548256312d
Port LEM scoring/training pipeline into CoreGo as pkg/ml with: - Inference abstraction with HTTP, llama-server, and Ollama backends - 3-tier scoring engine (heuristic, exact, LLM judge) - Capability and content probes for model evaluation - GGUF/safetensors format converters, MLX to PEFT adapter conversion - DuckDB integration for training data pipeline - InfluxDB metrics for lab dashboard - Training data export (JSONL + Parquet) - Expansion generation pipeline with distributed workers - 10 CLI commands under 'core ml' (score, probe, export, expand, status, gguf, convert, agent, worker) - 5 MCP tools (ml_generate, ml_score, ml_probe, ml_status, ml_backends) All 37 ML tests passing. Binary builds at 138MB with all commands. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
54 lines
1.2 KiB
Go
54 lines
1.2 KiB
Go
package ml
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import (
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"fmt"
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"os"
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"forge.lthn.ai/core/cli/pkg/cli"
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"forge.lthn.ai/core/cli/pkg/ml"
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)
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var statusCmd = &cli.Command{
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Use: "status",
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Short: "Show training and generation progress",
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Long: "Queries InfluxDB for training status, loss, and generation progress. Optionally shows DuckDB table counts.",
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RunE: runStatus,
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}
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func runStatus(cmd *cli.Command, args []string) error {
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influx := ml.NewInfluxClient(influxURL, influxDB)
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if err := ml.PrintStatus(influx, os.Stdout); err != nil {
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return fmt.Errorf("status: %w", err)
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}
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path := dbPath
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if path == "" {
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path = os.Getenv("LEM_DB")
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}
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if path != "" {
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db, err := ml.OpenDB(path)
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if err != nil {
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return fmt.Errorf("open db: %w", err)
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}
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defer db.Close()
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counts, err := db.TableCounts()
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if err != nil {
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return fmt.Errorf("table counts: %w", err)
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}
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fmt.Println()
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fmt.Println("DuckDB:")
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order := []string{"golden_set", "expansion_prompts", "seeds", "training_examples",
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"prompts", "gemini_responses", "benchmark_questions", "benchmark_results", "validations"}
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for _, table := range order {
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if count, ok := counts[table]; ok {
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fmt.Fprintf(os.Stdout, " %-22s %6d rows\n", table, count)
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}
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}
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}
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return nil
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}
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