Features grouped by lifecycle
From raw documents through training to deployment artifacts.
Training & alignment
Six post-training paradigms, parameter-efficient methods, and distributed configs.
SFT
Supervised fine-tuning on instruction-formatted JSONL.
DPO & SimPO
Direct Preference Optimization on chosen/rejected pairs; SimPO for reference-free, lower-memory variants.
KTO
Kahneman-Tversky preference learning on binary feedback.
ORPO
Single-pass odds-ratio preference optimization.
GRPO
Group Relative Policy Optimization for reasoning RL.
QLoRA + DoRA
4-bit NF4 quantization with LoRA.
GaLore
Gradient low-rank projection for full-parameter training.
DeepSpeed & FSDP
Multi-GPU distributed training. ZeRO presets, FSDP, Unsloth backend.
Long-context
RoPE scaling, NEFTune noise injection, sample packing.
Evaluation & safety
Benchmarks, judges, harm classification, and the auto-revert that ties them together.
Benchmark integration
Plug-in lm-evaluation-harness tasks. Per-task floor thresholds.
LLM-as-judge
Quality scoring via OpenAI API or a local judge model.
Llama Guard safety
Confidence-weighted scoring across 14 harm categories.
Auto-revert
If thresholds fail, ForgeLM rolls back to the last-good checkpoint.
Trend tracking
Cross-run safety history (safety_trend.jsonl per output_dir).
Exit codes
0 / 1 / 2 / 3 / 4 — a public contract for CI/CD.
Standalone safety eval
forgelm safety-eval runs Llama Guard against a --probes JSONL prompt file (or --default-probes for the bundled 50-prompt set) and emits a per-category safety summary (safety_results.json + safety_trend.jsonl).
Data ingestion & audit
Raw documents to clean JSONL, with PII / secrets / quality / leakage all surfaced before training.
Multi-format ingest
PDF, DOCX, EPUB, TXT, Markdown → SFT-ready JSONL.
PII masking
Email, phone, credit card, IBAN, national IDs.
Secrets scrubbing
AWS keys, GitHub PATs, JWTs — redacted with [REDACTED-SECRET].
Near-duplicate detection
LSH-banded simhash or MinHash LSH.
Quality filter
Gopher / C4 / RefinedWeb-style heuristics.
Language detection
Top-3 language counts per split via langdetect.
Cross-split leakage
Catches train rows that also appear in validation/test.
Annex IV provenance
Audit report embedded in EU AI Act Article 10 governance artifact.
Enterprise & MLOps
Built for pipelines: containers, webhooks, structured outputs, air-gap, model cards.
Webhook notifications
Slack, Teams, Discord, or any HTTP webhook receiver alerts on training events.
Experiment tracking
W&B, MLflow, TensorBoard via report_to.
JSON output mode
--output-format json emits a machine-readable envelope.
Air-gap operation
No mandatory internet calls.
Docker & compose
Official Dockerfile and docker-compose.yaml.
Model card auto-gen
HuggingFace-compatible README with metrics and config.
GPU cost estimation
Auto-detection across 16 GPU profiles with cost tracking.
Synthetic data pipeline
Teacher-to-student distillation with --generate-data.
Model merging
TIES, DARE, SLERP, linear merge via --merge.
Library API
from forgelm import … exposes ForgeTrainer, audit_dataset, verify_audit_log, verify_annex_iv_artifact, mask_pii, and friends as Python functions for embedding inside other pipelines.
Environment diagnostics
forgelm doctor probes Python, GPU, drivers, optional extras, disk space, network, and prints a pass / warn / fail triage report.
Air-gap pre-cache
forgelm cache-models and forgelm cache-tasks stage HuggingFace models and lm-eval-harness datasets for offline runs.
Deterministic audit workers
--workers N parallelises the data audit while preserving deterministic, byte-identical reports across runs.
Supply-chain security
CycloneDX 1.5 SBOM emitted per release tag, plus pip-audit nightly and bandit in CI.
Compliance & GDPR tooling
EU AI Act Articles 9-17, GDPR Article 15 + 17, ISO 27001 / SOC 2 Type II alignment evidence.
Article 14 staging gate
forgelm approve / forgelm reject + forgelm approvals implement the human-oversight staging directory with audit-stamped sign-off.
GDPR Article 17 erasure
forgelm purge drops a data-subject's rows from training corpora and stamps the deletion in the append-only audit log.
GDPR Article 15 access
forgelm reverse-pii scans masked JSONL corpora for forensic substring or salted-digest matches that surface every row in which a data-subject identifier appears.
Annex IV verifier
forgelm verify-annex-iv validates the Annex IV §1-9 schema completeness and the bundle's manifest_hash for tampering detection. (Audit-chain integrity is the separate forgelm verify-audit command.)
GGUF integrity verifier
forgelm verify-gguf validates the GGUF magic header, parses the metadata block, and re-checks the SHA-256 sidecar (when present) before you ship a quantized model.
ISO 27001 / SOC 2 alignment
Audit-trail, change-management, data-lineage, and supply-chain evidence the deployer's auditor asks for. Software is aligned, not certified.
Want to see it in YAML?
Every feature on this page maps to a field on a single YAML config.