390826a265e331105efeab489ffaf4dd86384595ee8ce6d35ae7f \
|
||||
--hash=sha256:034a5c08d36649591be1cbb10e09da9f531034acfe29275fc5454a3b101ce41a \
|
||||
--hash=sha256:05cdb16d09a0832eedf770cb7bd1fe57d8cf4eaf5aced29c4e41e3f20b30a784 \
|
||||
--hash=sha256:0693c609e9742c66ba4870bcee1ad5ff35462d5ffec18710b4ac89337ff16e27 \
|
||||
--hash=sha256:0771aed7f596c7d73444c847a1c16288937ef988dc04fb9f7be4b2aa91db609d \
|
||||
--hash=sha256:0af2e7c87d35b38732e810befb9d797a99279cbb85374d42ea61c1e9d23094b3 \
|
||||
--hash=sha256:14143ae966a6229350021384870458e4777d1eae4c28d1a7aa47f24d030e6678 \
|
||||
--hash=sha256:180c00c66bde6146a860cbb81b54ee0df350d2daf13ca85b275123bbf85de18a \
|
||||
--hash=sha256:1841e200fdafc3d51f974d9d377c079a0694a8f06de2e67b48150328d66d5483 \
|
||||
--hash=sha256:23d16d9f477bb55b6154654e0e74557040575d9d19fe78a161bd33d7d76808e8 \
|
||||
--hash=sha256:2b07ae0c1edaa0a36339ec6cce700f51b14a3fc6545fdd32930d2c83917332cf \
|
||||
--hash=sha256:2c926450857408e42f0bbc295e84395722ce74bae69a3b2aa2a65fe22cb14b99 \
|
||||
--hash=sha256:2e24900aa13212e75e5b366cb9065e78bbf3893d4baab6052d1aca10d46d944c \
|
||||
--hash=sha256:303e04d422e9b911a09ad499b0368dc551e8c3cd15293c99160c7f1f07b59a48 \
|
||||
--hash=sha256:352bd4c8c72d508778cf05ab491f6ef36149f4d0cb3c56b1b4302852255d05d5 \
|
||||
--hash=sha256:3843f84a6c465a36559161e6c59dce2f2ac10943040c2fd021cfb70d58c4ad56 \
|
||||
--hash=sha256:394c9c242113bfb4b9aa36e2b80a05ffa163a30691c7b5a29eba82e937895d5e \
|
||||
--hash=sha256:3bbdf44855ed8f0fbcd102ef05ec3012d6a4fd7c7562403f76ce6a52aeffb2b1 \
|
||||
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|
||||
--hash=sha256:e701b9dfb121faf71b0c5757485fc49e1b511b7b8a80034aa1f580488f8f872e \
|
||||
--hash=sha256:e8ab4d5acdd3ac17161539d9f2ea764497dc269dcd8dc722ae4a394c7b64ae7f \
|
||||
--hash=sha256:ea042ba262ea2a95d93c4d2d5879df956cf6c85ce22c037e3f0d4491182f10c5 \
|
||||
--hash=sha256:ed73d619e203aad54e2e6119a2b58b7568a36bd50a547817d13618ea0acf4412 \
|
||||
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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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|
||||
--hash=sha256:ffa6762f8ef1e7dfba101babe43de6edc541cbe64d33d816314ac67cd76c3979
|
||||
llama-cpp-python==0.1.67 ; python_version >= "3.8" and python_version < "4.0" \
|
||||
--hash=sha256:33bdcd42b30df3c21d56ce094132e1cdc0da0f8a27109f8eaf698addad02fd20
|
||||
markupsafe==2.1.3 ; python_version >= "3.8" and python_version < "4.0" \
|
||||
--hash=sha256:05fb21170423db021895e1ea1e1f3ab3adb85d1c2333cbc2310f2a26bc77272e \
|
||||
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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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|
||||
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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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|
||||
--hash=sha256:9402b03f1a1b4dc4c19845e5c749e3ab82d5078d16a2a4c2cd2df62d57bb0707 \
|
||||
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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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|
||||
--hash=sha256:cb0932dc158471523c9637e807d9bfb93e06a95cbf010f1a38b98623b929ef2b \
|
||||
--hash=sha256:cd0f502fe016460680cd20aaa5a76d241d6f35a1c3350c474bac1273803893fa \
|
||||
--hash=sha256:ceb01949af7121f9fc39f7d27f91be8546f3fb112c608bc4029aef0bab86a2a5 \
|
||||
--hash=sha256:d080e0a5eb2529460b30190fcfcc4199bd7f827663f858a226a81bc27beaa97e \
|
||||
--hash=sha256:dd15ff04ffd7e05ffcb7fe79f1b98041b8ea30ae9234aed2a9168b5797c3effb \
|
||||
--hash=sha256:df0be2b576a7abbf737b1575f048c23fb1d769f267ec4358296f31c2479db8f9 \
|
||||
--hash=sha256:e09031c87a1e51556fdcb46e5bd4f59dfb743061cf93c4d6831bf894f125eb57 \
|
||||
--hash=sha256:e4dd52d80b8c83fdce44e12478ad2e85c64ea965e75d66dbeafb0a3e77308fcc \
|
||||
--hash=sha256:fec21693218efe39aa7f8599346e90c705afa52c5b31ae019b2e57e8f6542bb2
|
||||
multidict==6.0.4 ; python_version >= "3.8" and python_version < "4.0" \
|
||||
--hash=sha256:01a3a55bd90018c9c080fbb0b9f4891db37d148a0a18722b42f94694f8b6d4c9 \
|
||||
--hash=sha256:0b1a97283e0c85772d613878028fec909f003993e1007eafa715b24b377cb9b8 \
|
||||
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|
||||
--hash=sha256:11bdf3f5e1518b24530b8241529d2050014c884cf18b6fc69c0c2b30ca248710 \
|
||||
--hash=sha256:1502e24330eb681bdaa3eb70d6358e818e8e8f908a22a1851dfd4e15bc2f8161 \
|
||||
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|
||||
--hash=sha256:16d232d4e5396c2efbbf4f6d4df89bfa905eb0d4dc5b3549d872ab898451f569 \
|
||||
--hash=sha256:21a12c4eb6ddc9952c415f24eef97e3e55ba3af61f67c7bc388dcdec1404a067 \
|
||||
--hash=sha256:27c523fbfbdfd19c6867af7346332b62b586eed663887392cff78d614f9ec313 \
|
||||
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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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|
||||
--hash=sha256:4d1a3d7ef5e96b1c9e92f973e43aa5e5b96c659c9bc3124acbbd81b0b9c8a951 \
|
||||
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|
||||
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|
||||
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|
||||
--hash=sha256:52f2dffc8acaba9a2f27174c41c9e57f60b907bb9f096b36b1a1f3be71c6284d \
|
||||
--hash=sha256:574b7eae1ab267e5f8285f0fe881f17efe4b98c39a40858247720935b893bba8 \
|
||||
--hash=sha256:5979b5632c3e3534e42ca6ff856bb24b2e3071b37861c2c727ce220d80eee9ed \
|
||||
--hash=sha256:59d43b61c59d82f2effb39a93c48b845efe23a3852d201ed2d24ba830d0b4cf2 \
|
||||
--hash=sha256:5a4dcf02b908c3b8b17a45fb0f15b695bf117a67b76b7ad18b73cf8e92608775 \
|
||||
--hash=sha256:5cad9430ab3e2e4fa4a2ef4450f548768400a2ac635841bc2a56a2052cdbeb87 \
|
||||
--hash=sha256:5fc1b16f586f049820c5c5b17bb4ee7583092fa0d1c4e28b5239181ff9532e0c \
|
||||
--hash=sha256:62501642008a8b9871ddfccbf83e4222cf8ac0d5aeedf73da36153ef2ec222d2 \
|
||||
--hash=sha256:64bdf1086b6043bf519869678f5f2757f473dee970d7abf6da91ec00acb9cb98 \
|
||||
--hash=sha256:64da238a09d6039e3bd39bb3aee9c21a5e34f28bfa5aa22518581f910ff94af3 \
|
||||
--hash=sha256:666daae833559deb2d609afa4490b85830ab0dfca811a98b70a205621a6109fe \
|
||||
--hash=sha256:67040058f37a2a51ed8ea8f6b0e6ee5bd78ca67f169ce6122f3e2ec80dfe9b78 \
|
||||
--hash=sha256:6748717bb10339c4760c1e63da040f5f29f5ed6e59d76daee30305894069a660 \
|
||||
--hash=sha256:6b181d8c23da913d4ff585afd1155a0e1194c0b50c54fcfe286f70cdaf2b7176 \
|
||||
--hash=sha256:6ed5f161328b7df384d71b07317f4d8656434e34591f20552c7bcef27b0ab88e \
|
||||
--hash=sha256:7582a1d1030e15422262de9f58711774e02fa80df0d1578995c76214f6954988 \
|
||||
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|
||||
--hash=sha256:7d6ae9d593ef8641544d6263c7fa6408cc90370c8cb2bbb65f8d43e5b0351d9c \
|
||||
--hash=sha256:81a4f0b34bd92df3da93315c6a59034df95866014ac08535fc819f043bfd51f0 \
|
||||
--hash=sha256:8316a77808c501004802f9beebde51c9f857054a0c871bd6da8280e718444449 \
|
||||
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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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|
||||
--hash=sha256:c048099e4c9e9d615545e2001d3d8a4380bd403e1a0578734e0d31703d1b0c0b \
|
||||
--hash=sha256:c5cb09abb18c1ea940fb99360ea0396f34d46566f157122c92dfa069d3e0e982 \
|
||||
--hash=sha256:cc8e1d0c705233c5dd0c5e6460fbad7827d5d36f310a0fadfd45cc3029762258 \
|
||||
--hash=sha256:d5e3fc56f88cc98ef8139255cf8cd63eb2c586531e43310ff859d6bb3a6b51f1 \
|
||||
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|
||||
--hash=sha256:d6c254ba6e45d8e72739281ebc46ea5eb5f101234f3ce171f0e9f5cc86991480 \
|
||||
--hash=sha256:d6d635d5209b82a3492508cf5b365f3446afb65ae7ebd755e70e18f287b0adf7 \
|
||||
--hash=sha256:dcfe792765fab89c365123c81046ad4103fcabbc4f56d1c1997e6715e8015461 \
|
||||
--hash=sha256:ddd3915998d93fbcd2566ddf9cf62cdb35c9e093075f862935573d265cf8f65d \
|
||||
--hash=sha256:ddff9c4e225a63a5afab9dd15590432c22e8057e1a9a13d28ed128ecf047bbdc \
|
||||
--hash=sha256:e41b7e2b59679edfa309e8db64fdf22399eec4b0b24694e1b2104fb789207779 \
|
||||
--hash=sha256:e69924bfcdda39b722ef4d9aa762b2dd38e4632b3641b1d9a57ca9cd18f2f83a \
|
||||
--hash=sha256:ea20853c6dbbb53ed34cb4d080382169b6f4554d394015f1bef35e881bf83547 \
|
||||
--hash=sha256:ee2a1ece51b9b9e7752e742cfb661d2a29e7bcdba2d27e66e28a99f1890e4fa0 \
|
||||
--hash=sha256:eeb6dcc05e911516ae3d1f207d4b0520d07f54484c49dfc294d6e7d63b734171 \
|
||||
--hash=sha256:f70b98cd94886b49d91170ef23ec5c0e8ebb6f242d734ed7ed677b24d50c82cf \
|
||||
--hash=sha256:fc35cb4676846ef752816d5be2193a1e8367b4c1397b74a565a9d0389c433a1d \
|
||||
--hash=sha256:ff959bee35038c4624250473988b24f846cbeb2c6639de3602c073f10410ceba
|
||||
numpy==1.24.4 ; python_version >= "3.8" and python_version < "4.0" \
|
||||
--hash=sha256:04640dab83f7c6c85abf9cd729c5b65f1ebd0ccf9de90b270cd61935eef0197f \
|
||||
--hash=sha256:1452241c290f3e2a312c137a9999cdbf63f78864d63c79039bda65ee86943f61 \
|
||||
--hash=sha256:222e40d0e2548690405b0b3c7b21d1169117391c2e82c378467ef9ab4c8f0da7 \
|
||||
--hash=sha256:2541312fbf09977f3b3ad449c4e5f4bb55d0dbf79226d7724211acc905049400 \
|
||||
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||||
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|
||||
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python-levenshtein==0.21.1 ; python_version >= "3.8" and python_version < "4.0" \
|
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pyyaml==6.0 ; python_version >= "3.8" and python_version < "4.0" \
|
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||||
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|
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||||
--hash=sha256:de784bbe06d32e66617cd20766c37aae2438902d54b3fa608d2e0a929ca705f4 \
|
||||
--hash=sha256:e0755f5ac6c3d1dc2505eb2e6eaf5508ff17b42c084406714fbabf2d50d098b6 \
|
||||
--hash=sha256:e549da8d68ad4ee385c918ea8b9efeda875df9edf6c6b48df927bd061c00bfef \
|
||||
--hash=sha256:e6772eb7cc4429f1eae5a9b41e5b0b1af8f0d50727c6e338d9ad5bceee01da5a \
|
||||
--hash=sha256:ea3e46a534de97a6cad2018cb950492a0fcacad380e35440ce3c1c8fef96a261 \
|
||||
--hash=sha256:ec5523d5c08c639cd4e301d42f3ad7c6fb061a1f1cd6b5b627e59af345edfed7 \
|
||||
--hash=sha256:ef3ad80458e47723812976a2ea1282ff207ad20e6cb19da1917f76699bd5aaa5 \
|
||||
--hash=sha256:ef6c38040d868dcc0132fad377aafeb5b2da71354759e77f41ae599316df2dee \
|
||||
--hash=sha256:f1e23665be5918f979180130babedab9317fbb34cdae237c7defad7e86bc684e \
|
||||
--hash=sha256:f25d1975e846d07990cf946a5927a932aa7cccd308ae9979b03a58ff1cd80087 \
|
||||
--hash=sha256:f7acc5c9c7cf567372de5b6c817f93db508e7b9bd7f29bd6187df8d2cc60ced5 \
|
||||
--hash=sha256:fb7049dff52cded65184a3d2ff45cfd226bff7314f49a8f4b83f943eea9181a7 \
|
||||
--hash=sha256:fdd2ab5ab56fcaf839a9f58caa8756dbfeba0b3dc187850b763d0a1e6ee9c97a
|
||||
requests==2.31.0 ; python_version >= "3.8" and python_version < "4.0" \
|
||||
--hash=sha256:58cd2187c01e70e6e26505bca751777aa9f2ee0b7f4300988b709f44e013003f \
|
||||
--hash=sha256:942c5a758f98d790eaed1a29cb6eefc7ffb0d1cf7af05c3d2791656dbd6ad1e1
|
||||
termcolor==2.3.0 ; python_version >= "3.8" and python_version < "4.0" \
|
||||
--hash=sha256:3afb05607b89aed0ffe25202399ee0867ad4d3cb4180d98aaf8eefa6a5f7d475 \
|
||||
--hash=sha256:b5b08f68937f138fe92f6c089b99f1e2da0ae56c52b78bf7075fd95420fd9a5a
|
||||
thefuzz[speedup]==0.19.0 ; python_version >= "3.8" and python_version < "4.0" \
|
||||
--hash=sha256:4fcdde8e40f5ca5e8106bc7665181f9598a9c8b18b0a4d38c41a095ba6788972 \
|
||||
--hash=sha256:6f7126db2f2c8a54212b05e3a740e45f4291c497d75d20751728f635bb74aa3d
|
||||
tqdm==4.65.0 ; python_version >= "3.8" and python_version < "4.0" \
|
||||
--hash=sha256:1871fb68a86b8fb3b59ca4cdd3dcccbc7e6d613eeed31f4c332531977b89beb5 \
|
||||
--hash=sha256:c4f53a17fe37e132815abceec022631be8ffe1b9381c2e6e30aa70edc99e9671
|
||||
typing-extensions==4.7.0 ; python_version >= "3.8" and python_version < "4.0" \
|
||||
--hash=sha256:5d8c9dac95c27d20df12fb1d97b9793ab8b2af8a3a525e68c80e21060c161771 \
|
||||
--hash=sha256:935ccf31549830cda708b42289d44b6f74084d616a00be651601a4f968e77c82
|
||||
urllib3==2.0.3 ; python_version >= "3.8" and python_version < "4.0" \
|
||||
--hash=sha256:48e7fafa40319d358848e1bc6809b208340fafe2096f1725d05d67443d0483d1 \
|
||||
--hash=sha256:bee28b5e56addb8226c96f7f13ac28cb4c301dd5ea8a6ca179c0b9835e032825
|
||||
validators==0.20.0 ; python_version >= "3.8" and python_version < "4.0" \
|
||||
--hash=sha256:24148ce4e64100a2d5e267233e23e7afeb55316b47d30faae7eb6e7292bc226a
|
||||
yarl==1.9.2 ; python_version >= "3.8" and python_version < "4.0" \
|
||||
--hash=sha256:04ab9d4b9f587c06d801c2abfe9317b77cdf996c65a90d5e84ecc45010823571 \
|
||||
--hash=sha256:066c163aec9d3d073dc9ffe5dd3ad05069bcb03fcaab8d221290ba99f9f69ee3 \
|
||||
--hash=sha256:13414591ff516e04fcdee8dc051c13fd3db13b673c7a4cb1350e6b2ad9639ad3 \
|
||||
--hash=sha256:149ddea5abf329752ea5051b61bd6c1d979e13fbf122d3a1f9f0c8be6cb6f63c \
|
||||
--hash=sha256:159d81f22d7a43e6eabc36d7194cb53f2f15f498dbbfa8edc8a3239350f59fe7 \
|
||||
--hash=sha256:1b1bba902cba32cdec51fca038fd53f8beee88b77efc373968d1ed021024cc04 \
|
||||
--hash=sha256:22a94666751778629f1ec4280b08eb11815783c63f52092a5953faf73be24191 \
|
||||
--hash=sha256:2a96c19c52ff442a808c105901d0bdfd2e28575b3d5f82e2f5fd67e20dc5f4ea \
|
||||
--hash=sha256:2b0738fb871812722a0ac2154be1f049c6223b9f6f22eec352996b69775b36d4 \
|
||||
--hash=sha256:2c315df3293cd521033533d242d15eab26583360b58f7ee5d9565f15fee1bef4 \
|
||||
--hash=sha256:32f1d071b3f362c80f1a7d322bfd7b2d11e33d2adf395cc1dd4df36c9c243095 \
|
||||
--hash=sha256:3458a24e4ea3fd8930e934c129b676c27452e4ebda80fbe47b56d8c6c7a63a9e \
|
||||
--hash=sha256:38a3928ae37558bc1b559f67410df446d1fbfa87318b124bf5032c31e3447b74 \
|
||||
--hash=sha256:3da8a678ca8b96c8606bbb8bfacd99a12ad5dd288bc6f7979baddd62f71c63ef \
|
||||
--hash=sha256:494053246b119b041960ddcd20fd76224149cfea8ed8777b687358727911dd33 \
|
||||
--hash=sha256:50f33040f3836e912ed16d212f6cc1efb3231a8a60526a407aeb66c1c1956dde \
|
||||
--hash=sha256:52a25809fcbecfc63ac9ba0c0fb586f90837f5425edfd1ec9f3372b119585e45 \
|
||||
--hash=sha256:53338749febd28935d55b41bf0bcc79d634881195a39f6b2f767870b72514caf \
|
||||
--hash=sha256:5415d5a4b080dc9612b1b63cba008db84e908b95848369aa1da3686ae27b6d2b \
|
||||
--hash=sha256:5610f80cf43b6202e2c33ba3ec2ee0a2884f8f423c8f4f62906731d876ef4fac \
|
||||
--hash=sha256:566185e8ebc0898b11f8026447eacd02e46226716229cea8db37496c8cdd26e0 \
|
||||
--hash=sha256:56ff08ab5df8429901ebdc5d15941b59f6253393cb5da07b4170beefcf1b2528 \
|
||||
--hash=sha256:59723a029760079b7d991a401386390c4be5bfec1e7dd83e25a6a0881859e716 \
|
||||
--hash=sha256:5fcd436ea16fee7d4207c045b1e340020e58a2597301cfbcfdbe5abd2356c2fb \
|
||||
--hash=sha256:61016e7d582bc46a5378ffdd02cd0314fb8ba52f40f9cf4d9a5e7dbef88dee18 \
|
||||
--hash=sha256:63c48f6cef34e6319a74c727376e95626f84ea091f92c0250a98e53e62c77c72 \
|
||||
--hash=sha256:646d663eb2232d7909e6601f1a9107e66f9791f290a1b3dc7057818fe44fc2b6 \
|
||||
--hash=sha256:662e6016409828ee910f5d9602a2729a8a57d74b163c89a837de3fea050c7582 \
|
||||
--hash=sha256:674ca19cbee4a82c9f54e0d1eee28116e63bc6fd1e96c43031d11cbab8b2afd5 \
|
||||
--hash=sha256:6a5883464143ab3ae9ba68daae8e7c5c95b969462bbe42e2464d60e7e2698368 \
|
||||
--hash=sha256:6e7221580dc1db478464cfeef9b03b95c5852cc22894e418562997df0d074ccc \
|
||||
--hash=sha256:75df5ef94c3fdc393c6b19d80e6ef1ecc9ae2f4263c09cacb178d871c02a5ba9 \
|
||||
--hash=sha256:783185c75c12a017cc345015ea359cc801c3b29a2966c2655cd12b233bf5a2be \
|
||||
--hash=sha256:822b30a0f22e588b32d3120f6d41e4ed021806418b4c9f0bc3048b8c8cb3f92a \
|
||||
--hash=sha256:8288d7cd28f8119b07dd49b7230d6b4562f9b61ee9a4ab02221060d21136be80 \
|
||||
--hash=sha256:82aa6264b36c50acfb2424ad5ca537a2060ab6de158a5bd2a72a032cc75b9eb8 \
|
||||
--hash=sha256:832b7e711027c114d79dffb92576acd1bd2decc467dec60e1cac96912602d0e6 \
|
||||
--hash=sha256:838162460b3a08987546e881a2bfa573960bb559dfa739e7800ceeec92e64417 \
|
||||
--hash=sha256:83fcc480d7549ccebe9415d96d9263e2d4226798c37ebd18c930fce43dfb9574 \
|
||||
--hash=sha256:84e0b1599334b1e1478db01b756e55937d4614f8654311eb26012091be109d59 \
|
||||
--hash=sha256:891c0e3ec5ec881541f6c5113d8df0315ce5440e244a716b95f2525b7b9f3608 \
|
||||
--hash=sha256:8c2ad583743d16ddbdf6bb14b5cd76bf43b0d0006e918809d5d4ddf7bde8dd82 \
|
||||
--hash=sha256:8c56986609b057b4839968ba901944af91b8e92f1725d1a2d77cbac6972b9ed1 \
|
||||
--hash=sha256:8ea48e0a2f931064469bdabca50c2f578b565fc446f302a79ba6cc0ee7f384d3 \
|
||||
--hash=sha256:8ec53a0ea2a80c5cd1ab397925f94bff59222aa3cf9c6da938ce05c9ec20428d \
|
||||
--hash=sha256:95d2ecefbcf4e744ea952d073c6922e72ee650ffc79028eb1e320e732898d7e8 \
|
||||
--hash=sha256:9b3152f2f5677b997ae6c804b73da05a39daa6a9e85a512e0e6823d81cdad7cc \
|
||||
--hash=sha256:9bf345c3a4f5ba7f766430f97f9cc1320786f19584acc7086491f45524a551ac \
|
||||
--hash=sha256:a60347f234c2212a9f0361955007fcf4033a75bf600a33c88a0a8e91af77c0e8 \
|
||||
--hash=sha256:a74dcbfe780e62f4b5a062714576f16c2f3493a0394e555ab141bf0d746bb955 \
|
||||
--hash=sha256:a83503934c6273806aed765035716216cc9ab4e0364f7f066227e1aaea90b8d0 \
|
||||
--hash=sha256:ac9bb4c5ce3975aeac288cfcb5061ce60e0d14d92209e780c93954076c7c4367 \
|
||||
--hash=sha256:aff634b15beff8902d1f918012fc2a42e0dbae6f469fce134c8a0dc51ca423bb \
|
||||
--hash=sha256:b03917871bf859a81ccb180c9a2e6c1e04d2f6a51d953e6a5cdd70c93d4e5a2a \
|
||||
--hash=sha256:b124e2a6d223b65ba8768d5706d103280914d61f5cae3afbc50fc3dfcc016623 \
|
||||
--hash=sha256:b25322201585c69abc7b0e89e72790469f7dad90d26754717f3310bfe30331c2 \
|
||||
--hash=sha256:b7232f8dfbd225d57340e441d8caf8652a6acd06b389ea2d3222b8bc89cbfca6 \
|
||||
--hash=sha256:b8cc1863402472f16c600e3e93d542b7e7542a540f95c30afd472e8e549fc3f7 \
|
||||
--hash=sha256:b9a4e67ad7b646cd6f0938c7ebfd60e481b7410f574c560e455e938d2da8e0f4 \
|
||||
--hash=sha256:be6b3fdec5c62f2a67cb3f8c6dbf56bbf3f61c0f046f84645cd1ca73532ea051 \
|
||||
--hash=sha256:bf74d08542c3a9ea97bb8f343d4fcbd4d8f91bba5ec9d5d7f792dbe727f88938 \
|
||||
--hash=sha256:c027a6e96ef77d401d8d5a5c8d6bc478e8042f1e448272e8d9752cb0aff8b5c8 \
|
||||
--hash=sha256:c0c77533b5ed4bcc38e943178ccae29b9bcf48ffd1063f5821192f23a1bd27b9 \
|
||||
--hash=sha256:c1012fa63eb6c032f3ce5d2171c267992ae0c00b9e164efe4d73db818465fac3 \
|
||||
--hash=sha256:c3a53ba34a636a256d767c086ceb111358876e1fb6b50dfc4d3f4951d40133d5 \
|
||||
--hash=sha256:d4e2c6d555e77b37288eaf45b8f60f0737c9efa3452c6c44626a5455aeb250b9 \
|
||||
--hash=sha256:de119f56f3c5f0e2fb4dee508531a32b069a5f2c6e827b272d1e0ff5ac040333 \
|
||||
--hash=sha256:e65610c5792870d45d7b68c677681376fcf9cc1c289f23e8e8b39c1485384185 \
|
||||
--hash=sha256:e9fdc7ac0d42bc3ea78818557fab03af6181e076a2944f43c38684b4b6bed8e3 \
|
||||
--hash=sha256:ee4afac41415d52d53a9833ebae7e32b344be72835bbb589018c9e938045a560 \
|
||||
--hash=sha256:f364d3480bffd3aa566e886587eaca7c8c04d74f6e8933f3f2c996b7f09bee1b \
|
||||
--hash=sha256:f3b078dbe227f79be488ffcfc7a9edb3409d018e0952cf13f15fd6512847f3f7 \
|
||||
--hash=sha256:f4e2d08f07a3d7d3e12549052eb5ad3eab1c349c53ac51c209a0e5991bbada78 \
|
||||
--hash=sha256:f7a3d8146575e08c29ed1cd287068e6d02f1c7bdff8970db96683b9591b86ee7
|
||||
yaspin==2.3.0 ; python_version >= "3.8" and python_version < "4.0" \
|
||||
--hash=sha256:17b5548479b3d5b30adec7a87ffcdcddb403d14a2bb86fbcee97f37951e13427 \
|
||||
--hash=sha256:547afd1a9700ac3a29a9f5591c70343bef186ed5dfb5e545a9bb0c77e561a1c9
|
||||
|
|
@ -17,26 +17,14 @@ import (
|
|||
func Serve(ln net.Listener) error {
|
||||
r := gin.Default()
|
||||
|
||||
var l *llama.LLama
|
||||
|
||||
gpulayers := 1
|
||||
// TODO: these should be request parameters
|
||||
gpulayers := 0
|
||||
tokens := 512
|
||||
threads := runtime.NumCPU()
|
||||
model := "/Users/pdevine/.cache/gpt4all/GPT4All-13B-snoozy.ggmlv3.q4_0.bin"
|
||||
|
||||
r.POST("/api/load", func(c *gin.Context) {
|
||||
var err error
|
||||
l, err = llama.New(model, llama.EnableF16Memory, llama.SetContext(128), llama.EnableEmbeddings, llama.SetGPULayers(gpulayers))
|
||||
if err != nil {
|
||||
fmt.Println("Loading the model failed:", err.Error())
|
||||
}
|
||||
})
|
||||
|
||||
r.POST("/api/unload", func(c *gin.Context) {
|
||||
})
|
||||
|
||||
r.POST("/api/generate", func(c *gin.Context) {
|
||||
// TODO: set prompt from template
|
||||
fmt.Println("Generating text...")
|
||||
|
||||
var req api.GenerateRequest
|
||||
if err := c.ShouldBindJSON(&req); err != nil {
|
||||
|
|
@ -44,6 +32,14 @@ func Serve(ln net.Listener) error {
|
|||
return
|
||||
}
|
||||
|
||||
fmt.Println(req)
|
||||
|
||||
l, err := llama.New(req.Model, llama.EnableF16Memory, llama.SetContext(128), llama.EnableEmbeddings, llama.SetGPULayers(gpulayers))
|
||||
if err != nil {
|
||||
fmt.Println("Loading the model failed:", err.Error())
|
||||
return
|
||||
}
|
||||
|
||||
ch := make(chan string)
|
||||
|
||||
go func() {
|
||||
|
|
@ -65,11 +61,6 @@ func Serve(ln net.Listener) error {
|
|||
c.SSEvent("token", tok)
|
||||
return true
|
||||
})
|
||||
|
||||
// embeds, err := l.Embeddings(text)
|
||||
// if err != nil {
|
||||
// fmt.Printf("Embeddings: error %s \n", err.Error())
|
||||
// }
|
||||
})
|
||||
|
||||
log.Printf("Listening on %s", ln.Addr())
|
||||
|
|
|
|||
|
|
@ -1,63 +0,0 @@
|
|||
package signature
|
||||
|
||||
import (
|
||||
"bytes"
|
||||
"crypto/sha256"
|
||||
"encoding/base64"
|
||||
"encoding/hex"
|
||||
"fmt"
|
||||
"strings"
|
||||
|
||||
"golang.org/x/crypto/ssh"
|
||||
)
|
||||
|
||||
type SignatureData struct {
|
||||
Method string
|
||||
Path string
|
||||
Data []byte
|
||||
}
|
||||
|
||||
func GetBytesToSign(s SignatureData) []byte {
|
||||
// contentHash = base64(hex(sha256(s.Data)))
|
||||
hash := sha256.Sum256(s.Data)
|
||||
hashHex := make([]byte, hex.EncodedLen(len(hash)))
|
||||
hex.Encode(hashHex, hash[:])
|
||||
contentHash := base64.StdEncoding.EncodeToString(hashHex)
|
||||
|
||||
// bytesToSign e.g.: "GET,http://localhost,OTdkZjM1O...
|
||||
bytesToSign := []byte(strings.Join([]string{s.Method, s.Path, contentHash}, ","))
|
||||
|
||||
return bytesToSign
|
||||
}
|
||||
|
||||
// SignData takes a SignatureData object and signs it with a raw private key
|
||||
func SignAuthData(s SignatureData, rawKey []byte) (string, error) {
|
||||
bytesToSign := GetBytesToSign(s)
|
||||
|
||||
// TODO replace this w/ a non-SSH based private key
|
||||
privateKey, err := ssh.ParseRawPrivateKey(rawKey)
|
||||
if err != nil {
|
||||
return "", err
|
||||
}
|
||||
|
||||
signer, err := ssh.NewSignerFromKey(privateKey)
|
||||
if err != nil {
|
||||
return "", err
|
||||
}
|
||||
|
||||
// get the pubkey, but remove the type
|
||||
pubKey := ssh.MarshalAuthorizedKey(signer.PublicKey())
|
||||
parts := bytes.Split(pubKey, []byte(" "))
|
||||
if len(parts) < 2 {
|
||||
return "", fmt.Errorf("malformed private key")
|
||||
}
|
||||
|
||||
signedData, err := signer.Sign(nil, bytesToSign)
|
||||
if err != nil {
|
||||
return "", err
|
||||
}
|
||||
|
||||
// signature is <pubkey>:<signature>
|
||||
sig := fmt.Sprintf("%s:%s", bytes.TrimSpace(parts[1]), base64.StdEncoding.EncodeToString(signedData.Blob))
|
||||
return sig, nil
|
||||
}
|
||||
Loading…
Reference in a new issue