provenir 0.5.1

The trust layer for model post-training: reproducibility, contamination-safe eval, reward-hacking detection, and signed provenance for any fine-tuning or RL run

Provenir

The trust layer for model post-training — reproducible, evaluation-first orchestration for LLM fine-tuning and RL.

Provenir sits above training engines (verl, TRL, Unsloth) and coordinates every layer of the fine-tuning and reinforcement-learning lifecycle — from raw data through preference learning, verifiable-reward RL, RAG dataset generation, model merging, benchmark evaluation, and production serving — in a single, auditable, reproducible pipeline. It adds the trust primitives that raw throughput engines leave out: RL observability, reward-hacking detection, contamination-safe evaluation, deterministic replay, and a signed Model Passport.

pip install provenir
pip install "provenir[train]"          # SFT + DPO + LoRA / QLoRA via TRL
pip install "provenir[all]"            # Everything

v0.4.0 · Apache-2.0 · Python ≥ 3.11 · 1038 tests · zero breaking changes

---

Why Provenir?

Every major fine-tuning framework optimises for raw throughput. Provenir optimises for trust — the ability to reproduce a run, audit every decision, catch regressions and reward-hacking before they reach production, verify rewards that cannot be gamed, and prove exactly what data and code produced a model.

	Axolotl	TRL	Unsloth	verl	Provenir
SFT / DPO / GRPO	✓	✓	✓	✓	✓
LoRA / QLoRA	✓	✓	✓	✓	✓
Reproducible manifests	—	—	—	—	✓
Eval-in-the-loop	—	partial	—	—	✓
LLM-as-Judge pipeline	—	—	—	—	✓
RLAIF (no human labels)	—	—	—	—	✓
Data flywheel (auto-augment)	—	—	—	—	✓
RAG dataset generation	—	—	—	—	✓
Adapter merging (SLERP/TIES/DARE)	—	—	—	—	✓
Standard benchmark suite	—	—	—	—	✓
Semantic decontamination	—	—	—	—	✓
REST API server	—	—	—	—	✓
Full governance & audit log	—	—	—	—	✓
Plugin architecture	—	—	—	—	✓
RL Flight Recorder	—	—	—	—	✓
Reward-hacking detection	—	—	—	—	✓
Verifiable-reward environments (RLVR)	—	—	—	—	✓
Contamination firewall	—	—	—	—	✓
Judge calibration	—	—	—	—	✓
Deterministic replay / lineage DAG	—	—	—	—	✓
Signed Model Passport	—	—	—	—	✓
Loop Doctor (differential diagnosis)	—	—	—	—	✓
Agentic environments (multi-turn tool use)	—	—	—	—	✓

---

New in v0.3 — The Trust Layer

v0.3.0 turns Provenir into the trust layer for model post-training. It orchestrates the best RL engines (verl, TRL, Unsloth) rather than reimplementing kernels, and wraps every run with the observability, verification, and provenance that RL at scale actually needs. Four pillars:

A. RL Flight Recorder + reward-hacking detection

A black box for RL runs. The Flight Recorder (provenir.observability) watches every step and flags KL blowup/collapse, entropy collapse, response-length explosion, GRPO advantage collapse, reward-std collapse, reward spikes, and gradient explosion. The reward-hacking detector catches the #1 RL failure mode — length inflation, format exploits, test tampering (unittest.skip / sys.exit(0) / monkeypatch), verifier gaming, proxy-reward divergence, degenerate repetition, and advantage collapse. No other OSS RL framework ships RL-native observability.

B. Contamination-safe trustworthy eval + judge calibration

A contamination firewall (provenir.eval.contamination, provenir.eval.canary) detects train/eval overlap via 13-gram, embedding, and exact matching (with MinHash for scale) and supports canary-tagged private eval vaults that fire if a held-out set leaks into training. Judge calibration (provenir.eval.judge_calibration) measures LLM-judge position bias, self-consistency, and flip-rate, with a DebiasedJudge (evaluates both orderings) and EnsembleJudge (majority vote).

C. Verifiable-reward environments + GRPO/DAPO/GSPO orchestration

A library of sandboxed, hack-resistant reward functions for RLVR (provenir.environments): ExactAnswerVerifier, MathVerifier, RegexFormatVerifier, JSONSchemaVerifier, ToolCallVerifier, ContainsVerifier, CompositeVerifier, and a CodeVerifier backed by a PythonSandbox (subprocess isolation + reward-hacking detection) — all behind an OpenEnv-compatible Environment protocol. Provenir's RLOrchestrator (provenir.train.rl) runs a real rollout → verify → reward → flight recorder → hacking detector → eval-gate loop over GRPO + DAPO + GSPO, delegating the gradient step to backend-agnostic adapters (provenir.train.backends.adapters) that wrap verl / TRL / Unsloth with capability detection and a BackendSelector that auto-routes by scale tier.

D. Deterministic replay + lineage DAG + signed Model Passport

Deterministic replay (provenir.provenance) captures a content-addressed environment fingerprint, kernel-determinism flags, a lineage DAG (dataset → run → adapter → eval → merge), and a ReplayEngine. The Model Passport / BOM (provenir.governance.bom, provenir.governance.passport) is a signed (HMAC-SHA256), portable Bill-of-Materials of exactly what data, code, evals, and config produced a model — with compliance risk flags (unscanned_pii, contaminated_eval, unknown_license). Maps directly to EU AI Act Article 12 (tamper-proof audit trails + model lineage, enforced Aug 2, 2026).

The import provenir wrapper — the viral 3-line substrate

Drop provenance, trustworthy eval, reward-hacking detection, and a signed passport into any training loop:

import provenir

with provenir.track("my-run", dataset=train_ds) as run:
    for step, metrics in training_loop():
        run.log_step(metrics)
    run.record_eval("mmlu", score=0.71)

# run.manifest, run.flight_recorder, run.hacking_report, run.passport

See the Trust Layer guide for full examples.

---

Architecture

┌──────────────────────────────────────────────────────────────────┐
│                      provenir CLI / REST API                      │
└──────────────┬───────────────────────────────────┬───────────────┘
               │                                   │
   ┌───────────▼──────────┐           ┌────────────▼──────────────┐
   │      Orchestration    │           │       Data Pipeline        │
   │  ┌─────────────────┐ │           │  ┌──────────────────┐   │
   │  │  RunManifest     │ │           │  │  JsonlDataset      │   │
   │  │  (content-addr.) │ │           │  │  QualityScorer     │   │
   │  ├─────────────────┤ │           │  │  CurriculumSampler │   │
   │  │  RLAIFPipeline  │ │           │  │  RAGDataGenerator  │   │
   │  │  RLOrchestrator │ │           │  │  DataFlywheel      │   │
   │  │  DataFlywheel   │ │           │  │  PromptTemplates   │   │
   │  ├─────────────────┤ │           │  └──────────────────┘   │
   │  │  HyperparmSweep │ │           └───────────────────────────┘
   │  └─────────────────┘ │
   └───────────┬──────────┘
               │
   ┌───────────▼──────────────────────────────────────────────────┐
   │                 Trust Layer (new in v0.3)                      │
   │  RL Flight Recorder · Reward-hacking detector                 │
   │  Verifiable-reward environments (RLVR) · Contamination firewall│
   │  Canary vaults · Judge calibration · Deterministic replay      │
   │  Lineage DAG · Signed Model Passport / BOM                     │
   └───────────┬──────────────────────────────────────────────────┘
               │
   ┌───────────▼──────────────────────────────────────────────────┐
   │                       Training Backends                        │
   │   TRLBackend (SFT · DPO · LoRA · QLoRA)   StubBackend        │
   │   BackendSelector → verl · TRL · Unsloth adapters            │
   │   DistributedConfig (FSDP · DeepSpeed · DDP)                 │
   └───────────┬──────────────────────────────────────────────────┘
               │
   ┌───────────▼──────────────────────────────────────────────────┐
   │                      Evaluation Layer                          │
   │  ExactMatch · TokenF1 · BLEU-4 · ROUGE-L · Wilson CI         │
   │  RAGEvaluator (faithfulness · precision · relevance)          │
   │  BenchmarkEvaluator (MMLU · HellaSwag · ARC · GSM8K · …)     │
   │  LLM-as-Judge (StubJudge · CachedJudge · Anthropic · OpenAI) │
   │  EvalCallback (early stopping · regression gate)              │
   └───────────┬──────────────────────────────────────────────────┘
               │
   ┌───────────▼──────────────────────────────────────────────────┐
   │                      Governance Layer                          │
   │  AuditLogger · PIIScanner · SecretScanner · ModelCardGen      │
   │  SemanticDecontamination · AdapterRegistry · HubClient        │
   │  Model Passport / BOM (signed) · Lineage DAG                  │
   └──────────────────────────────────────────────────────────────┘

---

Installation

# Minimal — manifests, eval, governance, CLI
pip install provenir

# Full training stack (SFT + DPO + LoRA/QLoRA via TRL)
pip install "provenir[train]"

# Individual feature groups
pip install "provenir[serve]"            # REST API server
pip install "provenir[hub]"             # HuggingFace Hub push / pull
pip install "provenir[merge]"           # Adapter merging
pip install "provenir[semantic]"        # Semantic decontamination
pip install "provenir[benchmarks]"      # MMLU, HellaSwag, ARC, …
pip install "provenir[judge-anthropic]" # LLM-as-judge via Claude
pip install "provenir[judge-openai]"    # LLM-as-judge via GPT-4o

# Everything
pip install "provenir[all]"

---

Quick Start

1 — Train from a YAML config

# config.yaml
name: my-llama3-run
backend: trl
model_name_or_path: meta-llama/Llama-3.2-1B
seed: 42
max_steps: 500
batch_size: 4
peft:
  rank: 16
  alpha: 32
  target_modules: ["q_proj", "v_proj"]
  dropout: 0.05
observability_backend: wandb
observability_project: my-project

provenir train config.yaml --dataset data/train.jsonl

Every run produces a content-addressed manifest — a tamper-evident record of the exact config hash, dataset hash, git SHA, and provenance chain.

2 — Evaluate predictions

provenir eval predictions.jsonl --dataset data/eval.jsonl --metrics all

Outputs exact_match, token_f1, bleu4, rouge_l, plus Wilson 95% confidence intervals and a regression gate vs. the baseline.

3 — Verifiable-reward RL with a flight recorder

provenir rl --dataset data/train.jsonl \
  --algorithm grpo --verifier math

Runs the RLOrchestrator loop (rollout → verify → reward → flight recorder → hacking detector → eval gate) with a hack-resistant verifiable reward and full RL observability. --algorithm accepts grpo | dapo | gspo; --verifier accepts exact_answer | math | contains.

4 — Wrap any training run with import provenir

import provenir

with provenir.track("my-run", dataset=train_ds) as run:
    for step, metrics in training_loop():
        run.log_step(metrics)
    run.record_eval("mmlu", score=0.71)

# run.manifest, run.flight_recorder, run.hacking_report, run.passport

5 — Run the RLAIF pipeline (no human labels needed)

from provenir.train.rlaif import RLAIFConfig, RLAIFPipeline
from provenir.eval.judge import AnthropicJudge
from provenir.train.backends.trl import TRLBackend
from provenir.core.config import RunConfig
from provenir.data.dataset import JsonlDataset

pipeline = RLAIFPipeline(
    judge=AnthropicJudge(model="claude-haiku-4-5-20251001"),
    backend=TRLBackend(),
    base_config=RunConfig(model_name_or_path="meta-llama/Llama-3.2-1B"),
    rlaif_config=RLAIFConfig(n_iterations=3, responses_per_prompt=4),
)

train_ds = JsonlDataset.from_jsonl("data/train.jsonl")
eval_ds  = JsonlDataset.from_jsonl("data/eval.jsonl")

for it in pipeline.run(train_ds, eval_ds):
    print(f"Iteration {it.iteration}: {it.preference_count} preferences, "
          f"eval={it.eval_result}")

6 — Generate a RAG training dataset from documents

from provenir.data.rag_generator import RAGDataGenerator, RAGGeneratorConfig
from provenir.eval.judge import OpenAIJudge
from pathlib import Path

generator = RAGDataGenerator(
    config=RAGGeneratorConfig(questions_per_chunk=5, quality_filter_threshold=0.6),
    judge=OpenAIJudge(model="gpt-4o-mini"),
)

docs = [open(p).read() for p in Path("docs/").glob("*.txt")]
dataset = generator.generate_from_documents(docs)
dataset.save("data/rag_train.jsonl")
print(f"Generated {len(dataset.records)} Q&A pairs")

7 — Data flywheel: auto-augment from eval failures

from provenir.data.flywheel import DataFlywheel, FlywheelConfig
from provenir.eval.judge import AnthropicJudge

flywheel = DataFlywheel(
    config=FlywheelConfig(
        min_score_threshold=0.7,       # mine failures below this
        max_variants_per_failure=3,    # generate N augmented variants
        quality_filter_threshold=0.5,  # keep only quality variants
    ),
    judge=AnthropicJudge(),
)

augmented_dataset = flywheel.run(train_dataset, eval_dataset)

8 — Merge LoRA adapters

provenir merge adapter_v1/ adapter_v2/ --strategy slerp --output merged/

Or via the Python API:

from provenir.adapters.merging import MergeConfig, ModelMerger
from pathlib import Path

merger = ModelMerger()
result = merger.merge(
    [Path("adapter_v1"), Path("adapter_v2"), Path("adapter_v3")],
    config=MergeConfig(strategy="ties", density=0.5),
    output_dir=Path("merged_adapter"),
)
print(f"Merged to {result.output_path} using {result.strategy}")

9 — Run standard benchmarks

provenir benchmark --model-path ./my-adapter --benchmarks mmlu hellaswag arc_easy

from provenir.eval.benchmarks import BenchmarkConfig, BenchmarkEvaluator

evaluator = BenchmarkEvaluator()
results = evaluator.run_suite("./my-adapter", [
    BenchmarkConfig(benchmark="mmlu", num_fewshot=5),
    BenchmarkConfig(benchmark="hellaswag", num_fewshot=10),
    BenchmarkConfig(benchmark="gsm8k", num_fewshot=8),
])
for r in results:
    print(f"{r.benchmark}: {r.score:.3f} ({r.num_examples} examples)")

10 — Push to HuggingFace Hub

provenir hub push ./my-adapter --repo-id myorg/llama3-finetuned --private

11 — Start the REST API server

provenir serve --host 0.0.0.0 --port 8000

GET  /health              → {"status": "ok"}
POST /jobs/train          → Submit training job, returns manifest
GET  /manifests           → List all run IDs
GET  /manifests/{run_id}  → Retrieve manifest by ID
POST /eval                → Run evaluation on predictions
GET  /adapters            → List registered adapters
GET  /audit               → Full audit log (JSONL)

---

Core Concepts

Manifests — Reproducibility by Default

Every training run produces a RunManifest: a content-addressed record that captures the SHA-256 hash of the config, dataset, git commit, random seed, and full provenance chain. You can reproduce any run exactly:

provenir reproduce manifest_abc123.json --output reproduced_run/

Prompt Templates

Six built-in formats, all swappable at runtime:

Template	Use Case
alpaca	Alpaca-style instruction tuning
chatml	ChatML multi-turn (OpenAI format)
llama3	Llama 3 / Meta's official format
mistral	Mistral instruction format
phi3	Microsoft Phi-3 format
raw_completion	Plain prompt → completion (legacy)

from provenir.data.templates import TEMPLATE_REGISTRY

text = TEMPLATE_REGISTRY.format("llama3", {
    "system": "You are a helpful assistant.",
    "prompt": "Explain transformers.",
    "response": "Transformers are...",
})

RLAIF Pipeline — The Unique Moat

Provenir is the only open-source fine-tuning framework that combines automated preference generation, DPO training, evaluation-gated iteration, and regression detection into a single reproducible loop — without requiring human labellers.

┌─────────────────────────────────────────────────────────────┐
│                    RLAIF Iteration Loop                      │
│                                                             │
│  dataset ──► generate N response variants per prompt        │
│              ──► LLM judge: pairwise ranking                │
│              ──► (chosen, rejected) preference pairs        │
│              ──► DPO training                               │
│              ──► automatic evaluation                       │
│              ──► regression gate                            │
│              ──► iterate (up to n_iterations)               │
└─────────────────────────────────────────────────────────────┘

Each iteration is fully logged, manifested, and replayable. The pipeline terminates early if quality regresses beyond the configured threshold.

Verifiable-Reward RL — RLVR without gaming

For RL with verifiable rewards (RLVR), Provenir provides an OpenEnv-compatible Environment protocol and a library of hack-resistant verifiers. The RLOrchestrator fuses rollout, verification, reward-hacking detection, RL observability, and an eval gate into one loop over GRPO / DAPO / GSPO:

from provenir.environments import MathVerifier
from provenir.train.rl import RLOrchestrator, RLConfig

orchestrator = RLOrchestrator(
    config=RLConfig(algorithm="grpo"),   # grpo | dapo | gspo
    verifier=MathVerifier(),
)

report = orchestrator.run(train_ds, eval_ds)
# report.flight_recorder → per-step RL anomalies
# report.hacking_report  → reward-hacking findings

See the Trust Layer guide.

LLM-as-Judge

from provenir.eval.judge import (
    StubJudge,      # deterministic, no API calls — for CI
    CachedJudge,    # SHA-256 disk cache over any judge
    AnthropicJudge, # Claude
    OpenAIJudge,    # GPT-4o
)

# Wrap any judge with disk caching
judge = CachedJudge(AnthropicJudge(model="claude-haiku-4-5-20251001"))
pref = judge.score_pairwise("Explain gravity", response_a, response_b)
# → Preference(preferred='a', confidence=0.87, rationale='...')

scores = judge.score_rubric("Explain gravity", response, criteria=[
    "factual accuracy", "clarity", "conciseness"
])

To measure and remove judge bias, wrap judges with the calibration tooling:

from provenir.eval.judge_calibration import DebiasedJudge, EnsembleJudge

# Evaluate both orderings to remove position bias
debiased = DebiasedJudge(AnthropicJudge())

# Majority vote across multiple judges
ensemble = EnsembleJudge([AnthropicJudge(), OpenAIJudge(), StubJudge()])

Evaluation Layer

from provenir.eval.harness import MultiMetricEvaluator

evaluator = MultiMetricEvaluator()
result = evaluator.evaluate(dataset, predictions)
# result.metrics → {exact_match, token_f1, bleu4, rouge_l}
# result.metrics[*].mean, .ci_lower, .ci_upper (Wilson 95% CI)

RL Observability & Reward-Hacking Detection

from provenir.observability import FlightRecorder, RewardHackingDetector

recorder = FlightRecorder()
detector = RewardHackingDetector()

for step, metrics in rl_loop():
    recorder.log_step(metrics)     # KL, entropy, reward std, advantages, …

for anomaly in recorder.anomalies():
    print(anomaly.kind, anomaly.step, anomaly.detail)

report = detector.analyze(rollouts)
if report.is_hacking:
    print("Reward hacking:", report.findings)

Contamination Firewall

Detect and remove training samples that overlap with your evaluation set — preventing inflated benchmark numbers:

from provenir.eval.contamination import ContaminationChecker

checker = ContaminationChecker()          # 13-gram + embedding + exact, MinHash at scale
report  = checker.check(train_dataset, eval_dataset)
print(f"Overlap: {report.overlap_ratio:.1%} across {report.n_hits} records")

Legacy SemanticDecontaminationChecker remains available and falls back to substring matching when sentence-transformers is not installed.

Model Passport / BOM

from provenir.governance.passport import ModelPassport

passport = ModelPassport.build(run.manifest, key="team-signing-key")
passport.save("passport.json")           # signed HMAC-SHA256 Bill-of-Materials

# Later, verify integrity and inspect compliance risk flags
loaded = ModelPassport.load("passport.json")
assert loaded.verify(key="team-signing-key")
print(loaded.risk_flags)  # e.g. ["unscanned_pii", "contaminated_eval", "unknown_license"]

Semantic Decontamination

from provenir.data.quality import SemanticDecontaminationChecker

checker = SemanticDecontaminationChecker(threshold=0.90)
contaminated = checker.check(train_dataset, eval_references)
clean_dataset = train_dataset.filter(lambda r: r not in contaminated)

Falls back to substring matching when sentence-transformers is not installed.

Governance

from provenir.governance.audit import AuditLogger
from provenir.governance.pii import PIIScanner, PIIMasker

# Every training event is logged to an append-only JSONL audit trail
audit = AuditLogger("artifacts/audit")
audit.log("training_complete", actor="ci-bot", run_id=manifest.run_id)

# Scan for PII before training
scanner = PIIScanner()
report = scanner.scan(dataset)
if report.has_pii:
    masker = PIIMasker()
    clean_dataset = masker.mask(dataset)

---

CLI Reference

provenir train <config.yaml>              Run a training job
provenir eval <predictions.jsonl>         Evaluate predictions
provenir rl --dataset <data.jsonl>        Verifiable-reward RL + flight recorder
                                          (--algorithm grpo|dapo|gspo,
                                           --verifier exact_answer|math|contains)
provenir diagnose <reward_history...>     Loop Doctor: diagnose a stalled loop
                                          (--anomaly, --hacking-rate,
                                           --contamination-rate, --data-age-days)
provenir contamination <train> <eval>     Train/eval overlap check
provenir passport show|verify <p.json>    Inspect / verify a signed Model Passport
provenir audit                            Inspect the audit log
provenir model-card                       Generate a model card
provenir reproduce <manifest.json>        Reproduce a run exactly
provenir sweep <config.yaml>              Hyperparameter sweep
provenir compare <run_a> <run_b>          Side-by-side manifest diff
provenir benchmark --model-path <path>    Run MMLU / HellaSwag / ARC / …
provenir merge <a/> <b/> --strategy slerp Merge LoRA adapters
provenir hub push <adapter/> --repo-id …  Push to HuggingFace Hub
provenir hub pull <repo_id> --output …    Pull from HuggingFace Hub
provenir serve                            Start REST API server
provenir rlaif <config.yaml>              Run RLAIF pipeline

---

Configuration Reference

RunConfig (YAML or Python):

# Core
name: my-run
backend: trl              # trl | stub
seed: 42
deterministic: true
output_dir: artifacts/

# Model
model_name_or_path: meta-llama/Llama-3.2-1B
max_steps: 1000
batch_size: 8

# PEFT / LoRA
peft:
  rank: 16
  alpha: 32
  target_modules: ["q_proj", "v_proj", "k_proj", "o_proj"]
  dropout: 0.05
  load_in_4bit: false     # QLoRA
  load_in_8bit: false
  use_rslora: false       # rsLoRA scaling

# Distributed
distributed:
  strategy: fsdp          # fsdp | deepspeed | ddp | none
  num_gpus: 8
  num_nodes: 1
  deepspeed_stage: 3

# Observability
observability_backend: wandb    # wandb | mlflow | tensorboard | none
observability_project: provenir

---

Supported Benchmarks

Benchmark	Category	Shots
mmlu	Knowledge (57 subjects)	5-shot
hellaswag	Commonsense reasoning	10-shot
arc_easy	Science QA	0-shot
arc_challenge	Science QA (hard)	25-shot
truthfulqa_mc1	Truthfulness	0-shot
truthfulqa_mc2	Truthfulness	0-shot
winogrande	Commonsense NLI	5-shot
gsm8k	Grade-school math	8-shot
humaneval	Code generation	0-shot

Powered by EleutherAI lm-evaluation-harness.

---

Adapter Merging Algorithms

Strategy	Best For	Description
slerp	Two similar-task adapters	Spherical linear interpolation — preserves weight direction, blends magnitude smoothly
ties	Dissimilar-task adapters	Magnitude trimming + sign election — reduces task interference
dare	Many adapters	Random drop + rescale (DARE-TIES) — scales to 5+ adapters without magnitude collapse

---

REST API

Start the server:

provenir serve --host 0.0.0.0 --port 8000
# or
pip install "provenir[serve]"
uvicorn provenir.server.app:create_app --factory --port 8000

Example requests:

# Health check
curl http://localhost:8000/health

# Submit a training job
curl -X POST http://localhost:8000/jobs/train \
  -H "Content-Type: application/json" \
  -d '{"config": {"name": "api-run", "backend": "stub"}, "records": [...]}'

# List manifests
curl http://localhost:8000/manifests

# Get audit log
curl http://localhost:8000/audit

Interactive docs available at http://localhost:8000/docs (Swagger UI).

---

Optional Dependency Groups

Group	Install	Unlocks
train	pip install "provenir[train]"	SFT, DPO, LoRA, QLoRA via TRL
distributed	pip install "provenir[distributed]"	FSDP, DeepSpeed multi-GPU
serve	pip install "provenir[serve]"	REST API server (FastAPI + uvicorn)
hub	pip install "provenir[hub]"	HuggingFace Hub push / pull
merge	pip install "provenir[merge]"	SLERP / TIES / DARE adapter merging
semantic	pip install "provenir[semantic]"	Embedding-based decontamination
benchmarks	pip install "provenir[benchmarks]"	MMLU, HellaSwag, ARC, GSM8K, …
judge-anthropic	pip install "provenir[judge-anthropic]"	LLM-as-judge via Claude
judge-openai	pip install "provenir[judge-openai]"	LLM-as-judge via GPT-4o
all	pip install "provenir[all]"	Everything

All optional features degrade gracefully when their packages are absent — stub implementations run without error so pipelines still work in CPU-only or sandboxed environments.

---

Package Layout

src/provenir/
├── core/           RunManifest, RunConfig, DistributedConfig
├── data/           JsonlDataset, QualityScorer, CurriculumSampler,
│                   SemanticDecontaminationChecker, PromptTemplates (6 formats),
│                   DataFlywheel, RAGDataGenerator
├── environments/   Verifiable-reward RLVR verifiers + OpenEnv-compatible
│                   Environment protocol (ExactAnswer, Math, RegexFormat,
│                   JSONSchema, ToolCall, Contains, Composite, Code +
│                   PythonSandbox)
├── observability/  RL Flight Recorder (KL / entropy / length / advantage /
│                   reward-std / gradient anomalies), RewardHackingDetector
├── provenance/     Deterministic replay: environment fingerprint,
│                   kernel-determinism flags, lineage DAG, ReplayEngine
├── integrations/   `import provenir` wrapper — provenir.track(...) run context
├── train/          Trainer, TRLBackend (SFT/DPO/LoRA/QLoRA),
│   ├── rl.py       RLOrchestrator + GRPO / DAPO / GSPO configs
│   ├── rl_eval_gate.py  Fused contamination + regression + hacking loop guard
│   ├── backends/   PEFTConfig, TrainingObserver (W&B/MLflow/TensorBoard),
│   │   └── adapters.py  verl / TRL / Unsloth adapters + BackendSelector
│   └── ...         EvalCallback, DPOTrainer, GRPOTrainer, PPOTrainer,
│                   GridSweep, RandomSweep, RLAIFPipeline
├── eval/           ExactMatch, TokenF1, BLEU-4, ROUGE-L, Wilson CI,
│   ├── contamination.py  13-gram / embedding / exact + MinHash firewall
│   ├── canary.py         Canary-tagged private eval vaults
│   ├── judge_calibration.py  DebiasedJudge, EnsembleJudge, bias metrics
│   └── ...         RAGEvaluator, BenchmarkEvaluator,
│                   LLM-as-Judge (Stub/Cached/Anthropic/OpenAI)
├── adapters/       AdapterRegistry, HubClient, ModelMerger (SLERP/TIES/DARE)
├── rewards/        ExactMatch, Format, WeightedSum, Min, Max, Threshold, Clamped
├── governance/     AuditLogger, PIIScanner, PIIMasker, SecretScanner, ModelCardGen
│   ├── bom.py      Model Bill-of-Materials (data + code + evals + config)
│   └── passport.py Signed (HMAC-SHA256) Model Passport + risk flags
├── orchestrate/    CostEstimator
├── plugins/        PluginRegistry
├── server/         FastAPI REST server
└── cli/            CLI commands (incl. rl, contamination, passport)

---

Development

git clone https://github.com/anilatambharii/provenir
cd provenir
pip install -e ".[dev,all]"

# Quality gate (must pass before any PR)
python -m ruff check .
python -m mypy src
python -m pytest -q

The project enforces strict type-checking (mypy --strict), ruff linting (E, F, I rules, line-length 100), and comprehensive test coverage of all core logic across 1038 tests.

---

Project Status

Component	Status
Core manifests + reproducibility	Production-ready
Evaluation pipeline	Production-ready
PII / governance / audit	Production-ready
RL Flight Recorder + reward-hacking detection	Production-ready
Contamination firewall + canary vaults	Production-ready
Judge calibration	Production-ready
Deterministic replay + lineage DAG	Production-ready
Signed Model Passport / BOM	Production-ready
Verifiable-reward environments (RLVR)	Beta
RL orchestration (GRPO / DAPO / GSPO)	Beta
Backend adapters (verl / TRL / Unsloth)	Beta
TRL backend (SFT + DPO + LoRA)	Beta
RLAIF pipeline	Beta
REST API server	Beta
Data flywheel	Beta
RAG dataset generation	Beta
Adapter merging	Beta
HuggingFace Hub integration	Beta
Benchmark evaluation suite	Beta
Distributed training (FSDP / DeepSpeed)	Experimental
Web UI / dashboard	Roadmap

---

Roadmap

• v0.4 — Streaming training events via WebSocket; live eval + flight-recorder dashboard
• v0.5 — Multi-agent RLAIF (constitutional AI loop); reward model training
• v1.0 — Production SLAs; enterprise governance controls; HIPAA/SOC2 audit trail

---

Design Principles

1. Reproducibility is a first-class citizen. Every run produces a content-addressed manifest. Every manifest can reproduce the exact run that created it. There is no "it worked on my machine."

2. Evaluation before deployment. The regression gate, EvalCallback, RL eval gate, and RLAIF loop all prevent degraded models from advancing through the pipeline without explicit override.

3. Trust over throughput. RL observability, reward-hacking detection, verifiable rewards, contamination firewalls, and signed passports catch the failures that raw-throughput engines ignore.

4. Degrade gracefully. Every optional dependency — torch, TRL, anthropic, wandb, huggingface_hub — is conditionally imported. The full orchestration stack, manifest system, and evaluation pipeline work without a GPU.

5. Governance is not optional. PII scanning, secret detection, audit logging, model card generation, and the signed Model Passport are built into the core package, not plugins. Every production deployment should be auditable.

6. Orchestrate the winners. Provenir wraps verl / TRL / Unsloth via backend-agnostic adapters rather than reimplementing kernels. It adds the trust layer on top of the best engines.

---

Non-Goals

• Reimplementing training kernels or competing with Unsloth on raw single-GPU throughput
• Forking or replacing the Hugging Face / verl training stacks — Provenir wraps and orchestrates them
• Providing hosted SaaS, billing, or proprietary cloud services

---

License

Apache 2.0 — see LICENSE.

---

Citation

If you use Provenir in research, please cite:

@software{provenir2025,
  title  = {Provenir: The Trust Layer for Model Post-Training},
  year   = {2025},
  url    = {https://github.com/anilatambharii/provenir},
  note   = {v0.4.0}
}