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>
67 lines
2.5 KiB
Go
67 lines
2.5 KiB
Go
package ml
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import (
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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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agentM3Host string
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agentM3User string
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agentM3SSHKey string
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agentM3AdapterBase string
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agentBaseModel string
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agentPollInterval int
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agentWorkDir string
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agentFilter string
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agentForce bool
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agentOneShot bool
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agentDryRun bool
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)
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var agentCmd = &cli.Command{
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Use: "agent",
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Short: "Run the scoring agent daemon",
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Long: "Polls M3 for unscored LoRA checkpoints, converts, probes, and pushes results to InfluxDB.",
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RunE: runAgent,
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}
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func init() {
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agentCmd.Flags().StringVar(&agentM3Host, "m3-host", ml.EnvOr("M3_HOST", "10.69.69.108"), "M3 host address")
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agentCmd.Flags().StringVar(&agentM3User, "m3-user", ml.EnvOr("M3_USER", "claude"), "M3 SSH user")
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agentCmd.Flags().StringVar(&agentM3SSHKey, "m3-ssh-key", ml.EnvOr("M3_SSH_KEY", ml.ExpandHome("~/.ssh/id_ed25519")), "SSH key for M3")
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agentCmd.Flags().StringVar(&agentM3AdapterBase, "m3-adapter-base", ml.EnvOr("M3_ADAPTER_BASE", "/Volumes/Data/lem"), "Adapter base dir on M3")
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agentCmd.Flags().StringVar(&agentBaseModel, "base-model", ml.EnvOr("BASE_MODEL", "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B"), "HuggingFace base model ID")
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agentCmd.Flags().IntVar(&agentPollInterval, "poll", ml.IntEnvOr("POLL_INTERVAL", 300), "Poll interval in seconds")
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agentCmd.Flags().StringVar(&agentWorkDir, "work-dir", ml.EnvOr("WORK_DIR", "/tmp/scoring-agent"), "Working directory for adapters")
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agentCmd.Flags().StringVar(&agentFilter, "filter", "", "Filter adapter dirs by prefix")
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agentCmd.Flags().BoolVar(&agentForce, "force", false, "Re-score already-scored checkpoints")
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agentCmd.Flags().BoolVar(&agentOneShot, "one-shot", false, "Process one checkpoint and exit")
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agentCmd.Flags().BoolVar(&agentDryRun, "dry-run", false, "Discover and plan but don't execute")
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}
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func runAgent(cmd *cli.Command, args []string) error {
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cfg := &ml.AgentConfig{
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M3Host: agentM3Host,
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M3User: agentM3User,
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M3SSHKey: agentM3SSHKey,
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M3AdapterBase: agentM3AdapterBase,
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InfluxURL: influxURL,
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InfluxDB: influxDB,
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DBPath: dbPath,
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APIURL: apiURL,
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JudgeURL: judgeURL,
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JudgeModel: judgeModel,
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Model: modelName,
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BaseModel: agentBaseModel,
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PollInterval: agentPollInterval,
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WorkDir: agentWorkDir,
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Filter: agentFilter,
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Force: agentForce,
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OneShot: agentOneShot,
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DryRun: agentDryRun,
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}
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ml.RunAgentLoop(cfg)
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return nil
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}
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