moved models

Flake lock file updates:

• Updated input 'firefox-addons':
    'gitlab:rycee/nur-expressions/81e28f4?dir=pkgs/firefox-addons' (2026-03-20)
  → 'gitlab:rycee/nur-expressions/0581568?dir=pkgs/firefox-addons' (2026-04-17)
• Updated input 'home-manager':
    'github:nix-community/home-manager/9670de2' (2026-03-20)
  → 'github:nix-community/home-manager/565e534' (2026-04-17)
• Updated input 'nixos-hardware':
    'github:nixos/nixos-hardware/2d4b471' (2026-03-20)
  → 'github:nixos/nixos-hardware/c775c27' (2026-04-06)
• Updated input 'nixpkgs':
    'github:nixos/nixpkgs/b40629e' (2026-03-18)
  → 'github:nixos/nixpkgs/4bd9165' (2026-04-14)
• Updated input 'nixpkgs-master':
    'github:nixos/nixpkgs/8620c0b' (2026-03-21)
  → 'github:nixos/nixpkgs/025c852' (2026-04-17)
• Updated input 'sops-nix':
    'github:Mic92/sops-nix/29b6519' (2026-03-19)
  → 'github:Mic92/sops-nix/d4971dd' (2026-04-13)

.gitignore

@@ -169,7 +169,3 @@ test.*
 # Frontend build output
 frontend/dist/
 frontend/node_modules/
-
-# data dir for training, validation, and testing
-data/
-config.toml

.vscode/settings.json

@@ -40,7 +40,6 @@
     "cgroupdriver",
     "charliermarsh",
     "Checkpointing",
-    "cloudflared",
     "codellama",
     "codezombiech",
     "compactmode",

common/global/nix.nix

@@ -34,6 +34,7 @@ in
       warn-dirty = false;
       flake-registry = ""; # disable global flake registries
       connect-timeout = 10;
+      download-buffer-size = 536870912;
       fallback = true;
     };
 

common/optional/syncthing_base.nix

@@ -12,7 +12,7 @@
       brain.id = "SSCGIPI-IV3VYKB-TRNIJE3-COV4T2H-CDBER7F-I2CGHYA-NWOEUDU-3T5QAAN"; # cspell:disable-line
       ipad.id = "KI76T3X-SFUGV2L-VSNYTKR-TSIUV5L-SHWD3HE-GQRGRCN-GY4UFMD-CW6Z6AX"; # cspell:disable-line
       jeeves.id = "ICRHXZW-ECYJCUZ-I4CZ64R-3XRK7CG-LL2HAAK-FGOHD22-BQA4AI6-5OAL6AG"; # cspell:disable-line
-      phone.id = "TBRULKD-7DZPGGZ-F6LLB7J-MSO54AY-7KLPBIN-QOFK6PX-W2HBEWI-PHM2CQI"; # cspell:disable-line
+      phone.id = "JPVQKQW-CFXOJXT-Q5G5F3H-QIDHDRE-GKHPTQB-GXZUQSP-U7FR7F7-INP3AAH"; # cspell:disable-line
       rhapsody-in-green.id = "ASL3KC4-3XEN6PA-7BQBRKE-A7JXLI6-DJT43BY-Q4WPOER-7UALUAZ-VTPQ6Q4"; # cspell:disable-line
     };
   };

flake.lock

@@ -8,11 +8,11 @@
       },
       "locked": {
         "dir": "pkgs/firefox-addons",
-        "lastModified": 1773979456,
+        "lastModified": 1776398575,
-        "narHash": "sha256-9kBMJ5IvxqNlkkj/swmE8uK1Sc7TL/LIRUI958m7uBM=",
+        "narHash": "sha256-WArU6WOdWxzbzGqYk4w1Mucg+bw/SCl6MoSp+/cZMio=",
         "owner": "rycee",
         "repo": "nur-expressions",
-        "rev": "81e28f47ac18d9e89513929c77e711e657b64851",
+        "rev": "05815686caf4e3678f5aeb5fd36e567886ab0d30",
         "type": "gitlab"
       },
       "original": {
@@ -29,11 +29,11 @@
         ]
       },
       "locked": {
-        "lastModified": 1774007980,
+        "lastModified": 1776454077,
-        "narHash": "sha256-FOnZjElEI8pqqCvB6K/1JRHTE8o4rer8driivTpq2uo=",
+        "narHash": "sha256-7zSUFWsU0+jlD7WB3YAxQ84Z/iJurA5hKPm8EfEyGJk=",
         "owner": "nix-community",
         "repo": "home-manager",
-        "rev": "9670de2921812bc4e0452f6e3efd8c859696c183",
+        "rev": "565e5349208fe7d0831ef959103c9bafbeac0681",
         "type": "github"
       },
       "original": {
@@ -44,11 +44,11 @@
     },
     "nixos-hardware": {
       "locked": {
-        "lastModified": 1774018263,
+        "lastModified": 1775490113,
-        "narHash": "sha256-HHYEwK1A22aSaxv2ibhMMkKvrDGKGlA/qObG4smrSqc=",
+        "narHash": "sha256-2ZBhDNZZwYkRmefK5XLOusCJHnoeKkoN95hoSGgMxWM=",
         "owner": "nixos",
         "repo": "nixos-hardware",
-        "rev": "2d4b4717b2534fad5c715968c1cece04a172b365",
+        "rev": "c775c2772ba56e906cbeb4e0b2db19079ef11ff7",
         "type": "github"
       },
       "original": {
@@ -60,11 +60,11 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1773821835,
+        "lastModified": 1776169885,
-        "narHash": "sha256-TJ3lSQtW0E2JrznGVm8hOQGVpXjJyXY2guAxku2O9A4=",
+        "narHash": "sha256-l/iNYDZ4bGOAFQY2q8y5OAfBBtrDAaPuRQqWaFHVRXM=",
         "owner": "nixos",
         "repo": "nixpkgs",
-        "rev": "b40629efe5d6ec48dd1efba650c797ddbd39ace0",
+        "rev": "4bd9165a9165d7b5e33ae57f3eecbcb28fb231c9",
         "type": "github"
       },
       "original": {
@@ -76,11 +76,11 @@
     },
     "nixpkgs-master": {
       "locked": {
-        "lastModified": 1774051532,
+        "lastModified": 1776469842,
-        "narHash": "sha256-d3CGMweyYIcPuTj5BKq+1Lx4zwlgL31nVtN647tOZKo=",
+        "narHash": "sha256-sqzM6PKMQoGk8Sl+uv2sbP1qiS2SPQhA2yn5zgZINMc=",
         "owner": "nixos",
         "repo": "nixpkgs",
-        "rev": "8620c0b5cc8fbe76502442181be1d0514bc3a1b7",
+        "rev": "025c852a89be820b3117f604c8ace42e9b4caa08",
         "type": "github"
       },
       "original": {
@@ -125,11 +125,11 @@
         ]
       },
       "locked": {
-        "lastModified": 1773889674,
+        "lastModified": 1776119890,
-        "narHash": "sha256-+ycaiVAk3MEshJTg35cBTUa0MizGiS+bgpYw/f8ohkg=",
+        "narHash": "sha256-Zm6bxLNnEOYuS/SzrAGsYuXSwk3cbkRQZY0fJnk8a5M=",
         "owner": "Mic92",
         "repo": "sops-nix",
-        "rev": "29b6519f3e0780452bca0ac0be4584f04ac16cc5",
+        "rev": "d4971dd58c6627bfee52a1ad4237637c0a2fb0cd",
         "type": "github"
       },
       "original": {

overlays/default.nix

@@ -23,8 +23,8 @@
         apscheduler
         fastapi
         fastapi-cli
+        faster-whisper
         httpx
-        huggingface-hub
         mypy
         orjson
         polars
@@ -42,7 +42,6 @@
         sqlalchemy
         tenacity
         textual
-        tiktoken
         tinytuya
         typer
         websockets

pyproject.toml

@@ -12,7 +12,6 @@ dependencies = [
     "alembic",
     "apprise",
     "apscheduler",
-    "huggingface-hub",
     "httpx",
     "python-multipart",
     "polars",
@@ -27,11 +26,7 @@ dependencies = [
 [project.scripts]
 database = "python.database_cli:app"
 van-inventory = "python.van_inventory.main:serve"
-prompt-bench = "python.prompt_bench.main:cli"
+whisper-transcribe = "python.tools.whisper.transcribe:main"
-prompt-bench-download = "python.prompt_bench.downloader:cli"
-finetune = "python.prompt_bench.finetune:cli"
-finetune-container = "python.prompt_bench.finetune_container:cli"
-build-finetune-dataset = "python.prompt_bench.build_finetune_dataset:cli"
 
 [dependency-groups]
 dev = [
@@ -56,6 +51,7 @@ lint.ignore = [
     "COM812", # (TEMP) conflicts when used with the formatter
     "ISC001", # (TEMP) conflicts when used with the formatter
     "S603",   # (PERM) This is known to cause a false positive
+    "S607",   # (PERM) This is becoming a consistent annoyance
 ]
 
 [tool.ruff.lint.per-file-ignores]
@@ -84,14 +80,7 @@ lint.ignore = [
 "python/congress_tracker/**" = [
     "TC003", # (perm) this creates issues because sqlalchemy uses these at runtime
 ]
-"python/eval_warnings/**" = [
+
-    "S607", # (perm) gh and git are expected on PATH in the runner environment
-]
-"python/prompt_bench/**" = [
-    "FBT002",  # (perm) typer requires boolean defaults for --flag/--no-flag options
-    "PLR0913", # (perm) typer CLIs naturally have many parameters
-    "S607",    # (perm) docker and nvidia-smi are expected on PATH
-]
 "python/alembic/**" = [
     "INP001", # (perm) this creates LSP issues for alembic
 ]

python/prompt_bench/Dockerfile.finetune

@@ -1,25 +0,0 @@
-# Unsloth fine-tuning container for Qwen 3.5 4B on RTX 3090.
-#
-# Build:
-#   docker build -f python/prompt_bench/Dockerfile.finetune -t bill-finetune .
-#
-# Run:
-#   docker run --rm --device=nvidia.com/gpu=all --ipc=host \
-#     -v $(pwd)/output:/workspace/output \
-#     -v $(pwd)/output/finetune_dataset.jsonl:/workspace/dataset.jsonl:ro \
-#     -v /zfs/models/hf:/models \
-#     bill-finetune \
-#     --dataset /workspace/dataset.jsonl \
-#     --output-dir /workspace/output/qwen-bill-summarizer
-
-FROM ghcr.io/unslothai/unsloth:latest
-
-RUN pip install --no-cache-dir typer
-
-WORKDIR /workspace
-COPY python/prompt_bench/finetune.py python/prompt_bench/finetune.py
-COPY python/prompt_bench/summarization_prompts.py python/prompt_bench/summarization_prompts.py
-COPY python/prompt_bench/__init__.py python/prompt_bench/__init__.py
-COPY python/__init__.py python/__init__.py
-
-ENTRYPOINT ["python", "-m", "python.prompt_bench.finetune"]

python/prompt_bench/__init__.py

@@ -1 +0,0 @@
-"""Prompt benchmarking system for evaluating LLMs via vLLM."""

python/prompt_bench/batch_bill_summarizer.py

@@ -1,233 +0,0 @@
-"""Submit an OpenAI Batch API bill-summarization job over compressed text.
-
-Reads the first N bills from a CSV with a `text_content` column, compresses
-each via `bill_token_compression.compress_bill_text`, builds a JSONL file of
-summarization requests, and submits it as an asynchronous Batch API job
-against `/v1/chat/completions`. Also writes a CSV of per-bill pre/post-
-compression token counts.
-"""
-
-from __future__ import annotations
-
-import csv
-import json
-import logging
-import re
-import sys
-from os import getenv
-from pathlib import Path
-from typing import Annotated
-
-import httpx
-import typer
-from tiktoken import Encoding, get_encoding
-
-from python.prompt_bench.bill_token_compression import compress_bill_text
-from python.prompt_bench.summarization_prompts import SUMMARIZATION_SYSTEM_PROMPT, SUMMARIZATION_USER_TEMPLATE
-
-logger = logging.getLogger(__name__)
-
-OPENAI_API_BASE = "https://api.openai.com/v1"
-
-
-def load_bills(csv_path: Path, count: int = 0) -> list[tuple[str, str]]:
-    """Return (bill_id, text_content) tuples with non-empty text.
-
-    If `count` is 0 or negative, all rows are returned.
-    """
-    csv.field_size_limit(sys.maxsize)
-    bills: list[tuple[str, str]] = []
-    with csv_path.open(newline="", encoding="utf-8") as handle:
-        reader = csv.DictReader(handle)
-        for row in reader:
-            text_content = (row.get("text_content") or "").strip()
-            if not text_content:
-                continue
-            bill_id = row.get("bill_id") or row.get("id") or f"row-{len(bills)}"
-            version_code = row.get("version_code") or ""
-            unique_id = f"{bill_id}-{version_code}" if version_code else bill_id
-            bills.append((unique_id, text_content))
-            if count > 0 and len(bills) >= count:
-                break
-    return bills
-
-
-def safe_filename(value: str) -> str:
-    """Make a string safe for use as a filename or batch custom_id."""
-    return re.sub(r"[^A-Za-z0-9._-]+", "_", value).strip("_") or "unnamed"
-
-
-def build_request(custom_id: str, model: str, bill_text: str) -> dict:
-    """Build one OpenAI batch request line."""
-    return {
-        "custom_id": custom_id,
-        "method": "POST",
-        "url": "/v1/chat/completions",
-        "body": {
-            "model": model,
-            "messages": [
-                {"role": "system", "content": SUMMARIZATION_SYSTEM_PROMPT},
-                {"role": "user", "content": SUMMARIZATION_USER_TEMPLATE.format(text_content=bill_text)},
-            ],
-        },
-    }
-
-
-def write_jsonl(path: Path, lines: list[dict]) -> None:
-    """Write a list of dicts as JSONL."""
-    with path.open("w", encoding="utf-8") as handle:
-        for line in lines:
-            handle.write(json.dumps(line, ensure_ascii=False))
-            handle.write("\n")
-
-
-def upload_file(client: httpx.Client, path: Path) -> str:
-    """Upload a JSONL file to the OpenAI Files API and return its file id."""
-    with path.open("rb") as handle:
-        response = client.post(
-            f"{OPENAI_API_BASE}/files",
-            files={"file": (path.name, handle, "application/jsonl")},
-            data={"purpose": "batch"},
-        )
-    response.raise_for_status()
-    return response.json()["id"]
-
-
-def prepare_requests(
-    bills: list[tuple[str, str]],
-    *,
-    model: str,
-    encoder: Encoding,
-) -> tuple[list[dict], list[dict]]:
-    """Build (request_lines, token_rows) from bills.
-
-    Each bill is compressed before being turned into a request line.
-    Each `token_rows` entry has chars + token counts for one bill so the caller
-    can write a per-bill CSV.
-    """
-    request_lines: list[dict] = []
-    token_rows: list[dict] = []
-    for bill_id, text_content in bills:
-        raw_token_count = len(encoder.encode(text_content))
-        compressed_text = compress_bill_text(text_content)
-        compressed_token_count = len(encoder.encode(compressed_text))
-        token_rows.append(
-            {
-                "bill_id": bill_id,
-                "raw_chars": len(text_content),
-                "compressed_chars": len(compressed_text),
-                "raw_tokens": raw_token_count,
-                "compressed_tokens": compressed_token_count,
-                "token_ratio": (compressed_token_count / raw_token_count) if raw_token_count else None,
-            },
-        )
-        safe_id = safe_filename(bill_id)
-        request_lines.append(build_request(safe_id, model, compressed_text))
-    return request_lines, token_rows
-
-
-def write_token_csv(path: Path, token_rows: list[dict]) -> tuple[int, int]:
-    """Write per-bill token counts to CSV. Returns (raw_total, compressed_total)."""
-    with path.open("w", newline="", encoding="utf-8") as handle:
-        writer = csv.DictWriter(
-            handle,
-            fieldnames=["bill_id", "raw_chars", "compressed_chars", "raw_tokens", "compressed_tokens", "token_ratio"],
-        )
-        writer.writeheader()
-        writer.writerows(token_rows)
-    raw_total = sum(row["raw_tokens"] for row in token_rows)
-    compressed_total = sum(row["compressed_tokens"] for row in token_rows)
-    return raw_total, compressed_total
-
-
-def create_batch(client: httpx.Client, input_file_id: str, description: str) -> dict:
-    """Create a batch job and return its full response payload."""
-    response = client.post(
-        f"{OPENAI_API_BASE}/batches",
-        json={
-            "input_file_id": input_file_id,
-            "endpoint": "/v1/chat/completions",
-            "completion_window": "24h",
-            "metadata": {"description": description},
-        },
-    )
-    response.raise_for_status()
-    return response.json()
-
-
-def main(
-    csv_path: Annotated[Path, typer.Option("--csv", help="Bills CSV path")] = Path("bills.csv"),
-    output_dir: Annotated[Path, typer.Option("--output-dir", help="Where to write JSONL + metadata")] = Path(
-        "output/openai_batch",
-    ),
-    model: Annotated[str, typer.Option(help="OpenAI model id")] = "gpt-5-mini",
-    count: Annotated[int, typer.Option(help="Max bills to process, 0 = all")] = 0,
-    log_level: Annotated[str, typer.Option(help="Log level")] = "INFO",
-) -> None:
-    """Submit an OpenAI Batch job of compressed bill summaries."""
-    logging.basicConfig(level=log_level, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
-
-    api_key = getenv("CLOSEDAI_TOKEN") or getenv("OPENAI_API_KEY")
-    if not api_key:
-        message = "Neither CLOSEDAI_TOKEN nor OPENAI_API_KEY is set"
-        raise typer.BadParameter(message)
-    if not csv_path.is_file():
-        message = f"CSV not found: {csv_path}"
-        raise typer.BadParameter(message)
-
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    logger.info("Loading %d bills from %s", count, csv_path)
-    bills = load_bills(csv_path, count)
-    if len(bills) < count:
-        logger.warning("Only %d bills available (requested %d)", len(bills), count)
-
-    encoder = get_encoding("o200k_base")
-    request_lines, token_rows = prepare_requests(bills, model=model, encoder=encoder)
-
-    token_csv_path = output_dir / "token_counts.csv"
-    raw_tokens_total, compressed_tokens_total = write_token_csv(token_csv_path, token_rows)
-    logger.info(
-        "Token counts: raw=%d compressed=%d ratio=%.3f -> %s",
-        raw_tokens_total,
-        compressed_tokens_total,
-        (compressed_tokens_total / raw_tokens_total) if raw_tokens_total else 0.0,
-        token_csv_path,
-    )
-
-    jsonl_path = output_dir / "requests.jsonl"
-    write_jsonl(jsonl_path, request_lines)
-    logger.info("Wrote %s (%d bills)", jsonl_path, len(request_lines))
-
-    headers = {"Authorization": f"Bearer {api_key}"}
-    with httpx.Client(headers=headers, timeout=httpx.Timeout(300.0)) as client:
-        logger.info("Uploading JSONL")
-        file_id = upload_file(client, jsonl_path)
-        logger.info("Uploaded: %s", file_id)
-
-        logger.info("Creating batch")
-        batch = create_batch(client, file_id, f"compressed bill summaries x{len(request_lines)} ({model})")
-        logger.info("Batch created: %s", batch["id"])
-
-    metadata = {
-        "model": model,
-        "count": len(bills),
-        "jsonl": str(jsonl_path),
-        "input_file_id": file_id,
-        "batch_id": batch["id"],
-        "raw_tokens_total": raw_tokens_total,
-        "compressed_tokens_total": compressed_tokens_total,
-        "batch": batch,
-    }
-    metadata_path = output_dir / "batch.json"
-    metadata_path.write_text(json.dumps(metadata, indent=2))
-    logger.info("Wrote metadata to %s", metadata_path)
-
-
-def cli() -> None:
-    """Typer entry point."""
-    typer.run(main)
-
-
-if __name__ == "__main__":
-    cli()

python/prompt_bench/bill_token_compression.py

@@ -1,162 +0,0 @@
-"""Lossless-ish text compression for Congressional bill text."""
-
-from __future__ import annotations
-
-import re
-
-STATES = (
-    "Alabama",
-    "Alaska",
-    "Arizona",
-    "Arkansas",
-    "California",
-    "Colorado",
-    "Connecticut",
-    "Delaware",
-    "Florida",
-    "Georgia",
-    "Hawaii",
-    "Idaho",
-    "Illinois",
-    "Indiana",
-    "Iowa",
-    "Kansas",
-    "Kentucky",
-    "Louisiana",
-    "Maine",
-    "Maryland",
-    "Massachusetts",
-    "Michigan",
-    "Minnesota",
-    "Mississippi",
-    "Missouri",
-    "Montana",
-    "Nebraska",
-    "Nevada",
-    "New Hampshire",
-    "New Jersey",
-    "New Mexico",
-    "New York",
-    "North Carolina",
-    "North Dakota",
-    "Ohio",
-    "Oklahoma",
-    "Oregon",
-    "Pennsylvania",
-    "Rhode Island",
-    "South Carolina",
-    "South Dakota",
-    "Tennessee",
-    "Texas",
-    "Utah",
-    "Vermont",
-    "Virginia",
-    "Washington",
-    "West Virginia",
-    "Wisconsin",
-    "Wyoming",
-    "Puerto Rico",
-    "Guam",
-    "American Samoa",
-    "District of Columbia",
-    "US Virgin Islands",
-)
-STATE_PATTERNS = [(re.compile(re.escape(state), re.IGNORECASE), state) for state in STATES]
-
-
-def normalize_state_names(text: str) -> str:
-    """Replace any casing of state names with title case."""
-    for pattern, replacement in STATE_PATTERNS:
-        text = pattern.sub(replacement, text)
-    return text
-
-
-def strip_number_commas(text: str) -> str:
-    """Remove commas from numeric thousands separators."""
-    return re.sub(r"(\d{1,3}(?:,\d{3})+)", lambda match: match.group().replace(",", ""), text)
-
-
-def strip_horizontal_rules(text: str) -> str:
-    """Remove ASCII horizontal-rule lines built from underscores, dashes, equals, or asterisks."""
-    return re.sub(r"^\s*[_\-=\*]{3,}\s*$", "", text, flags=re.MULTILINE)
-
-
-def collapse_double_dashes(text: str) -> str:
-    """Replace ``--`` em-dash stand-ins with a single space so they don't tokenize oddly."""
-    return text.replace("--", " ")
-
-
-def collapse_inline_whitespace(text: str) -> str:
-    """Collapse runs of horizontal whitespace (spaces, tabs) into a single space, leaving newlines intact."""
-    return re.sub(r"[^\S\n]+", " ", text)
-
-
-def collapse_blank_lines(text: str) -> str:
-    """Collapse three-or-more consecutive newlines down to a blank-line separator."""
-    return re.sub(r"\n{3,}", "\n\n", text)
-
-
-def trim_line_edges(text: str) -> str:
-    """Strip spaces immediately before and after newline characters on every line."""
-    text = re.sub(r" +\n", "\n", text)
-    return re.sub(r"\n +", "\n", text)
-
-
-def shorten_section_markers(text: str) -> str:
-    """Rewrite ``Sec. 12.`` style section headings as the more compact ``SEC 12``."""
-    return re.sub(r"(?i)sec\.\s*(\d+[a-zA-Z]?)\.", r"SEC \1", text)
-
-
-def unwrap_parens(text: str) -> str:
-    """Strip parentheses around short alphanumeric labels like ``(a)`` or ``(12)``."""
-    return re.sub(r"\(([a-zA-Z0-9]+)\)", r"\1", text)
-
-
-def strip_typeset_quotes(text: str) -> str:
-    """Remove the `` and '' typeset quote markers used in the GPO bill format."""
-    return text.replace("``", "").replace("''", "")
-
-
-def normalize_usc_acronym(text: str) -> str:
-    """Collapse ``U.S.C.`` to ``USC`` to save tokens on the common citation."""
-    return text.replace("U.S.C.", "USC")
-
-
-def normalize_us_acronym(text: str) -> str:
-    """Normalize the various ``U.S.``/``U. S.`` spellings to the bare ``US`` form."""
-    for acronym in ("U. S.", "u. s.", "U.S. ", "u.s. "):
-        text = text.replace(acronym, "US ")
-    return text
-
-
-def collapse_ellipses(text: str) -> str:
-    """Collapse runs of two-or-more periods (``...``, ``....``) down to a single period."""
-    return re.sub(r"\.{2,}", ".", text)
-
-
-COMPRESSION_STEPS = (
-    strip_horizontal_rules,
-    collapse_double_dashes,
-    collapse_inline_whitespace,
-    collapse_blank_lines,
-    trim_line_edges,
-    shorten_section_markers,
-    unwrap_parens,
-    strip_typeset_quotes,
-    normalize_usc_acronym,
-    normalize_us_acronym,
-    strip_number_commas,
-    collapse_ellipses,
-    normalize_state_names,
-)
-
-
-def compress_bill_text(text: str) -> str:
-    """Apply lossless-ish whitespace and boilerplate compression to bill text.
-
-    Runs every transform in :data:`COMPRESSION_STEPS` in order, then strips
-    leading/trailing whitespace from the final result.
-    """
-    for step in COMPRESSION_STEPS:
-        text = step(text)
-    return text.strip()

python/prompt_bench/compresion_test.py

@@ -1,236 +0,0 @@
-"""Run two interactive OpenAI chat-completion sweeps over bill text.
-
-Reads the first N bills from a CSV with a `text_content` column and sends two
-sweeps through `/v1/chat/completions` concurrently — one with the raw bill
-text, one with the compressed bill text. Each request's prompt is saved to
-disk alongside the OpenAI response id so the prompts and responses can be
-correlated later.
-"""
-
-from __future__ import annotations
-
-import csv
-import json
-import logging
-import re
-import sys
-import time
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from os import getenv
-from pathlib import Path
-from typing import Annotated
-
-import httpx
-import typer
-
-from python.prompt_bench.bill_token_compression import compress_bill_text
-from python.prompt_bench.summarization_prompts import SUMMARIZATION_SYSTEM_PROMPT, SUMMARIZATION_USER_TEMPLATE
-
-logger = logging.getLogger(__name__)
-
-OPENAI_API_BASE = "https://api.openai.com/v1"
-DEFAULT_MODEL = "gpt-5.4-mini"
-DEFAULT_COUNT = 100
-SEED = 42
-
-
-def load_bills(csv_path: Path, count: int) -> list[tuple[str, str]]:
-    """Return up to `count` (bill_id, text_content) tuples with non-empty text."""
-    csv.field_size_limit(sys.maxsize)
-    bills: list[tuple[str, str]] = []
-    with csv_path.open(newline="", encoding="utf-8") as handle:
-        reader = csv.DictReader(handle)
-        for row in reader:
-            text_content = (row.get("text_content") or "").strip()
-            if not text_content:
-                continue
-            bill_id = row.get("bill_id") or row.get("id") or f"row-{len(bills)}"
-            version_code = row.get("version_code") or ""
-            unique_id = f"{bill_id}-{version_code}" if version_code else bill_id
-            bills.append((unique_id, text_content))
-            if len(bills) >= count:
-                break
-    return bills
-
-
-def build_messages(bill_text: str) -> list[dict]:
-    """Return the system + user message pair for a bill."""
-    return [
-        {"role": "system", "content": SUMMARIZATION_SYSTEM_PROMPT},
-        {"role": "user", "content": SUMMARIZATION_USER_TEMPLATE.format(text_content=bill_text)},
-    ]
-
-
-def safe_filename(value: str) -> str:
-    """Make a string safe for use as a filename."""
-    return re.sub(r"[^A-Za-z0-9._-]+", "_", value).strip("_") or "unnamed"
-
-
-def run_one_request(
-    client: httpx.Client,
-    *,
-    bill_id: str,
-    label: str,
-    bill_text: str,
-    model: str,
-    output_path: Path,
-) -> tuple[bool, float, str | None]:
-    """Send one chat-completion request and persist prompt + response.
-
-    Returns (success, elapsed_seconds, response_id).
-    """
-    messages = build_messages(bill_text)
-    payload = {
-        "model": model,
-        "messages": messages,
-        "seed": SEED,
-    }
-    start = time.monotonic()
-    record: dict = {
-        "bill_id": bill_id,
-        "label": label,
-        "model": model,
-        "seed": SEED,
-        "input_chars": len(bill_text),
-        "messages": messages,
-    }
-    try:
-        response = client.post(f"{OPENAI_API_BASE}/chat/completions", json=payload)
-        response.raise_for_status()
-        body = response.json()
-    except httpx.HTTPStatusError as error:
-        elapsed = time.monotonic() - start
-        record["error"] = {
-            "status_code": error.response.status_code,
-            "body": error.response.text,
-            "elapsed_seconds": elapsed,
-        }
-        output_path.write_text(json.dumps(record, ensure_ascii=False, indent=2))
-        logger.exception("HTTP error for %s/%s after %.2fs", label, bill_id, elapsed)
-        return False, elapsed, None
-    except Exception as error:
-        elapsed = time.monotonic() - start
-        record["error"] = {"message": str(error), "elapsed_seconds": elapsed}
-        output_path.write_text(json.dumps(record, ensure_ascii=False, indent=2))
-        logger.exception("Failed: %s/%s after %.2fs", label, bill_id, elapsed)
-        return False, elapsed, None
-
-    elapsed = time.monotonic() - start
-    response_id = body.get("id")
-    record["response_id"] = response_id
-    record["elapsed_seconds"] = elapsed
-    record["usage"] = body.get("usage")
-    record["response"] = body
-    output_path.write_text(json.dumps(record, ensure_ascii=False, indent=2))
-    logger.info("Done: %s/%s id=%s in %.2fs", label, bill_id, response_id, elapsed)
-    return True, elapsed, response_id
-
-
-def main(
-    csv_path: Annotated[Path, typer.Option("--csv", help="Bills CSV path")] = Path("bills.csv"),
-    output_dir: Annotated[Path, typer.Option("--output-dir", help="Where to write per-request JSON")] = Path(
-        "output/openai_runs",
-    ),
-    model: Annotated[str, typer.Option(help="OpenAI model id")] = DEFAULT_MODEL,
-    count: Annotated[int, typer.Option(help="Number of bills per set")] = DEFAULT_COUNT,
-    concurrency: Annotated[int, typer.Option(help="Concurrent in-flight requests")] = 16,
-    log_level: Annotated[str, typer.Option(help="Log level")] = "INFO",
-) -> None:
-    """Run two interactive OpenAI sweeps (compressed + uncompressed) over bill text."""
-    logging.basicConfig(level=log_level, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
-
-    api_key = getenv("CLOSEDAI_TOKEN") or getenv("OPENAI_API_KEY")
-    if not api_key:
-        message = "Neither CLOSEDAI_TOKEN nor OPENAI_API_KEY is set"
-        raise typer.BadParameter(message)
-    if not csv_path.is_file():
-        message = f"CSV not found: {csv_path}"
-        raise typer.BadParameter(message)
-
-    compressed_dir = output_dir / "compressed"
-    uncompressed_dir = output_dir / "uncompressed"
-    compressed_dir.mkdir(parents=True, exist_ok=True)
-    uncompressed_dir.mkdir(parents=True, exist_ok=True)
-
-    logger.info("Loading %d bills from %s", count, csv_path)
-    bills = load_bills(csv_path, count)
-    if len(bills) < count:
-        logger.warning("Only %d bills available (requested %d)", len(bills), count)
-
-    tasks: list[tuple[str, str, str, Path]] = []
-    for bill_id, text_content in bills:
-        filename = f"{safe_filename(bill_id)}.json"
-        tasks.append((bill_id, "compressed", compress_bill_text(text_content), compressed_dir / filename))
-        tasks.append((bill_id, "uncompressed", text_content, uncompressed_dir / filename))
-
-    logger.info("Submitting %d requests at concurrency=%d", len(tasks), concurrency)
-
-    headers = {"Authorization": f"Bearer {api_key}"}
-    completed = 0
-    failed = 0
-    index: list[dict] = []
-    wall_start = time.monotonic()
-    with (
-        httpx.Client(headers=headers, timeout=httpx.Timeout(300.0)) as client,
-        ThreadPoolExecutor(
-            max_workers=concurrency,
-        ) as executor,
-    ):
-        future_to_task = {
-            executor.submit(
-                run_one_request,
-                client,
-                bill_id=bill_id,
-                label=label,
-                bill_text=bill_text,
-                model=model,
-                output_path=output_path,
-            ): (bill_id, label, output_path)
-            for bill_id, label, bill_text, output_path in tasks
-        }
-        for future in as_completed(future_to_task):
-            bill_id, label, output_path = future_to_task[future]
-            success, elapsed, response_id = future.result()
-            if success:
-                completed += 1
-            else:
-                failed += 1
-            index.append(
-                {
-                    "bill_id": bill_id,
-                    "label": label,
-                    "response_id": response_id,
-                    "elapsed_seconds": elapsed,
-                    "success": success,
-                    "path": str(output_path),
-                },
-            )
-    wall_elapsed = time.monotonic() - wall_start
-
-    summary = {
-        "model": model,
-        "count": len(bills),
-        "completed": completed,
-        "failed": failed,
-        "wall_seconds": wall_elapsed,
-        "concurrency": concurrency,
-        "results": index,
-    }
-    summary_path = output_dir / "summary.json"
-    summary_path.write_text(json.dumps(summary, indent=2))
-    logger.info(
-        "Done: completed=%d failed=%d wall=%.1fs summary=%s",
-        completed,
-        failed,
-        wall_elapsed,
-        summary_path,
-    )
-
-
-def cli() -> None:
-    """Typer entry point."""
-    typer.run(main)
-
-
-if __name__ == "__main__":
-    cli()

python/prompt_bench/containers/__init__.py

@@ -1 +0,0 @@
-"""Prompt benchmarking system for evaluating LLMs via vLLM."""

python/prompt_bench/containers/finetune.py

@@ -1,165 +0,0 @@
-"""Docker container lifecycle management for Unsloth fine-tuning."""
-
-from __future__ import annotations
-
-import logging
-import subprocess
-from pathlib import Path
-from typing import Annotated
-
-import typer
-
-from python.prompt_bench.containers.lib import check_gpu_free
-
-logger = logging.getLogger(__name__)
-
-CONTAINER_NAME = "bill-finetune"
-FINETUNE_IMAGE = "bill-finetune:latest"
-DOCKERFILE_PATH = "/home/richie/dotfiles/python/prompt_bench/Dockerfile.finetune"
-DEFAULT_HF_CACHE = Path("/zfs/models/hf")
-
-
-def build_image() -> None:
-    """Build the fine-tuning Docker image."""
-    logger.info("Building fine-tuning image: %s", FINETUNE_IMAGE)
-    result = subprocess.run(
-        ["docker", "build", "-f", DOCKERFILE_PATH, "-t", FINETUNE_IMAGE, "."],
-        text=True,
-        check=False,
-    )
-    if result.returncode != 0:
-        message = "Failed to build fine-tuning image"
-        raise RuntimeError(message)
-    logger.info("Image built: %s", FINETUNE_IMAGE)
-
-
-def start_finetune(
-    *,
-    dataset_path: Path,
-    output_dir: Path,
-    hf_cache: Path = DEFAULT_HF_CACHE,
-) -> None:
-    """Run the fine-tuning container.
-
-    Args:
-        dataset_path: Host path to the fine-tuning JSONL dataset.
-        output_dir: Host path where the trained model will be saved.
-        hf_cache: Host path to HuggingFace model cache (bind-mounted to avoid re-downloading).
-        validation_split: Fraction of data held out for validation.
-    """
-    dataset_path = dataset_path.resolve()
-    output_dir = output_dir.resolve()
-
-    if not dataset_path.is_file():
-        message = f"Dataset not found: {dataset_path}"
-        raise FileNotFoundError(message)
-
-    output_dir.mkdir(parents=True, exist_ok=True)
-    stop_finetune()
-
-    hf_cache = hf_cache.resolve()
-    hf_cache.mkdir(parents=True, exist_ok=True)
-
-    command = [
-        "docker",
-        "run",
-        "--name",
-        CONTAINER_NAME,
-        "--device=nvidia.com/gpu=all",
-        "--ipc=host",
-        "-v",
-        f"{hf_cache}:/root/.cache/huggingface",
-        "-v",
-        f"{output_dir}:/workspace/output/qwen-bill-summarizer",
-        "-v",
-        f"{dataset_path}:/workspace/dataset.jsonl:ro",
-        FINETUNE_IMAGE,
-        "--dataset",
-        "/workspace/dataset.jsonl",
-        "--output-dir",
-        "/workspace/output/qwen-bill-summarizer",
-    ]
-
-    logger.info("Starting fine-tuning container")
-    logger.info("  Dataset:    %s", dataset_path)
-    logger.info("  Output:     %s", output_dir)
-
-    result = subprocess.run(command, text=True, check=False)
-    if result.returncode != 0:
-        message = f"Fine-tuning container exited with code {result.returncode}"
-        raise RuntimeError(message)
-    logger.info("Fine-tuning complete. Model saved to %s", output_dir)
-
-
-def stop_finetune() -> None:
-    """Stop and remove the fine-tuning container."""
-    logger.info("Stopping fine-tuning container")
-    subprocess.run(["docker", "stop", CONTAINER_NAME], capture_output=True, check=False)
-    subprocess.run(["docker", "rm", "-f", CONTAINER_NAME], capture_output=True, check=False)
-
-
-def logs_finetune() -> str | None:
-    """Return recent logs from the fine-tuning container, or None if not running."""
-    result = subprocess.run(
-        ["docker", "logs", "--tail", "50", CONTAINER_NAME],
-        capture_output=True,
-        text=True,
-        check=False,
-    )
-    if result.returncode != 0:
-        return None
-    return result.stdout + result.stderr
-
-
-app = typer.Typer(help="Fine-tuning container management.")
-
-
-@app.command()
-def build() -> None:
-    """Build the fine-tuning Docker image."""
-    build_image()
-
-
-@app.command()
-def run(
-    dataset: Annotated[Path, typer.Option(help="Fine-tuning JSONL")] = Path(
-        "/home/richie/dotfiles/data/finetune_dataset.jsonl"
-    ),
-    output_dir: Annotated[Path, typer.Option(help="Where to save the trained model")] = Path(
-        "/home/richie/dotfiles/data/output/qwen-bill-summarizer",
-    ),
-    hf_cache: Annotated[Path, typer.Option(help="Host path to HuggingFace model cache")] = DEFAULT_HF_CACHE,
-    log_level: Annotated[str, typer.Option(help="Log level")] = "INFO",
-) -> None:
-    """Run fine-tuning inside a Docker container."""
-    logging.basicConfig(level=log_level, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
-    check_gpu_free()
-    start_finetune(
-        dataset_path=dataset,
-        output_dir=output_dir,
-        hf_cache=hf_cache,
-    )
-
-@app.command()
-def stop() -> None:
-    """Stop and remove the fine-tuning container."""
-    stop_finetune()
-
-
-@app.command()
-def logs() -> None:
-    """Show recent logs from the fine-tuning container."""
-    output = logs_finetune()
-    if output is None:
-        typer.echo("No running fine-tuning container found.")
-        raise typer.Exit(code=1)
-    typer.echo(output)
-
-
-def cli() -> None:
-    """Typer entry point."""
-    app()
-
-
-if __name__ == "__main__":
-    cli()

python/prompt_bench/containers/lib.py

@@ -1,23 +0,0 @@
-from __future__ import annotations
-
-import logging
-import subprocess
-
-logger = logging.getLogger(__name__)
-
-
-def check_gpu_free() -> None:
-    """Warn if GPU-heavy processes (e.g. Ollama) are running."""
-    result = subprocess.run(
-        ["nvidia-smi", "--query-compute-apps=pid,process_name", "--format=csv,noheader"],
-        capture_output=True,
-        text=True,
-        check=False,
-    )
-    if result.returncode != 0:
-        logger.warning("Could not query GPU processes: %s", result.stderr.strip())
-        return
-    processes = result.stdout.strip()
-    if processes:
-        logger.warning("GPU processes detected:\n%s", processes)
-        logger.warning("Consider stopping Ollama (sudo systemctl stop ollama) before benchmarking")

python/prompt_bench/containers/vllm.py

@@ -1,70 +0,0 @@
-"""Docker container lifecycle management for vLLM."""
-
-from __future__ import annotations
-
-import logging
-import subprocess
-
-logger = logging.getLogger(__name__)
-
-CONTAINER_NAME = "vllm-bench"
-VLLM_IMAGE = "vllm/vllm-openai:v0.19.0"
-
-
-def start_vllm(
-    *,
-    model: str,
-    port: int,
-    model_dir: str,
-    gpu_memory_utilization: float,
-) -> None:
-    """Start a vLLM container serving the given model.
-
-    Args:
-        model: HuggingFace model directory name (relative to model_dir).
-        port: Host port to bind.
-        model_dir: Host path containing HuggingFace model directories.
-        gpu_memory_utilization: Fraction of GPU memory to use (0-1).
-    """
-    command = [
-        "docker",
-        "run",
-        "-d",
-        "--name",
-        CONTAINER_NAME,
-        "--device=nvidia.com/gpu=all",
-        "--ipc=host",
-        "-v",
-        f"{model_dir}:/models",
-        "-p",
-        f"{port}:8000",
-        VLLM_IMAGE,
-        "--model",
-        f"/models/{model}",
-        "--served-model-name",
-        model,
-        "--gpu-memory-utilization",
-        str(gpu_memory_utilization),
-        "--max-model-len",
-        "4096",
-    ]
-    logger.info("Starting vLLM container with model: %s", model)
-    stop_vllm()
-    result = subprocess.run(command, capture_output=True, text=True, check=False)
-    if result.returncode != 0:
-        msg = f"Failed to start vLLM container: {result.stderr.strip()}"
-        raise RuntimeError(msg)
-    logger.info("vLLM container started: %s", result.stdout.strip()[:12])
-
-
-def stop_vllm() -> None:
-    """Stop and remove the vLLM benchmark container."""
-    logger.info("Stopping vLLM container")
-    subprocess.run(["docker", "stop", CONTAINER_NAME], capture_output=True, check=False)
-    subprocess.run(["docker", "rm", "-f", CONTAINER_NAME], capture_output=True, check=False)
-    subprocess.run(
-        ["docker", "network", "disconnect", "-f", "bridge", CONTAINER_NAME],
-        capture_output=True,
-        check=False,
-    )
-    logger.info("vLLM container stopped and removed")

python/prompt_bench/downloader.py

@@ -1,75 +0,0 @@
-"""HuggingFace model downloader."""
-
-from __future__ import annotations
-
-import logging
-from pathlib import Path
-from typing import Annotated
-
-import typer
-from huggingface_hub import snapshot_download
-
-from python.prompt_bench.models import BenchmarkConfig
-
-logger = logging.getLogger(__name__)
-
-
-def local_model_path(repo: str, model_dir: str) -> Path:
-    """Return the local directory path for a HuggingFace repo."""
-    return Path(model_dir) / repo
-
-
-def is_model_present(repo: str, model_dir: str) -> bool:
-    """Check if a model has already been downloaded."""
-    path = local_model_path(repo, model_dir)
-    return path.exists() and any(path.iterdir())
-
-
-def download_model(repo: str, model_dir: str) -> Path:
-    """Download a HuggingFace model to the local model directory.
-
-    Skips the download if the model directory already exists and contains files.
-    """
-    local_path = local_model_path(repo, model_dir)
-
-    if is_model_present(repo, model_dir):
-        logger.info("Model already exists: %s", local_path)
-        return local_path
-
-    logger.info("Downloading model: %s -> %s", repo, local_path)
-    snapshot_download(
-        repo_id=repo,
-        local_dir=str(local_path),
-    )
-    logger.info("Download complete: %s", repo)
-    return local_path
-
-
-def download_all(config: BenchmarkConfig) -> None:
-    """Download every model listed in the config, top to bottom."""
-    for repo in config.models:
-        download_model(repo, config.model_dir)
-
-
-def main(
-    config: Annotated[Path, typer.Option(help="Path to TOML config file")] = Path("bench.toml"),
-    log_level: Annotated[str, typer.Option(help="Log level")] = "INFO",
-) -> None:
-    """Download all models listed in the benchmark config."""
-    logging.basicConfig(level=log_level, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
-
-    if not config.is_file():
-        message = f"Config file does not exist: {config}"
-        raise typer.BadParameter(message)
-
-    benchmark_config = BenchmarkConfig.from_toml(config)
-    download_all(benchmark_config)
-
-
-def cli() -> None:
-    """Typer entry point."""
-    typer.run(main)
-
-
-if __name__ == "__main__":
-    cli()

python/prompt_bench/finetune.py

@@ -1,214 +0,0 @@
-"""Fine-tune Qwen 3.5 4B on bill summarization data using Unsloth.
-
-Loads a ChatML-style JSONL dataset (system/user/assistant messages),
-applies QLoRA with 4-bit quantization, and saves the merged model
-in HuggingFace format. Designed for a single RTX 3090 (24GB).
-
-Usage:
-    python -m python.prompt_bench.finetune \
-        --dataset output/finetune_dataset.jsonl \
-        --output-dir output/qwen-bill-summarizer
-"""
-
-from __future__ import annotations
-
-import json
-import logging
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Annotated
-
-import tomllib
-import typer
-from unsloth import FastLanguageModel
-from datasets import Dataset
-from transformers import TrainingArguments
-from trl import SFTTrainer
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class LoraConfig:
-    """LoRA adapter hyperparameters."""
-
-    rank: int
-    alpha: int
-    dropout: float
-    targets: list[str]
-
-
-@dataclass
-class TrainingConfig:
-    """Training loop hyperparameters."""
-
-    learning_rate: float
-    epochs: int
-    batch_size: int
-    gradient_accumulation: int
-    max_seq_length: int
-    warmup_ratio: float
-    weight_decay: float
-    logging_steps: int
-    save_steps: int
-
-
-@dataclass
-class FinetuneConfig:
-    """Top-level finetune configuration."""
-
-    base_model: str
-    lora: LoraConfig
-    training: TrainingConfig
-
-    @classmethod
-    def from_toml(cls, config_path: Path) -> FinetuneConfig:
-        """Load finetune config from a TOML file."""
-        raw = tomllib.loads(config_path.read_text())["finetune"]
-        return cls(
-            base_model=raw["base_model"],
-            lora=LoraConfig(**raw["lora"]),
-            training=TrainingConfig(**raw["training"]),
-        )
-
-
-def _messages_to_chatml(messages: list[dict]) -> str:
-    r"""Convert a message list to Qwen ChatML format.
-
-    Produces:
-        <|im_start|>system\n...\n<|im_end|>
-        <|im_start|>user\n...\n<|im_end|>
-        <|im_start|>assistant\n...\n<|im_end|>
-    """
-    parts = []
-    for message in messages:
-        role = message["role"]
-        content = message["content"]
-        parts.append(f"<|im_start|>{role}\n{content}<|im_end|>")
-    return "\n".join(parts)
-
-
-def load_dataset_from_jsonl(path: Path) -> Dataset:
-    """Load a ChatML JSONL file into a HuggingFace Dataset.
-
-    Each line must have {"messages": [{"role": ..., "content": ...}, ...]}.
-    Pre-formats into a `text` column with the Qwen ChatML template applied,
-    which SFTTrainer consumes directly.
-    """
-    records = []
-    with path.open(encoding="utf-8") as handle:
-        for raw_line in handle:
-            stripped = raw_line.strip()
-            if stripped:
-                entry = json.loads(stripped)
-                records.append({"text": _messages_to_chatml(entry["messages"])})
-    logger.info("Loaded %d examples from %s", len(records), path)
-    return Dataset.from_list(records)
-
-
-def main(
-    dataset_path: Annotated[Path, typer.Option("--dataset", help="Fine-tuning JSONL")] = Path(
-        "output/finetune_dataset.jsonl",
-    ),
-    validation_split: Annotated[float, typer.Option("--val-split", help="Fraction held out for validation")] = 0.1,
-    output_dir: Annotated[Path, typer.Option("--output-dir", help="Where to save the merged model")] = Path(
-        "output/qwen-bill-summarizer",
-    ),
-    config_path: Annotated[
-        Path,
-        typer.Option("--config", help="TOML config file"),
-    ] = Path(__file__).parent / "config.toml",
-    save_gguf: Annotated[bool, typer.Option("--save-gguf/--no-save-gguf", help="Also save GGUF")] = False,
-) -> None:
-    """Fine-tune Qwen 3.5 4B on bill summarization with Unsloth + QLoRA."""
-    logging.basicConfig(level="INFO", format="%(asctime)s %(levelname)s %(name)s: %(message)s")
-
-    if not dataset_path.is_file():
-        message = f"Dataset not found: {dataset_path}"
-        raise typer.BadParameter(message)
-
-    config = FinetuneConfig.from_toml(config_path)
-
-    logger.info("Loading base model: %s", config.base_model)
-    model, tokenizer = FastLanguageModel.from_pretrained(
-        model_name=config.base_model,
-        max_seq_length=config.training.max_seq_length,
-        load_in_4bit=True,
-        dtype=None,
-    )
-
-    logger.info("Applying LoRA (rank=%d, alpha=%d)", config.lora.rank, config.lora.alpha)
-    model = FastLanguageModel.get_peft_model(
-        model,
-        r=config.lora.rank,
-        lora_alpha=config.lora.alpha,
-        lora_dropout=config.lora.dropout,
-        target_modules=config.lora.targets,
-        bias="none",
-        use_gradient_checkpointing="unsloth",
-        random_state=42,
-    )
-
-    full_dataset = load_dataset_from_jsonl(dataset_path)
-    split = full_dataset.train_test_split(test_size=validation_split, seed=42)
-    train_dataset = split["train"]
-    validation_dataset = split["test"]
-    logger.info("Split: %d train, %d validation", len(train_dataset), len(validation_dataset))
-    training_args = TrainingArguments(
-        output_dir=str(output_dir / "checkpoints"),
-        num_train_epochs=config.training.epochs,
-        per_device_train_batch_size=config.training.batch_size,
-        gradient_accumulation_steps=config.training.gradient_accumulation,
-        learning_rate=config.training.learning_rate,
-        warmup_ratio=config.training.warmup_ratio,
-        weight_decay=config.training.weight_decay,
-        lr_scheduler_type="cosine",
-        logging_steps=config.training.logging_steps,
-        save_steps=config.training.save_steps,
-        save_total_limit=3,
-        eval_strategy="steps",
-        eval_steps=config.training.save_steps,
-        load_best_model_at_end=True,
-        bf16=True,
-        optim="adamw_8bit",
-        seed=42,
-        report_to="none",
-    )
-
-    trainer = SFTTrainer(
-        model=model,
-        tokenizer=tokenizer,
-        train_dataset=train_dataset,
-        eval_dataset=validation_dataset,
-        args=training_args,
-        max_seq_length=config.training.max_seq_length,
-        packing=True,
-    )
-
-    logger.info(
-        "Starting training: %d train, %d val, %d epochs",
-        len(train_dataset),
-        len(validation_dataset),
-        config.training.epochs,
-    )
-    trainer.train()
-
-    merged_path = str(output_dir / "merged")
-    logger.info("Saving merged model to %s", merged_path)
-    model.save_pretrained_merged(merged_path, tokenizer, save_method="merged_16bit")
-
-    if save_gguf:
-        gguf_path = str(output_dir / "gguf")
-        logger.info("Saving GGUF to %s", gguf_path)
-        model.save_pretrained_gguf(gguf_path, tokenizer, quantization_method="q4_k_m")
-
-    logger.info("Done! Model saved to %s", output_dir)
-
-
-def cli() -> None:
-    """Typer entry point."""
-    typer.run(main)
-
-
-if __name__ == "__main__":
-    cli()

python/prompt_bench/main.py

@@ -1,215 +0,0 @@
-"""CLI entry point for the prompt benchmarking system."""
-
-from __future__ import annotations
-
-import json
-import logging
-import time
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from pathlib import Path
-from typing import Annotated
-
-import typer
-
-from python.prompt_bench.containers.lib import check_gpu_free
-from python.prompt_bench.containers.vllm import start_vllm, stop_vllm
-from python.prompt_bench.downloader import is_model_present
-from python.prompt_bench.models import BenchmarkConfig
-from python.prompt_bench.vllm_client import VLLMClient
-
-logger = logging.getLogger(__name__)
-
-
-def discover_prompts(input_dir: Path) -> list[Path]:
-    """Find all .txt files in the input directory."""
-    prompts = list(input_dir.glob("*.txt"))
-    if not prompts:
-        message = f"No .txt files found in {input_dir}"
-        raise FileNotFoundError(message)
-    return prompts
-
-
-def _run_prompt(
-    client: VLLMClient,
-    prompt_path: Path,
-    *,
-    repo: str,
-    model_dir_name: str,
-    model_output: Path,
-    temperature: float,
-) -> tuple[bool, float]:
-    """Run a single prompt. Returns (success, elapsed_seconds)."""
-    filename = prompt_path.name
-    output_path = model_output / filename
-    start = time.monotonic()
-    try:
-        prompt_text = prompt_path.read_text()
-        response = client.complete(prompt_text, model_dir_name, temperature=temperature)
-        output_path.write_text(response)
-        elapsed = time.monotonic() - start
-        logger.info("Completed: %s / %s in %.2fs", repo, filename, elapsed)
-    except Exception:
-        elapsed = time.monotonic() - start
-        error_path = model_output / f"{filename}.error"
-        logger.exception("Failed: %s / %s after %.2fs", repo, filename, elapsed)
-        error_path.write_text(f"Error processing {filename}")
-        return False, elapsed
-    return True, elapsed
-
-
-def benchmark_model(
-    client: VLLMClient,
-    prompts: list[Path],
-    *,
-    repo: str,
-    model_dir_name: str,
-    model_output: Path,
-    temperature: float,
-    concurrency: int,
-) -> tuple[int, int]:
-    """Run all prompts against a single model in parallel.
-
-    vLLM batches concurrent requests internally, so submitting many at once is
-    significantly faster than running them serially.
-    """
-    pending = [prompt for prompt in prompts if not (model_output / prompt.name).exists()]
-    skipped = len(prompts) - len(pending)
-    if skipped:
-        logger.info("Skipping %d prompts with existing output for %s", skipped, repo)
-
-    if not pending:
-        logger.info("Nothing to do for %s", repo)
-        return 0, 0
-
-    completed = 0
-    failed = 0
-    latencies: list[float] = []
-
-    wall_start = time.monotonic()
-    with ThreadPoolExecutor(max_workers=concurrency) as executor:
-        futures = [
-            executor.submit(
-                _run_prompt,
-                client,
-                prompt_path,
-                repo=repo,
-                model_dir_name=model_dir_name,
-                model_output=model_output,
-                temperature=temperature,
-            )
-            for prompt_path in pending
-        ]
-        for future in as_completed(futures):
-            success, elapsed = future.result()
-            latencies.append(elapsed)
-            if success:
-                completed += 1
-            else:
-                failed += 1
-    wall_elapsed = time.monotonic() - wall_start
-
-    attempted = completed + failed
-    avg_latency = sum(latencies) / attempted
-    throughput = attempted / wall_elapsed if wall_elapsed > 0 else 0.0
-    timing = {
-        "repo": repo,
-        "wall_seconds": wall_elapsed,
-        "attempted": attempted,
-        "completed": completed,
-        "failed": failed,
-        "avg_latency_seconds": avg_latency,
-        "throughput_prompts_per_second": throughput,
-        "concurrency": concurrency,
-    }
-    timing_path = model_output / "_timing.json"
-    timing_path.write_text(json.dumps(timing, indent=2))
-
-    return completed, failed
-
-
-def run_benchmark(
-    config: BenchmarkConfig,
-    input_dir: Path,
-    output_dir: Path,
-) -> None:
-    """Execute the benchmark across all models and prompts."""
-    prompts = discover_prompts(input_dir)
-    logger.info("Found %d prompts in %s", len(prompts), input_dir)
-
-    check_gpu_free()
-
-    total_completed = 0
-    total_failed = 0
-
-    for repo in config.models:
-        if not is_model_present(repo, config.model_dir):
-            logger.warning("Skipping (not downloaded): %s", repo)
-            continue
-
-        model_output = output_dir / repo
-        model_output.mkdir(parents=True, exist_ok=True)
-
-        logger.info("=== Benchmarking model: %s ===", repo)
-
-        stop_vllm()
-        try:
-            start_vllm(
-                model=repo,
-                port=config.port,
-                model_dir=config.model_dir,
-                gpu_memory_utilization=config.gpu_memory_utilization,
-            )
-        except RuntimeError:
-            logger.exception("Failed to start vLLM for %s, skipping", repo)
-            continue
-        logger.info("vLLM started for %s", repo)
-        try:
-            with VLLMClient(port=config.port, timeout=config.timeout) as client:
-                client.wait_ready(max_wait=config.vllm_startup_timeout)
-                completed, failed = benchmark_model(
-                    client,
-                    prompts,
-                    repo=repo,
-                    model_dir_name=repo,
-                    model_output=model_output,
-                    temperature=config.temperature,
-                    concurrency=config.concurrency,
-                )
-                total_completed += completed
-                total_failed += failed
-        finally:
-            stop_vllm()
-
-    logger.info("=== Benchmark complete ===")
-    logger.info("Completed: %d | Failed: %d", total_completed, total_failed)
-
-
-def main(
-    input_dir: Annotated[Path, typer.Argument(help="Directory containing input .txt prompt files")],
-    config: Annotated[Path, typer.Option(help="Path to TOML config file")] = Path("bench.toml"),
-    output_dir: Annotated[Path, typer.Option(help="Output directory for results")] = Path("output"),
-    log_level: Annotated[str, typer.Option(help="Log level")] = "INFO",
-) -> None:
-    """Run prompts through multiple LLMs via vLLM and save results."""
-    logging.basicConfig(level=log_level, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
-
-    if not input_dir.is_dir():
-        message = f"Input directory does not exist: {input_dir}"
-        raise typer.BadParameter(message)
-    if not config.is_file():
-        message = f"Config file does not exist: {config}"
-        raise typer.BadParameter(message)
-
-    benchmark_config = BenchmarkConfig.from_toml(config)
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    run_benchmark(benchmark_config, input_dir, output_dir)
-
-
-def cli() -> None:
-    """Typer entry point."""
-    typer.run(main)
-
-
-if __name__ == "__main__":
-    cli()

python/prompt_bench/models.py

@@ -1,30 +0,0 @@
-"""Pydantic models for benchmark configuration."""
-
-from __future__ import annotations
-
-import tomllib
-from typing import TYPE_CHECKING
-
-from pydantic import BaseModel
-
-if TYPE_CHECKING:
-    from pathlib import Path
-
-
-class BenchmarkConfig(BaseModel):
-    """Top-level benchmark configuration loaded from TOML."""
-
-    models: list[str]
-    model_dir: str = "/zfs/models/hf"
-    port: int = 8000
-    gpu_memory_utilization: float = 0.90
-    temperature: float = 0.0
-    timeout: int = 300
-    concurrency: int = 4
-    vllm_startup_timeout: int = 900
-
-    @classmethod
-    def from_toml(cls, config_path: Path) -> BenchmarkConfig:
-        """Load benchmark config from a TOML file."""
-        raw = tomllib.loads(config_path.read_text())["bench"]
-        return cls(**raw)

python/prompt_bench/summarization_prompts.py

@@ -1,34 +0,0 @@
-SUMMARIZATION_SYSTEM_PROMPT = """You are a legislative analyst extracting policy substance from Congressional bill text.
-
-Your job is to compress a bill into a dense, neutral structured summary that captures every distinct policy action — including secondary effects that might be buried in subsections.
-
-EXTRACTION RULES:
-- IGNORE: whereas clauses, congressional findings that are purely political statements, recitals, preambles, citations of existing law by number alone, and procedural boilerplate.
-- FOCUS ON: operative verbs — what the bill SHALL do, PROHIBIT, REQUIRE, AUTHORIZE, AMEND, APPROPRIATE, or ESTABLISH.
-- SURFACE ALL THREADS: If the bill touches multiple policy areas, list each thread separately. Do not collapse them.
-- BE CONCRETE: Name the affected population, the mechanism, and the direction (expands/restricts/maintains).
-- STAY NEUTRAL: No political framing. Describe what the text does, not what its sponsors claim it does.
-
-OUTPUT FORMAT — plain structured text, not JSON:
-
-OPERATIVE ACTIONS:
-[Numbered list of what the bill actually does, one action per line, max 20 words each]
-
-AFFECTED POPULATIONS:
-[Who gains something, who loses something, or whose behavior is regulated]
-
-MECHANISMS:
-[How it works: new funding, mandate, prohibition, amendment to existing statute, grant program, study commission, etc.]
-
-POLICY THREADS:
-[List each distinct policy domain this bill touches, even minor ones. Use plain language, not domain codes.]
-
-SYMBOLIC/PROCEDURAL ONLY:
-[Yes or No — is this bill primarily a resolution, designation, or awareness declaration with no operative effect?]
-
-LENGTH TARGET: 150-250 words total. Be ruthless about cutting. Density over completeness."""
-
-SUMMARIZATION_USER_TEMPLATE = """Summarize the following Congressional bill according to your instructions.
-
-BILL TEXT:
-{text_content}"""

python/prompt_bench/tools/build_finetune_dataset.py

@@ -1,114 +0,0 @@
-"""Build a fine-tuning JSONL dataset from batch request + output files.
-
-Joins the original request JSONL (system + user messages) with the batch
-output JSONL (assistant completions) by custom_id to produce a ChatML-style
-messages JSONL suitable for fine-tuning.
-"""
-
-from __future__ import annotations
-
-import json
-import logging
-from pathlib import Path
-from typing import Annotated
-
-import typer
-
-logger = logging.getLogger(__name__)
-
-HTTP_OK = 200
-
-
-def load_requests(path: Path) -> dict[str, list[dict]]:
-    """Parse request JSONL into {custom_id: messages}."""
-    results: dict[str, list[dict]] = {}
-    with path.open(encoding="utf-8") as handle:
-        for raw_line in handle:
-            stripped = raw_line.strip()
-            if not stripped:
-                continue
-            record = json.loads(stripped)
-            custom_id = record["custom_id"]
-            messages = record["body"]["messages"]
-            results[custom_id] = messages
-    return results
-
-
-def load_completions(path: Path) -> dict[str, str]:
-    """Parse batch output JSONL into {custom_id: assistant_content}."""
-    results: dict[str, str] = {}
-    with path.open(encoding="utf-8") as handle:
-        for line_number, raw_line in enumerate(handle, 1):
-            stripped = raw_line.strip()
-            if not stripped:
-                continue
-            record = json.loads(stripped)
-            custom_id = record["custom_id"]
-            response = record.get("response", {})
-            if response.get("status_code") != HTTP_OK:
-                logger.warning("Skipping %s (line %d): status %s", custom_id, line_number, response.get("status_code"))
-                continue
-            body = response.get("body", {})
-            choices = body.get("choices", [])
-            if not choices:
-                logger.warning("Skipping %s (line %d): no choices", custom_id, line_number)
-                continue
-            content = choices[0].get("message", {}).get("content", "")
-            if not content:
-                logger.warning("Skipping %s (line %d): empty content", custom_id, line_number)
-                continue
-            results[custom_id] = content
-    return results
-
-
-def main(
-    requests_path: Annotated[Path, typer.Option("--requests", help="Batch request JSONL")] = Path(
-        "output/openai_batch/requests.jsonl",
-    ),
-    batch_output: Annotated[Path, typer.Option("--batch-output", help="Batch output JSONL")] = Path(
-        "batch_69d84558d91c819091d53f08d78f9fd6_output.jsonl",
-    ),
-    output_path: Annotated[Path, typer.Option("--output", help="Fine-tuning JSONL output")] = Path(
-        "output/finetune_dataset.jsonl",
-    ),
-    log_level: Annotated[str, typer.Option(help="Log level")] = "INFO",
-) -> None:
-    """Build fine-tuning dataset by joining request and output JSONL files."""
-    logging.basicConfig(level=log_level, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
-
-    logger.info("Loading requests from %s", requests_path)
-    requests = load_requests(requests_path)
-    logger.info("Loaded %d requests", len(requests))
-
-    logger.info("Loading completions from %s", batch_output)
-    completions = load_completions(batch_output)
-    logger.info("Loaded %d completions", len(completions))
-
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    matched = 0
-    skipped = 0
-
-    with output_path.open("w", encoding="utf-8") as handle:
-        for custom_id, messages in requests.items():
-            assistant_content = completions.get(custom_id)
-            if assistant_content is None:
-                skipped += 1
-                continue
-
-            example = {
-                "messages": [*messages, {"role": "assistant", "content": assistant_content}],
-            }
-            handle.write(json.dumps(example, ensure_ascii=False))
-            handle.write("\n")
-            matched += 1
-
-    logger.info("Wrote %d examples to %s (skipped %d unmatched)", matched, output_path, skipped)
-
-
-def cli() -> None:
-    """Typer entry point."""
-    typer.run(main)
-
-
-if __name__ == "__main__":
-    cli()

python/prompt_bench/tools/count_tokens.py

@@ -1,97 +0,0 @@
-"""Sum token usage across compressed and uncompressed run directories."""
-
-from __future__ import annotations
-
-import json
-import logging
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Annotated
-
-import typer
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class UsageTotals:
-    """Aggregate usage counters for a directory of run records."""
-
-    files: int = 0
-    errors: int = 0
-    prompt_tokens: int = 0
-    cached_tokens: int = 0
-    completion_tokens: int = 0
-    reasoning_tokens: int = 0
-    total_tokens: int = 0
-    per_file: list[tuple[str, int, int, int]] = field(default_factory=list)
-
-
-def tally_directory(directory: Path) -> UsageTotals:
-    """Return aggregated usage stats for every JSON record in a directory."""
-    totals = UsageTotals()
-    decoder = json.JSONDecoder()
-    for path in sorted(directory.glob("*.json")):
-        text = path.read_text().lstrip()
-        record, _ = decoder.raw_decode(text)
-        totals.files += 1
-        usage = record.get("usage")
-        if not usage:
-            totals.errors += 1
-            continue
-        prompt_tokens = usage.get("prompt_tokens", 0)
-        completion_tokens = usage.get("completion_tokens", 0)
-        total_tokens = usage.get("total_tokens", 0)
-        cached_tokens = (usage.get("prompt_tokens_details") or {}).get("cached_tokens", 0)
-        reasoning_tokens = (usage.get("completion_tokens_details") or {}).get("reasoning_tokens", 0)
-        totals.prompt_tokens += prompt_tokens
-        totals.completion_tokens += completion_tokens
-        totals.total_tokens += total_tokens
-        totals.cached_tokens += cached_tokens
-        totals.reasoning_tokens += reasoning_tokens
-        totals.per_file.append((path.name, prompt_tokens, completion_tokens, total_tokens))
-    return totals
-
-
-def log_totals(label: str, totals: UsageTotals) -> None:
-    """Log a one-block summary for a directory."""
-    counted = totals.files - totals.errors
-    average_total = totals.total_tokens / counted if counted else 0
-    logger.info("[%s]", label)
-    logger.info("  files          : %d (with usage: %d, errors: %d)", totals.files, counted, totals.errors)
-    logger.info("  prompt tokens  : %d", totals.prompt_tokens)
-    logger.info("  cached tokens  : %d", totals.cached_tokens)
-    logger.info("  completion tok : %d", totals.completion_tokens)
-    logger.info("  reasoning tok  : %d", totals.reasoning_tokens)
-    logger.info("  total tokens   : %d", totals.total_tokens)
-    logger.info("  avg total/file : %.1f", average_total)
-
-
-def main(
-    runs_dir: Annotated[Path, typer.Option("--runs-dir")] = Path("output/openai_runs_temp_1"),
-    log_level: Annotated[str, typer.Option("--log-level")] = "INFO",
-) -> None:
-    """Print token usage totals for the compressed and uncompressed run directories."""
-    logging.basicConfig(level=log_level, format="%(message)s")
-
-    grand = UsageTotals()
-    for label in ("compressed", "uncompressed"):
-        directory = runs_dir / label
-        if not directory.is_dir():
-            logger.warning("%s: directory not found at %s", label, directory)
-            continue
-        totals = tally_directory(directory)
-        log_totals(label, totals)
-        grand.files += totals.files
-        grand.errors += totals.errors
-        grand.prompt_tokens += totals.prompt_tokens
-        grand.cached_tokens += totals.cached_tokens
-        grand.completion_tokens += totals.completion_tokens
-        grand.reasoning_tokens += totals.reasoning_tokens
-        grand.total_tokens += totals.total_tokens
-
-    log_totals("grand total", grand)
-
-
-if __name__ == "__main__":
-    typer.run(main)

python/prompt_bench/vllm_client.py

@@ -1,68 +0,0 @@
-"""OpenAI-compatible client for vLLM's API."""
-
-from __future__ import annotations
-
-import logging
-import time
-from typing import Self
-
-import httpx
-
-logger = logging.getLogger(__name__)
-
-READY_POLL_INTERVAL = 2.0
-
-
-class VLLMClient:
-    """Talk to a vLLM server via its OpenAI-compatible API.
-
-    Args:
-        host: vLLM host.
-        port: vLLM port.
-        timeout: Per-request timeout in seconds.
-    """
-
-    def __init__(self, *, host: str = "localhost", port: int = 8000, timeout: int = 300) -> None:
-        """Create a client connected to a vLLM server."""
-        self._client = httpx.Client(base_url=f"http://{host}:{port}", timeout=timeout)
-
-    def wait_ready(self, max_wait: int) -> None:
-        """Poll /v1/models until the server is ready or timeout."""
-        deadline = time.monotonic() + max_wait
-        while time.monotonic() < deadline:
-            try:
-                response = self._client.get("/v1/models")
-                if response.is_success:
-                    logger.info("vLLM server is ready")
-                    return
-            except httpx.TransportError:
-                pass
-            time.sleep(READY_POLL_INTERVAL)
-        msg = f"vLLM server not ready after {max_wait}s"
-        raise TimeoutError(msg)
-
-    def complete(self, prompt: str, model: str, *, temperature: float = 0.0, max_tokens: int = 4096) -> str:
-        """Send a prompt to /v1/completions and return the response text."""
-        payload = {
-            "model": model,
-            "prompt": prompt,
-            "temperature": temperature,
-            "max_tokens": max_tokens,
-        }
-        logger.info("Sending prompt to %s (%d chars)", model, len(prompt))
-        response = self._client.post("/v1/completions", json=payload)
-        response.raise_for_status()
-        data = response.json()
-        return data["choices"][0]["text"]
-
-    def close(self) -> None:
-        """Close the HTTP client."""
-        self._client.close()
-
-    def __enter__(self) -> Self:
-        """Enter the context manager."""
-        return self
-
-    def __exit__(self, *args: object) -> None:
-        """Close the HTTP client on exit."""
-        self.close()

python/tools/whisper/Dockerfile

@@ -0,0 +1,17 @@
+FROM nvidia/cuda:12.4.1-cudnn-runtime-ubuntu22.04
+
+ENV DEBIAN_FRONTEND=noninteractive \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1
+
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends python3 python3-pip ffmpeg \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN pip3 install --no-cache-dir --upgrade pip \
+    && pip3 install --no-cache-dir faster-whisper requests
+
+WORKDIR /app
+COPY python/tools/whisper/inference.py /app/inference.py
+
+ENTRYPOINT ["python3", "/app/inference.py"]

python/tools/whisper/Dockerfile.dockerignore

@@ -0,0 +1,2 @@
+*
+!python/tools/whisper/inference.py

python/tools/whisper/__init__.py

@@ -0,0 +1 @@
+"""Whisper transcription tools (host orchestrator and container entrypoint)."""

python/tools/whisper/inference.py

@@ -0,0 +1,136 @@
+"""Container entrypoint that transcribes a directory of audio files with faster-whisper.
+
+Run inside the whisper-transcribe docker image; segment timestamps are grouped
+into one-minute buckets so the output reads as ``[HH:MM:00] text``.
+"""
+
+from __future__ import annotations
+
+import argparse
+import logging
+from pathlib import Path
+
+from faster_whisper import WhisperModel
+
+logger = logging.getLogger(__name__)
+
+AUDIO_EXTENSIONS = {".mp3", ".wav", ".m4a", ".flac", ".ogg", ".opus", ".mp4", ".mkv", ".webm", ".aac"}
+BUCKET_SECONDS = 60
+BEAM_SIZE = 5
+SECONDS_PER_HOUR = 3600
+SECONDS_PER_MINUTE = 60
+
+
+def format_timestamp(total_seconds: float) -> str:
+    """Render a whole-minute timestamp as ``HH:MM:00``.
+
+    Args:
+        total_seconds: Offset in seconds from the start of the audio.
+
+    Returns:
+        A zero-padded ``HH:MM:00`` string.
+    """
+    hours = int(total_seconds // SECONDS_PER_HOUR)
+    minutes = int((total_seconds % SECONDS_PER_HOUR) // SECONDS_PER_MINUTE)
+    return f"{hours:02d}:{minutes:02d}:00"
+
+
+def transcribe_file(model: WhisperModel, audio_path: Path, output_path: Path) -> None:
+    """Transcribe one audio file and write the bucketed transcript to disk.
+
+    Args:
+        model: Loaded faster-whisper model.
+        audio_path: Source audio file.
+        output_path: Destination ``.txt`` path.
+    """
+    logger.info("Transcribing %s", audio_path)
+    segments, info = model.transcribe(
+        str(audio_path),
+        language="en",
+        beam_size=BEAM_SIZE,
+        vad_filter=True,
+    )
+    logger.info("Duration %.1fs", info.duration)
+
+    buckets: dict[int, list[str]] = {}
+    for segment in segments:
+        bucket = int(segment.start // BUCKET_SECONDS)
+        buckets.setdefault(bucket, []).append(segment.text.strip())
+
+    lines = [f"[{format_timestamp(bucket * BUCKET_SECONDS)}] {' '.join(buckets[bucket])}" for bucket in sorted(buckets)]
+    output_path.write_text("\n\n".join(lines) + "\n", encoding="utf-8")
+    logger.info("Wrote %s", output_path)
+
+
+def find_audio_files(input_directory: Path) -> list[Path]:
+    """Collect every audio file under ``input_directory``.
+
+    Args:
+        input_directory: Directory to walk recursively.
+
+    Returns:
+        Sorted list of audio file paths.
+    """
+    return sorted(
+        path for path in input_directory.rglob("*") if path.is_file() and path.suffix.lower() in AUDIO_EXTENSIONS
+    )
+
+
+def configure_container_logger() -> None:
+    """Configure logging for the container (stdout, INFO)."""
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s %(levelname)s %(message)s",
+    )
+
+
+def parse_arguments() -> argparse.Namespace:
+    """Parse CLI arguments for the container entrypoint.
+
+    Returns:
+        Parsed argparse namespace.
+    """
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--input", type=Path, default=Path("/audio"))
+    parser.add_argument("--output", type=Path, default=Path("/output"))
+    parser.add_argument("--model", default="large-v3")
+    parser.add_argument(
+        "--download-only",
+        action="store_true",
+        help="Download the model into the cache volume and exit without transcribing.",
+    )
+    return parser.parse_args()
+
+
+def main() -> None:
+    """Load the model, then either exit (download-only) or transcribe the directory."""
+    configure_container_logger()
+    arguments = parse_arguments()
+
+    logger.info("Loading model %s on CUDA", arguments.model)
+    model = WhisperModel(arguments.model, device="cuda", compute_type="float16")
+
+    if arguments.download_only:
+        logger.info("Model ready; exiting (download-only mode)")
+        return
+
+    arguments.output.mkdir(parents=True, exist_ok=True)
+
+    audio_files = find_audio_files(arguments.input)
+    if not audio_files:
+        logger.warning("No audio files found in %s", arguments.input)
+        return
+
+    logger.info("Found %d audio file(s)", len(audio_files))
+    for audio_path in audio_files:
+        relative = audio_path.relative_to(arguments.input)
+        output_path = arguments.output / relative.with_suffix(".txt")
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+        if output_path.exists():
+            logger.info("Skip %s (already transcribed)", relative)
+            continue
+        transcribe_file(model, audio_path, output_path)
+
+
+if __name__ == "__main__":
+    main()

python/tools/whisper/transcribe.py

@@ -0,0 +1,167 @@
+"""Build and run the whisper transcription docker container on demand.
+
+The container is started fresh for each invocation and removed on exit
+(``docker run --rm``). The model is cached in a named docker volume so
+only the first run pays the download cost.
+"""
+
+from __future__ import annotations
+
+import logging
+import subprocess
+from pathlib import Path
+from typing import Annotated
+
+import typer
+
+from python.common import configure_logger
+
+logger = logging.getLogger(__name__)
+
+
+class Config:
+    """Paths and names for the whisper-transcribe Docker workflow."""
+
+    image_tag = "whisper-transcribe:latest"
+    model_volume = "whisper-models"
+    repo_root = Path(__file__).resolve().parents[3]
+    dockerfile = Path(__file__).resolve().parent / "Dockerfile"
+    huggingface_cache = "/root/.cache/huggingface"
+
+
+def run_docker(arguments: list[str]) -> None:
+    """Run a docker subcommand, streaming output and raising on failure.
+
+    Args:
+        arguments: Arguments to pass to the ``docker`` binary.
+
+    Raises:
+        subprocess.CalledProcessError: If docker exits non-zero.
+    """
+    logger.info("docker %s", " ".join(arguments))
+    subprocess.run(["docker", *arguments], check=True)
+
+
+def build_image() -> None:
+    """Build the whisper-transcribe image using the repo root as build context."""
+    logger.info("Building image %s", Config.image_tag)
+    run_docker(
+        [
+            "build",
+            "--tag",
+            Config.image_tag,
+            "--file",
+            str(Config.dockerfile),
+            str(Config.repo_root),
+        ],
+    )
+
+
+def model_cache_present(model: str) -> bool:
+    """Check whether the given model is already downloaded in the cache volume.
+
+    Args:
+        model: faster-whisper model name (e.g. ``large-v3``).
+
+    Returns:
+        True if the HuggingFace cache directory for the model exists in the volume.
+    """
+    cache_directory = f"hub/models--Systran--faster-whisper-{model}"
+    completed = subprocess.run(
+        [
+            "docker",
+            "run",
+            "--rm",
+            "--volume",
+            f"{Config.model_volume}:/cache",
+            "alpine",
+            "test",
+            "-d",
+            f"/cache/{cache_directory}",
+        ],
+        check=False,
+    )
+    return completed.returncode == 0
+
+
+def download_model(model: str) -> None:
+    """Download the model into the cache volume and exit.
+
+    Args:
+        model: faster-whisper model name.
+    """
+    logger.info("Downloading model %s into volume %s", model, Config.model_volume)
+    run_docker(
+        [
+            "run",
+            "--rm",
+            "--device=nvidia.com/gpu=all",
+            "--ipc=host",
+            "--volume",
+            f"{Config.model_volume}:{Config.huggingface_cache}",
+            Config.image_tag,
+            "--model",
+            model,
+            "--download-only",
+        ],
+    )
+
+
+def transcribe(input_directory: Path, output_directory: Path, model: str) -> None:
+    """Run transcription on every audio file under ``input_directory``.
+
+    Args:
+        input_directory: Host path containing audio files (mounted read-only).
+        output_directory: Host path for ``.txt`` transcripts.
+        model: faster-whisper model name.
+    """
+    logger.info("Transcribing %s -> %s (model=%s)", input_directory, output_directory, model)
+    run_docker(
+        [
+            "run",
+            "--rm",
+            "--device=nvidia.com/gpu=all",
+            "--ipc=host",
+            "--volume",
+            f"{input_directory}:/audio:ro",
+            "--volume",
+            f"{output_directory}:/output",
+            "--volume",
+            f"{Config.model_volume}:{Config.huggingface_cache}",
+            Config.image_tag,
+            "--model",
+            model,
+        ],
+    )
+
+
+def main(
+    input_directory: Annotated[Path, typer.Argument(help="Directory of audio files to transcribe.")],
+    output_directory: Annotated[Path, typer.Argument(help="Directory to write .txt transcripts to.")],
+    model: Annotated[str, typer.Option(help="faster-whisper model name.")] = "large-v3",
+    *,
+    force_download: Annotated[
+        bool,
+        typer.Option("--force-download", help="Re-download the model even if already cached."),
+    ] = False,
+) -> None:
+    """Build the image, ensure the model is cached, then transcribe and stop."""
+    configure_logger()
+
+    resolved_input = input_directory.resolve(strict=True)
+    output_directory.mkdir(parents=True, exist_ok=True)
+    resolved_output = output_directory.resolve()
+
+    build_image()
+
+    if force_download or not model_cache_present(model):
+        download_model(model)
+    else:
+        logger.info("Model %s already cached in volume %s", model, Config.model_volume)
+
+    transcribe(resolved_input, resolved_output, model)
+    logger.info("Done. Container stopped.")
+
+
+if __name__ == "__main__":
+    typer.run(main)

systems/bob/default.nix

@@ -1,8 +1,9 @@
 { inputs, pkgs, ... }:
 {
   imports = [
-    "${inputs.self}/users/richie"
     "${inputs.self}/users/math"
+    "${inputs.self}/users/richie"
+    "${inputs.self}/users/steve"
     "${inputs.self}/common/global"
     "${inputs.self}/common/optional/docker.nix"
     "${inputs.self}/common/optional/scanner.nix"

systems/bob/hardware.nix

@@ -28,7 +28,6 @@
         allowDiscards = true;
         keyFileSize = 4096;
         keyFile = "/dev/disk/by-id/usb-Samsung_Flash_Drive_FIT_0374620080067131-0:0";
-        fallbackToPassword = true;
       };
     };
     kernelModules = [ "kvm-amd" ];

systems/bob/llms.nix

@@ -42,11 +42,13 @@
       "qwen3:8b"
       "qwen3.5:27b"
       "qwen3.5:35b"
+      "qwen3.6:35b"
       "translategemma:12b"
       "translategemma:27b"
       "translategemma:4b"
+      "rinex20/translategemma3:12b"
     ];
-    models = "/zfs/models";
+    models = "/zfs/storage/models";
     openFirewall = true;
   };
 }

systems/bob/scripts/zfs.sh

@@ -1,11 +0,0 @@
-#!/bin/bash
-
-# zpools
-
-# storage
-sudo zpool create -f -o ashift=12 -O acltype=posixacl -O atime=off -O dnodesize=auto -O xattr=sa -O compression=zstd -m /zfs/storage storage mirror
-sudo zpool create -o ashift=12 -O acltype=posixacl -O atime=off -O dnodesize=auto -O xattr=sa -O compression=zstd -m /zfs/storage storage
-
-
-# storage datasets
-sudo zfs create storage/models -o recordsize=1M

systems/bob/snapshot_config.toml

@@ -24,6 +24,6 @@ monthly = 0
 
 ["root_pool/models"]
 15_min = 4
-hourly = 2
+hourly = 24
 daily = 0
 monthly = 0

systems/bob/syncthing.nix

@@ -31,5 +31,15 @@
       ];
       fsWatcherEnabled = true;
     };
+    "recordings" = {
+      path = "/home/richie/recordings";
+      devices = [
+        "jeeves"
+        "phone"
+        "rhapsody-in-green"
+      ];
+      fsWatcherEnabled = true;
+    };
+
   };
 }

systems/brain/hardware.nix

@@ -26,7 +26,6 @@
         allowDiscards = true;
         keyFileSize = 4096;
         keyFile = "/dev/disk/by-id/usb-USB_SanDisk_3.2Gen1_03021630090925173333-0:0";
-        fallbackToPassword = true;
       };
     };
     kernelModules = [ "kvm-intel" ];

systems/jeeves/default.nix

@@ -4,9 +4,10 @@ let
 in
 {
   imports = [
-    "${inputs.self}/users/richie"
-    "${inputs.self}/users/math"
     "${inputs.self}/users/dov"
+    "${inputs.self}/users/math"
+    "${inputs.self}/users/richie"
+    "${inputs.self}/users/steve"
     "${inputs.self}/common/global"
     "${inputs.self}/common/optional/docker.nix"
     "${inputs.self}/common/optional/ssh_decrypt.nix"
@@ -15,6 +16,7 @@ in
     "${inputs.self}/common/optional/zerotier.nix"
     ./docker
     ./services
+    ./web_services
     ./hardware.nix
     ./networking.nix
     ./programs.nix

systems/jeeves/hardware.nix

@@ -9,7 +9,6 @@ let
     inherit device;
     keyFileSize = 4096;
     keyFile = "/dev/disk/by-id/usb-XIAO_USB_Drive_24587CE29074-0:0";
-    fallbackToPassword = true;
   };
   makeLuksSSD =
     device:

systems/jeeves/services/cloud_flare_tunnel.nix

@@ -1,17 +0,0 @@
-{ pkgs, ... }:
-let
-  vars = import ../vars.nix;
-in
-{
-  systemd.services.cloud_flare_tunnel = {
-    description = "cloud_flare_tunnel proxy's traffic through cloudflare";
-    after = [ "network.target" ];
-    wantedBy = [ "multi-user.target" ];
-    serviceConfig = {
-      Type = "simple";
-      EnvironmentFile = "${vars.secrets}/docker/cloud_flare_tunnel";
-      ExecStart = "${pkgs.cloudflared}/bin/cloudflared --no-autoupdate tunnel run";
-      Restart = "on-failure";
-    };
-  };
-}

systems/jeeves/services/gitea.nix

@@ -2,7 +2,10 @@ let
   vars = import ../vars.nix;
 in
 {
-  networking.firewall.allowedTCPPorts = [ 6443 ];
+  networking.firewall.allowedTCPPorts = [
+    6443
+    2223
+  ];
 
   services.gitea = {
     enable = true;
@@ -24,7 +27,7 @@ in
         ROOT_URL = "https://gitea.tmmworkshop.com/";
         HTTP_PORT = 6443;
         SSH_PORT = 2223;
-        SSH_LISTEN_PORT = 2224;
+        SSH_LISTEN_PORT = 2223;
         START_SSH_SERVER = true;
         PUBLIC_URL_DETECTION = "auto";
       };

systems/jeeves/services/validate_system.toml

@@ -1,7 +1,6 @@
 zpool = ["root_pool", "storage", "media"]
 services = [
     "audiobookshelf",
-    "cloud_flare_tunnel",
     "haproxy",
     "docker",
     "home-assistant",

systems/jeeves/syncthing.nix

@@ -89,7 +89,16 @@ in
           ];
           fsWatcherEnabled = true;
         };
-        #
+        "recordings" = {
+          path = "/home/richie/recordings";
+          devices = [
+            "bob"
+            "phone"
+            "rhapsody-in-green"
+          ];
+          fsWatcherEnabled = true;
+        };
+        # davids-server
         "davids-backup1" = {
           id = "8229p-8z3tm"; # cspell:disable-line
           path = "${vars.syncthing}/davids_backups/1";

systems/jeeves/web_services/acme.nix

@@ -0,0 +1,62 @@
+let
+  domains = [
+    "audiobookshelf"
+    "cache"
+    "gitea"
+    "jellyfin"
+    "share"
+  ];
+
+  makeCert = name: {
+    name = "${name}.tmmworkshop.com";
+    value = {
+      webroot = "/var/lib/acme/.challenges";
+      group = "acme";
+      reloadServices = [ "haproxy.service" ];
+    };
+  };
+
+  acmeServices = map (domain: "acme-${domain}.tmmworkshop.com.service") domains;
+in
+{
+  users.users.haproxy.extraGroups = [ "acme" ];
+
+  security.acme = {
+    acceptTerms = true;
+    defaults.email = "Richie@tmmworkshop.com";
+    certs = builtins.listToAttrs (map makeCert domains);
+  };
+
+  # Minimal nginx to serve ACME HTTP-01 challenge files for HAProxy
+  services.nginx = {
+    enable = true;
+    virtualHosts."acme-challenge" = {
+      listen = [
+        {
+          addr = "127.0.0.1";
+          port = 8402;
+        }
+      ];
+      locations."/.well-known/acme-challenge/" = {
+        root = "/var/lib/acme/.challenges";
+      };
+    };
+  };
+
+  # Ensure the challenge directory exists with correct permissions
+  systemd.tmpfiles.rules = [
+    "d /var/lib/acme/.challenges 0750 acme acme - -"
+    "d /var/lib/acme/.challenges/.well-known 0750 acme acme - -"
+    "d /var/lib/acme/.challenges/.well-known/acme-challenge 0750 acme acme - -"
+  ];
+
+  users.users.nginx.extraGroups = [ "acme" ];
+
+  # HAProxy needs certs to exist before it can bind :443.
+  # NixOS's acme module generates self-signed placeholders on first boot
+  # via acme-<domain>.service — just make HAProxy wait for them.
+  systemd.services.haproxy = {
+    after = acmeServices;
+    wants = acmeServices;
+  };
+}

systems/jeeves/web_services/default.nix

@@ -0,0 +1,9 @@
+{ lib, ... }:
+{
+  imports =
+    let
+      files = builtins.attrNames (builtins.readDir ./.);
+      nixFiles = builtins.filter (name: lib.hasSuffix ".nix" name && name != "default.nix") files;
+    in
+    map (file: ./. + "/${file}") nixFiles;
+}

systems/jeeves/web_services/haproxy.cfg

@@ -6,6 +6,7 @@ global
 defaults
   log global
   mode http
+  option httplog
   retries 3
   maxconn 2000
   timeout connect 5s
@@ -22,25 +23,37 @@ defaults
 #Application Setup
 frontend ContentSwitching
   bind *:80 v4v6
-  bind *:443 v4v6 ssl crt /zfs/storage/secrets/docker/cloudflare.pem
+  bind *:443 v4v6 ssl crt /var/lib/acme/audiobookshelf.tmmworkshop.com/full.pem crt /var/lib/acme/cache.tmmworkshop.com/full.pem crt /var/lib/acme/jellyfin.tmmworkshop.com/full.pem crt /var/lib/acme/share.tmmworkshop.com/full.pem crt /var/lib/acme/gitea.tmmworkshop.com/full.pem
   mode  http
+
+  # ACME challenge routing (must be first)
+  acl is_acme path_beg /.well-known/acme-challenge/
+  use_backend acme_challenge if is_acme
+
   # tmmworkshop.com
   acl host_audiobookshelf  hdr(host) -i audiobookshelf.tmmworkshop.com
   acl host_cache  hdr(host) -i cache.tmmworkshop.com
   acl host_jellyfin  hdr(host) -i jellyfin.tmmworkshop.com
   acl host_share  hdr(host) -i share.tmmworkshop.com
-  acl host_gcw  hdr(host) -i gcw.tmmworkshop.com
-  acl host_n8n  hdr(host) -i n8n.tmmworkshop.com
   acl host_gitea  hdr(host) -i gitea.tmmworkshop.com
 
+  # Hosts allowed to serve plain HTTP (add entries to skip the HTTPS redirect)
+  acl allow_http hdr(host) -i __none__
+  # acl allow_http hdr(host) -i example.tmmworkshop.com
+
+  # Redirect all HTTP to HTTPS unless on the allow list or ACME challenge
+  http-request redirect scheme https code 301 if !{ ssl_fc } !allow_http !is_acme
+
   use_backend audiobookshelf_nodes if host_audiobookshelf
   use_backend cache_nodes  if host_cache
   use_backend jellyfin if host_jellyfin
   use_backend share_nodes  if host_share
-  use_backend gcw_nodes  if host_gcw
-  use_backend n8n  if host_n8n
   use_backend gitea  if host_gitea
 
+backend acme_challenge
+  mode http
+  server acme 127.0.0.1:8402
+
 backend audiobookshelf_nodes
   mode http
   server server 127.0.0.1:8000
@@ -60,14 +73,6 @@ backend share_nodes
   mode http
   server server 127.0.0.1:8091
 
-backend gcw_nodes
-  mode http
-  server server 127.0.0.1:8092
-
-backend n8n
-  mode http
-  server server 127.0.0.1:5678
-
 backend gitea
   mode http
   server server 127.0.0.1:6443

systems/rhapsody-in-green/default.nix

@@ -24,11 +24,19 @@
     hostId = "6404140d";
     firewall = {
       enable = true;
-      allowedTCPPorts = [ ];
+      allowedTCPPorts = [
+        8000
+        8080
+      ];
     };
     networkmanager.enable = true;
   };
 
+  programs.appimage = {
+    enable = true;
+    binfmt = true; # allows *.AppImage to be run directly
+  };
+
   services = {
     openssh.ports = [ 922 ];
     flatpak.enable = true;

systems/rhapsody-in-green/sunshine.nix

@@ -7,18 +7,22 @@
   };
   environment.systemPackages = [ pkgs.kdePackages.libkscreen ];
 
-  boot.kernelParams = [
+  boot = {
+    kernelParams = [
       "drm.edid_firmware=DP-4:edid/virtual-display.bin"
       "video=DP-4:e"
     ];
+  };
 
-  hardware = {
+  hardware.firmware = [
-    firmwareCompression = "none";
+    (pkgs.runCommandLocal "virtual-display-edid"
-    firmware = [
+      {
-      (pkgs.runCommandLocal "virtual-display-edid" { } ''
+        compressFirmware = false;
+      }
+      ''
         mkdir -p $out/lib/firmware/edid
         cp ${./edid/virtual-display.bin} $out/lib/firmware/edid/virtual-display.bin
-      '')
+      ''
+    )
   ];
-  };
 }

systems/rhapsody-in-green/syncthing.nix

@@ -55,6 +55,15 @@
       ];
       fsWatcherEnabled = true;
     };
+    "recordings" = {
+      path = "/home/richie/recordings";
+      devices = [
+        "bob"
+        "jeeves"
+        "phone"
+      ];
+      fsWatcherEnabled = true;
+    };
     "vault" = {
       path = "/home/richie/vault";
       devices = [

users/dov/home/cli/git.nix

@@ -1,6 +1,7 @@
 {
   programs.git = {
     enable = true;
+    signing.format = null;
     settings = {
       user = {
         email = "dov.kruger@gmail.com";

users/elise/home/cli/git.nix

@@ -1,6 +1,7 @@
 {
   programs.git = {
     enable = true;
+    signing.format = null;
     settings = {
       user = {
         email = "DumbPuppy208@gmail.com";

users/math/home/cli/git.nix

@@ -1,6 +1,7 @@
 {
   programs.git = {
     enable = true;
+    signing.format = null;
     settings = {
       user = {
         email = "matthew.michal11@gmail.com";

users/richie/home/cli/git.nix

@@ -1,6 +1,7 @@
 {
   programs.git = {
     enable = true;
+    signing.format = null;
     settings = {
       user = {
         email = "Richie@tmmworkshop.com";

users/richie/home/gui/default.nix

@@ -22,9 +22,10 @@
     chromium
     # dev tools
     claude-code
+    codex
     gparted
     jetbrains.datagrip
-    proxychains
     opencode
+    proxychains
   ];
 }

users/richie/home/gui/vscode/settings.json

@@ -78,7 +78,6 @@
     "Corvidae",
     "drivername",
     "fastapi",
-    "Qwen",
     "sandboxing",
     "syncthing",
   ],
@@ -99,5 +98,4 @@
   "hediet.vscode-drawio.resizeImages": null,
   "hediet.vscode-drawio.appearance": "automatic",
   "claudeCode.preferredLocation": "panel",
-  "docker.extension.enableComposeLanguageServer": false,
 }

users/steve/default.nix

@@ -0,0 +1,44 @@
+{
+  pkgs,
+  config,
+  ...
+}:
+let
+  ifTheyExist = groups: builtins.filter (group: builtins.hasAttr group config.users.groups) groups;
+in
+{
+
+  users = {
+    users.steve = {
+      isNormalUser = true;
+
+      shell = pkgs.zsh;
+      group = "steve";
+      openssh.authorizedKeys.keys = [
+        "ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAIJH03VzDbUhzfhvwD+OsYh6GobODYaI9jdNdzWQoqFsp matth@Jove" # cspell:disable-line
+      ];
+      extraGroups = [
+        "audio"
+        "video"
+        "wheel"
+        "users"
+      ]
+      ++ ifTheyExist [
+        "dialout"
+        "docker"
+        "hass"
+        "libvirtd"
+        "networkmanager"
+        "plugdev"
+        "scanner"
+        "transmission"
+        "uaccess"
+        "wireshark"
+      ];
+      uid = 1005;
+    };
+
+    groups.steve.gid = 1005;
+  };
+  home-manager.users.steve = import ./systems/${config.networking.hostName}.nix;
+}

users/steve/home/cli/default.nix

@@ -0,0 +1,9 @@
+{
+  imports = [
+    ./direnv.nix
+    ./git.nix
+    ./zsh.nix
+  ];
+
+  programs.starship.enable = true;
+}

users/steve/home/cli/direnv.nix

@@ -0,0 +1,8 @@
+{
+
+  programs.direnv = {
+    enable = true;
+    enableZshIntegration = true;
+    nix-direnv.enable = true;
+  };
+}

users/steve/home/cli/git.nix

@@ -0,0 +1,15 @@
+{
+  programs.git = {
+    enable = true;
+    signing.format = null;
+    settings = {
+      user = {
+        email = "matthew.michal11@gmail.com";
+        name = "Matthew Michal";
+      };
+      pull.rebase = true;
+      color.ui = true;
+    };
+    lfs.enable = true;
+  };
+}

users/steve/home/cli/zsh.nix

@@ -0,0 +1,28 @@
+{
+  programs.zsh = {
+    enable = true;
+    syntaxHighlighting.enable = true;
+    history.size = 10000;
+    oh-my-zsh = {
+      enable = true;
+      plugins = [
+        "git"
+        "docker"
+        "docker-compose"
+        "colored-man-pages"
+        "rust"
+        "systemd"
+        "tmux"
+        "ufw"
+        "z"
+      ];
+    };
+    shellAliases = {
+      "lrt" = "eza --icons -lsnew";
+      "ls" = "eza";
+      "ll" = "eza --long --group";
+      "la" = "eza --all";
+
+    };
+  };
+}

users/steve/home/global.nix

@@ -0,0 +1,22 @@
+{ config, ... }:
+{
+  imports = [
+    ./cli
+    ./programs.nix
+    ./ssh_config.nix
+  ];
+
+  programs = {
+    home-manager.enable = true;
+    git.enable = true;
+  };
+
+  home = {
+    username = "steve";
+    homeDirectory = "/home/${config.home.username}";
+    stateVersion = "24.05";
+    sessionVariables = {
+      FLAKE = "$HOME/dotfiles";
+    };
+  };
+}

users/steve/home/programs.nix

@@ -0,0 +1,57 @@
+{ pkgs, ... }:
+{
+  home.packages = with pkgs; [
+    # cli
+    bat
+    btop
+    eza
+    fd
+    ffmpegthumbnailer
+    fzf
+    git
+    gnupg
+    imagemagick
+    jq
+    ncdu
+    ouch
+    p7zip
+    poppler
+    rar
+    ripgrep
+    starship
+    tmux
+    unzip
+    yazi
+    zoxide
+    # system info
+    hwloc
+    lynis
+    pciutils
+    smartmontools
+    usbutils
+    # networking
+    iperf3
+    nmap
+    wget
+    # python
+    poetry
+    ruff
+    uv
+    # nodejs
+    nodejs
+    # Rust packages
+    trunk
+    wasm-pack
+    cargo-watch
+    cargo-generate
+    cargo-audit
+    cargo-update
+    # nix
+    nix-init
+    nix-output-monitor
+    nix-prefetch
+    nix-tree
+    nixfmt
+    treefmt
+  ];
+}

users/steve/home/ssh_config.nix

@@ -0,0 +1,6 @@
+{
+  programs.ssh = {
+    enable = true;
+    enableDefaultConfig = false;
+  };
+}

users/steve/systems/bob.nix

@@ -0,0 +1,5 @@
+{
+  imports = [
+    ../home/global.nix
+  ];
+}

users/steve/systems/jeeves.nix

@@ -0,0 +1,5 @@
+{
+  imports = [
+    ../home/global.nix
+  ];
+}