5ff4b8a2eb
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>
40 lines
1.1 KiB
Go
40 lines
1.1 KiB
Go
package ml
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import (
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"fmt"
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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 (
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ggufInput string
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ggufConfig string
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ggufOutput string
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ggufArch string
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)
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var ggufCmd = &cli.Command{
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Use: "gguf",
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Short: "Convert MLX LoRA adapter to GGUF format",
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Long: "Converts an MLX safetensors LoRA adapter to GGUF v3 format for use with llama.cpp.",
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RunE: runGGUF,
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}
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func init() {
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ggufCmd.Flags().StringVar(&ggufInput, "input", "", "Input safetensors file (required)")
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ggufCmd.Flags().StringVar(&ggufConfig, "config", "", "Adapter config JSON (required)")
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ggufCmd.Flags().StringVar(&ggufOutput, "output", "", "Output GGUF file (required)")
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ggufCmd.Flags().StringVar(&ggufArch, "arch", "gemma3", "GGUF architecture name")
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ggufCmd.MarkFlagRequired("input")
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ggufCmd.MarkFlagRequired("config")
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ggufCmd.MarkFlagRequired("output")
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}
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func runGGUF(cmd *cli.Command, args []string) error {
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if err := ml.ConvertMLXtoGGUFLoRA(ggufInput, ggufConfig, ggufOutput, ggufArch); err != nil {
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return fmt.Errorf("convert to GGUF: %w", err)
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}
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fmt.Printf("GGUF LoRA adapter written to %s\n", ggufOutput)
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return nil
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}
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