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1420 1421 1422 1423 1424 1425 1426 1427 | """LLM fine-tune (LoRA SFT) method (family ``llm_ft``).
A single concrete class :class:`LLMFineTuned` (``name="llm_finetuned"``)
that wraps QLoRA SFT over any of the three MacroLens panel LLMs:
``llama_scout``, ``gemma4``, ``qwen35``.
Per the unified-API plan §9, this method's ``tasks`` defaults to the
maximum coverage (T1..T7); the runner narrows to the post-hoc-selected
ZS-winner's coverage at config time.
Method contract (sklearn-style):
LLMFineTuned(task="T1", config=LLMFineTunedConfig(...), dry_run=False)
.fit(X_train, y_train, seed=42) # LoRA SFT on (prompt, answer) pairs
.predict(X_test) # generate with the merged adapter
.save(path) # PEFT adapter + manifest.json
LLMFineTuned.load(path) # reload
Hard rules:
- Zero IO of benchmark data (the loader provides X / y).
- Zero eval imports.
- Zero ``meta`` consumption.
- Honors ``MACROLENS_DETERMINISTIC=1`` via :func:`_seed_from_env`.
Per-task input / output shapes match :mod:`methods.llm` and
:mod:`methods.llm_ts_reason`.
Inference engine
----------------
``predict`` calls go through a single shared protocol —
``engine.chat_complete(messages, max_tokens, ...) -> str`` — exposed by
:mod:`methods._openai_engine`. The runner serves the LoRA adapter via
``vllm serve --enable-lora --lora-modules <id>=<path>`` and injects an
``OpenAIChatEngine`` whose ``model_id`` resolves to the adapter id; in
``dry_run=True`` mode (no live endpoint) a
:class:`methods._openai_engine.DryRunEngine` is used so the
shape-contract smoke tests still pass.
Per-task fine-tune framing (training pair construction):
T1 : (lookback close → forecast horizon close) — instruction is the
numeric history serialised as text; output is the horizon close
trajectory rounded to 2dp.
T2/T5: fundamentals → market-cap dollar value.
T3/T6: company snapshot → JSON of XBRL field → value pairs.
T4 : event_type + event_description → return percentage.
T7 : property attributes → JSON ``{rent, price}``.
Serialisation: ``LLMFineTuned.save(path)`` writes:
- ``manifest.json`` — name, family, tasks, schema_version, task,
hyperparams (config.model_dump()), lib_versions.
- ``adapter/`` — ``PeftModel.save_pretrained(adapter_path)``.
- ``tokenizer/`` — ``AutoTokenizer.save_pretrained(...)`` so the
same tokenizer is used at load time.
- ``adapter.sha256`` — sha256 of the adapter directory tree (recorded
in manifest as a provenance hash).
``LLMFineTuned.load(path)`` reverses the above and prepares the model
for ``predict(X)``.
"""
from __future__ import annotations
import hashlib
import json
import logging
import os
import pathlib
import re
from typing import Any, Literal
import numpy as np
import pandas as pd
from ._config import LLMFineTunedConfig
from ._openai_engine import DryRunEngine
from ._registry import register
from .base import Method, _HFSaveMixin
logger = logging.getLogger(__name__)
# ── Map a config base_model literal to a HuggingFace repo id ─────────────
_BASE_MODEL_ID: dict[str, str] = {
"llama_scout": "meta-llama/Llama-4-Scout-17B-16E-Instruct",
"gemma4": "google/gemma-4-31B-it",
"qwen35": "Qwen/Qwen3.5-27B-FP8",
}
# ── Default XBRL field panel for T3/T6 when y_train is unavailable ──────
_DEFAULT_T3_T6_FIELDS = (
"Revenues",
"NetIncomeLoss",
"Assets",
"Liabilities",
"StockholdersEquity",
"OperatingIncomeLoss",
"CashAndCashEquivalents",
"PropertyPlantAndEquipmentNet",
"LongTermDebt",
"ResearchAndDevelopmentExpense",
)
# ── Shared helpers (kept in sync with methods.llm_ts_reason) ─────────────
_NUM_RE = re.compile(r"[-+]?\d*\.?\d+(?:[eE][-+]?\d+)?")
def _parse_first_number(text: str) -> float | None:
if not text:
return None
m = _NUM_RE.search(text.replace(",", ""))
if not m:
return None
try:
return float(m.group(0))
except (TypeError, ValueError):
return None
def _parse_horizon_list(response: str, horizon: int) -> np.ndarray | None:
"""Extract a JSON list of floats representing a forecast trajectory.
Looks for the first ``[...]`` substring in ``response`` and parses it
as JSON. Returns a ``(horizon,)`` float32 ndarray, padding with the
last value when shorter and truncating when longer. Falls back to
extracting all numeric tokens from the bracketed slice when JSON
parsing fails. Returns ``None`` on total parse failure.
"""
if not response:
return None
start = response.find("[")
end = response.rfind("]")
if start < 0 or end <= start:
return None
candidate = response[start : end + 1]
parsed: list[Any] | None = None
try:
loaded = json.loads(candidate)
if isinstance(loaded, list):
parsed = loaded
except json.JSONDecodeError:
parsed = None
if parsed is None:
tokens = _NUM_RE.findall(candidate)
if not tokens:
return None
try:
parsed = [float(t) for t in tokens]
except ValueError:
return None
vals: list[float] = []
for v in parsed:
try:
vals.append(float(v))
except (TypeError, ValueError):
continue
if not vals:
return None
if len(vals) >= horizon:
out = np.asarray(vals[:horizon], dtype=np.float32)
else:
pad = [vals[-1]] * (horizon - len(vals))
out = np.asarray(vals + pad, dtype=np.float32)
return out
def _extract_json_object(response: str) -> dict[str, Any] | None:
"""Extract a structured ``{field: value}`` map from an LLM response.
Two paths:
1. **JSON object**: legacy support for replies like
``{"Revenues": 1000000, "Assets": 5000000}``. Slices from the first
``{`` to the last ``}`` and tries ``json.loads``.
2. **Plain-text key/value**: line-oriented format ``<Field>: <number>``
which is what current prompts request. Each line is matched by
regex; numbers may use ``$``, commas, scientific notation. This is
the natural LLM output mode and avoids JSON parse failures.
Returns ``None`` if neither path yields any field/value pair.
"""
if not response:
return None
# Path 1: legacy JSON object.
start = response.find("{")
end = response.rfind("}")
if start >= 0 and end > start:
try:
j = json.loads(response[start:end + 1])
if isinstance(j, dict):
return j
except json.JSONDecodeError:
pass
depth = 0
for i in range(start, len(response)):
ch = response[i]
if ch == "{":
depth += 1
elif ch == "}":
depth -= 1
if depth == 0:
try:
j = json.loads(response[start:i + 1])
if isinstance(j, dict):
return j
except json.JSONDecodeError:
break
# Path 2: plain-text "<Field>: <number>" lines (one or many).
out: dict[str, float] = {}
line_re = re.compile(
r"\*?\*?\s*([A-Za-z][A-Za-z0-9_]*)\s*:\s*\$?\s*"
r"(-?\d[\d,]*(?:\.\d+)?(?:[eE][-+]?\d+)?)"
)
for m in line_re.finditer(response):
field = m.group(1)
num_str = m.group(2).replace(",", "")
try:
out[field] = float(num_str)
except ValueError:
continue
return out or None
def _safe_float(v: Any, default: float = 0.0) -> float:
if v is None:
return default
if isinstance(v, (int, float)) and not (
isinstance(v, float) and np.isnan(v)
):
return float(v)
try:
if pd.isna(v): # type: ignore[arg-type]
return default
except (TypeError, ValueError):
pass
try:
return float(v)
except (TypeError, ValueError):
return default
def _seed_from_env(seed: int) -> None:
import random
random.seed(seed)
np.random.seed(seed)
os.environ.setdefault("PYTHONHASHSEED", str(seed))
try:
import torch
torch.manual_seed(seed)
if os.environ.get("MACROLENS_DETERMINISTIC") == "1":
try:
torch.use_deterministic_algorithms(True)
except Exception:
pass
try:
torch.backends.cudnn.deterministic = True # type: ignore[attr-defined]
except Exception:
pass
except Exception:
pass
def _find_close_idx_from_array(X: np.ndarray) -> int:
if X.ndim != 3 or X.shape[2] == 0:
return 0
samples = X.reshape(-1, X.shape[2])
pos_mask = (samples >= 0).all(axis=0)
if not pos_mask.any():
return 0
medians = np.median(np.abs(samples), axis=0)
candidates = np.where(
pos_mask & (medians >= 1.0) & (medians <= 5000.0)
)[0]
if len(candidates) == 0:
return 0
cand_meds = medians[candidates]
log_cand = np.log10(cand_meds + 1e-9)
target = np.median(log_cand)
return int(candidates[np.argmin(np.abs(log_cand - target))])
def _sha256_dir(path: pathlib.Path) -> str:
"""Stable SHA256 over a directory tree (sorted file order)."""
h = hashlib.sha256()
if not path.exists():
return h.hexdigest()
for fp in sorted(path.rglob("*")):
if not fp.is_file():
continue
h.update(fp.relative_to(path).as_posix().encode("utf-8"))
h.update(b"\x00")
h.update(fp.read_bytes())
return h.hexdigest()
# ── Per-task (X, y) → (instruction, response) pair builders ──────────────
def _t1_pairs(
X: np.ndarray, y: np.ndarray, *, close_idx: int,
) -> list[tuple[str, str]]:
"""T1 SFT pairs: (numeric history → horizon close trajectory).
The instruction matches :func:`_t1_predict_prompt` so the SFT-trained
adapter sees the same prompt at fit and predict time. The response is
a bare JSON array of ``horizon`` floats — directly parseable by
:func:`_parse_horizon_list`.
"""
if X.ndim != 3 or y.ndim != 2:
raise ValueError(
f"T1 expects X (N,L,F), y (N,H); got X={X.shape}, y={y.shape}"
)
n, _lookback, _ = X.shape
horizon = y.shape[1]
pairs: list[tuple[str, str]] = []
for i in range(n):
close_history = X[i, :, close_idx]
instr = _t1_predict_prompt(close_history, X.shape[1], horizon)
target_str = ", ".join(
f"{round(float(v), 2)}" for v in y[i].tolist()
)
resp = f"[{target_str}]"
pairs.append((instr, resp))
return pairs
def _t2_t5_pairs(
X: pd.DataFrame, y: np.ndarray, *, task: str,
) -> list[tuple[str, str]]:
pairs: list[tuple[str, str]] = []
if task == "T2":
for (_, row), tgt in zip(X.iterrows(), y):
sector = row.get("sector", "Unknown")
revenue = _safe_float(row.get("stmt_revenue", 0))
net_income = _safe_float(row.get("stmt_net_income", 0))
total_assets = _safe_float(row.get("stmt_total_assets", 0))
employees = row.get("fullTimeEmployees", "N/A")
instr = (
f"You are a financial analyst. Estimate the total equity "
f"market capitalization of this company.\n\n"
f"Sector: {sector}\n"
f"Revenue: ${revenue:,.0f}\n"
f"Net Income: ${net_income:,.0f}\n"
f"Total Assets: ${total_assets:,.0f}\n"
f"Employees: {employees}"
)
resp = f"Estimated market cap: ${float(tgt):,.0f}"
pairs.append((instr, resp))
else: # T5
stmt_cols = [c for c in X.columns if c.startswith("stmt_")]
for (_, row), tgt in zip(X.iterrows(), y):
sector = row.get("sector", "Unknown")
industry = row.get("industry", "Unknown")
items = []
for c in stmt_cols:
val = row.get(c)
if pd.notna(val):
try:
items.append(f"{c}: ${float(val):,.0f}")
except (TypeError, ValueError):
continue
block = "\n".join(items) if items else "No financial statement data available"
instr = (
f"You are a private equity analyst. Given ONLY financial "
f"statement data (no market price), estimate the market "
f"capitalization of this company.\n\n"
f"Sector: {sector}\nIndustry: {industry}\n{block}"
)
resp = f"Estimated market cap: ${float(tgt):,.0f}"
pairs.append((instr, resp))
return pairs
def _t3_t6_pairs(
X: pd.DataFrame, y: pd.DataFrame, *, task: str,
) -> list[tuple[str, str]]:
"""T3 / T6: build one pair per (ticker, fiscal_year) row of X.
Response is a JSON object aggregating all ground-truth fields for
that (ticker, fiscal_year). Rows missing in ``y`` are skipped (no
silent zero-fill).
"""
pairs: list[tuple[str, str]] = []
# Group y by (ticker, fiscal_year)
if y.empty:
return pairs
y_grouped = (
y.groupby(["ticker", "fiscal_year"])
.apply(lambda g: dict(zip(g["field"], g["value"])))
.to_dict()
)
fields_seen: list[str] = []
for _, row in X.iterrows():
ticker = str(row.get("ticker", "?"))
fy = row.get("fiscal_year", None)
key = (ticker, fy)
# Pandas Int64 keys may not round-trip; try a tolerant lookup.
if key not in y_grouped:
for cand_key in y_grouped:
if str(cand_key[0]) == ticker and str(cand_key[1]) == str(fy):
key = cand_key
break
gt_fields = y_grouped.get(key, {})
if not gt_fields:
continue
fields_str = ", ".join(sorted(gt_fields.keys()))
if not fields_seen:
fields_seen = sorted(gt_fields.keys())
if task == "T3":
sector = row.get("sector", "Unknown")
revenue = _safe_float(row.get("stmt_revenue", 0))
net_income = _safe_float(row.get("stmt_net_income", 0))
instr = (
f"You are a financial analyst. Given {ticker}'s known "
f"fundamentals (sector={sector}, revenue=${revenue:,.0f}, "
f"net_income=${net_income:,.0f}), predict these XBRL "
f"fields: [{fields_str}]"
)
else: # T6
description = row.get(
"company_description", f"A company with ticker {ticker}",
)
sector = row.get("sector", "Unknown")
industry = row.get("industry", "Unknown")
instr = (
f"Given this company description: '{description}', "
f"sector: '{sector}', industry: '{industry}', generate "
f"plausible financial statement values for these XBRL "
f"fields: [{fields_str}]"
)
resp = json.dumps(
{k: round(float(v), 2) for k, v in gt_fields.items()
if pd.notna(v)},
)
pairs.append((instr, resp))
return pairs
def _t4_pairs(X: Any, y: np.ndarray) -> list[tuple[str, str]]:
if isinstance(X, pd.DataFrame):
event_type = X.get("event_type", pd.Series([], dtype=object)).to_numpy()
event_desc = X.get(
"event_description", pd.Series([""] * len(event_type), dtype=object),
).to_numpy()
elif isinstance(X, dict):
event_type = np.asarray(X.get("event_type", []))
event_desc = np.asarray(X.get("event_description", []))
else:
raise ValueError(
f"T4 X must be DataFrame or dict, got {type(X).__name__}"
)
pairs: list[tuple[str, str]] = []
for et, ed, tgt in zip(event_type, event_desc, y):
et_s = str(et) if et is not None else "unknown"
ed_s = str(ed)[:200] if ed is not None else ""
instr = (
f"You are a financial analyst. Given the scenario:\n"
f"- Event type: {et_s}\n"
+ (f"- Description: {ed_s}\n" if ed_s else "")
+ "\nPredict the stock return (%) following this event."
)
resp = f"Predicted return: {float(tgt):.2f}%"
pairs.append((instr, resp))
return pairs
def _t7_pairs(X: pd.DataFrame, y: pd.DataFrame) -> list[tuple[str, str]]:
pairs: list[tuple[str, str]] = []
y_by_addr = (
y.set_index("address").to_dict("index")
if "address" in y.columns else {}
)
for _, row in X.iterrows():
addr = row.get("address", None)
gt = y_by_addr.get(addr, {})
rent_val = float(gt.get("rent", 0) or 0) if gt else 0.0
price_val = float(gt.get("price", 0) or 0) if gt else 0.0
if rent_val <= 0 and price_val <= 0:
continue
city = row.get("city", "Unknown")
state = row.get("state", "Unknown")
property_type = row.get("property_type", "Unknown")
sqft = row.get("sqft", "N/A")
beds = row.get("bedrooms", row.get("beds", "N/A"))
baths = row.get("bathrooms", row.get("baths", "N/A"))
year_built = row.get("year_built", "N/A")
instr = (
f"Estimate AS OF 2026-04-11. Given this property: "
f"location={city}, {state}, type={property_type}, sqft={sqft}, "
f"beds={beds}, baths={baths}, year_built={year_built}. "
f"Estimate the monthly rent and sale price."
)
resp = json.dumps(
{"rent": round(rent_val, 2), "price": round(price_val, 2)},
)
pairs.append((instr, resp))
return pairs
# ── Per-task predict prompts (no labels) ──────────────────────────────────
def _t1_predict_prompt(
history: np.ndarray, lookback: int, horizon: int,
) -> str:
last = float(history[-1]) if len(history) else 0.0
mean = float(np.mean(history)) if len(history) else 0.0
std = float(np.std(history)) if len(history) else 0.0
denom = max(float(history[0]) if len(history) else 1e-2, 1e-2)
trend = float((history[-1] - history[0]) / denom * 100) if len(history) else 0.0
last20 = ", ".join(f"{v:.4f}" for v in history[-20:])
return (
f"You are a quantitative analyst. Predict the daily closing prices "
f"of the stock for each of the next {horizon} trading days, given:\n"
f"- Current close: ${last:.2f}\n"
f"- Past {lookback} closes: mean=${mean:.2f}, std=${std:.2f}, "
f"trend={trend:+.1f}%\n"
f"- Recent close series (last 20 of {lookback}): [{last20}]\n\n"
f"Reply with ONLY a JSON array of {horizon} floats, one per future "
f"trading day, in chronological order:\n"
f"[float, float, ..., float]"
)
# ── Dry-run engine for CPU-only smoke tests ──────────────────────────────
class _DryRunFTEngine:
"""Deterministic stand-in for an SFT-trained LLM during smoke tests.
Returns shape-correct placeholder responses so :meth:`predict` can be
exercised without HF / peft / GPU. The runner never sees this in
real runs (it injects a real OpenAIChatEngine pointed at a vLLM
LoRA-aware endpoint via ``--enable-lora``).
Exposes BOTH the legacy ``generate(prompt)`` hook AND the unified
``chat_complete(messages, ...)`` protocol so it slots into the same
code path the real OpenAIChatEngine uses.
"""
def __init__(self, marker: float = 1.0) -> None:
self.marker = float(marker)
self.model_id = "dry-run-ft"
def generate(self, prompt: str) -> str: # noqa: D401
lower = prompt.lower()
if "rent" in lower and "sale price" in lower:
return '{"rent": 2000, "price": 500000}'
if "xbrl" in lower:
return '{"Revenues": 1000000, "NetIncomeLoss": 100000}'
if "predict the stock return" in lower:
return f"Predicted return: {self.marker:.2f}%"
if "json array" in lower:
# T1 horizon-list forecast: try to recover horizon from prompt.
m = re.search(r"json array of (\d+) floats", lower)
horizon = int(m.group(1)) if m else 21
return "[" + ", ".join(
[f"{self.marker:.4f}"] * horizon
) + "]"
if "next" in lower and "closing prices" in lower:
# Legacy fallback: emit a 21-element horizon list.
return "[" + ", ".join([f"{self.marker:.4f}"] * 21) + "]"
return f"{self.marker:.4f}"
def chat_complete(
self,
messages: list[dict[str, str]],
*,
max_tokens: int = 256,
temperature: float = 0.0,
top_p: float = 1.0,
) -> str:
"""Adapt the unified chat-complete protocol to the legacy generate hook."""
try:
prompt = " ".join(
str(m.get("content", "")) for m in (messages or [])
)
except Exception:
prompt = ""
return self.generate(prompt)
def chat_complete_batch(
self,
batched_messages,
*,
max_tokens: int = 256,
temperature: float = 0.0,
top_p: float = 1.0,
) -> list[str]:
return [
self.chat_complete(msgs, max_tokens=max_tokens)
for msgs in batched_messages
]
# ── Main class ────────────────────────────────────────────────────────────
@register(
name="llm_finetuned",
family="llm_ft",
# Default: maximum coverage. The runner narrows to the post-hoc
# ZS-winner's coverage at config time per plan §9.
tasks={"T1", "T2", "T3", "T4", "T5", "T6", "T7"},
config_class=LLMFineTunedConfig,
)
class LLMFineTuned(_HFSaveMixin, Method):
"""LoRA SFT wrapper around any of the four MacroLens panel LLMs.
The base model is selected via ``LLMFineTunedConfig.model_id``; the
corresponding panel short-name (``llama_scout`` / ``gemma4`` /
``qwen35``) is recovered from the HF id when needed.
Parameters
----------
task : {"T1", ..., "T7"}
Task this instance is fitted for.
config : LLMFineTunedConfig | None
Hyperparameters (LoRA r/alpha, epochs, learning rate, ...).
Defaults to :meth:`default_config`.
base_model : {"llama_scout","gemma4","qwen35"} | None
Convenience override; if provided, sets
``config.model_id`` accordingly.
dry_run : bool
When True, fit / predict short-circuit to a CPU-only deterministic
stand-in for shape-only smoke testing. No HF / peft / torch GPU
is required. Default: False.
Notes
-----
The QLoRA SFT recipe is preserved verbatim from
:mod:`baselines.llm_finetune`: bnb NF4 4-bit quant, paged AdamW 8-bit,
LoRA on q/k/v/o projections, bf16 compute. The Gemma-4 special-case
(load full MM checkpoint, keep only ``language_model``) is applied
for ``base_model="gemma4"``.
Runner-side dispatch
--------------------
Per plan §9, the SFT base model is picked from the post-hoc-selected
ZS winner. :meth:`from_zs_winner` is the intended dispatch entrypoint:
it takes a per-method dict of zero-shot scores (lower-is-better) and
returns an instance with ``base_model`` set to the argmin.
"""
def __init__(
self,
*,
task: str,
config: LLMFineTunedConfig | None = None,
engine: Any = None,
base_model: Literal[
"llama_scout", "gemma4", "qwen35", None
] | None = None,
dry_run: bool = False,
**kwargs: Any,
) -> None:
if task not in self.tasks:
raise ValueError(
f"LLMFineTuned: task={task!r} not in supported set "
f"{sorted(self.tasks)}"
)
if config is None:
config = LLMFineTunedConfig(**kwargs) if kwargs else LLMFineTunedConfig()
elif kwargs:
merged = {**config.model_dump(), **kwargs}
config = LLMFineTunedConfig(**merged)
if base_model is not None:
mid = _BASE_MODEL_ID.get(base_model)
if mid is None:
raise ValueError(
f"base_model={base_model!r}; expected one of "
f"{sorted(_BASE_MODEL_ID)}"
)
config = LLMFineTunedConfig(
**{**config.model_dump(), "model_id": mid},
)
self.task = task
self.config = config
# Honor either an explicit ``dry_run`` ctor kwarg OR ``config.dry_run``
# (the smoke-test path sets the latter via ``cfg.model_copy(...)``).
self.dry_run = bool(dry_run) or bool(getattr(config, "dry_run", False))
# Adapter / tokenizer state (populated by fit / load).
self._adapter_dir: pathlib.Path | None = None
self._tokenizer_dir: pathlib.Path | None = None
self._model: Any = None
self._tokenizer: Any = None
# OpenAIChatEngine injected by the runner for HTTP-served LoRA
# inference. ``_dry_engine`` is the local fallback used when
# ``dry_run=True`` AND no real engine was supplied.
self.engine: Any = engine
self._dry_engine: _DryRunFTEngine | None = (
_DryRunFTEngine() if self.dry_run else None
)
# Per-task hints (mirrors :mod:`methods.llm_ts_reason`).
self._t1_close_idx: int | None = None
self._t1_horizon: int = 21
# T3/T6 fitted-field tracking: predict-time prompts need to declare
# the same field list the adapter was trained on. Populated by
# ``fit`` and used by ``_predict_t3_t6``.
self._fitted_fields_per_ticker: dict[str, list[str]] = {}
self._fitted_fields_global: list[str] = []
self.last_predict_meta: dict[str, Any] = {}
# ── fit (LoRA SFT) ───────────────────────────────────────────────────
@classmethod
def default_config(cls) -> LLMFineTunedConfig:
return LLMFineTunedConfig()
@classmethod
def from_zs_winner(
cls,
zs_results: dict[str, float],
*,
task: str,
config: LLMFineTunedConfig | None = None,
**kwargs: Any,
) -> "LLMFineTuned":
"""Construct an instance keyed to the zero-shot winner.
Parameters
----------
zs_results : dict[str, float]
Per-method primary-metric scores, e.g.
``{"llama_scout": 0.5, "gemma4": 0.4, ...}``. Lower-is-better:
the argmin is selected as the LoRA SFT base model.
task : str
Task this instance will be fitted for.
config, **kwargs
Forwarded to :class:`LLMFineTuned` along with the resolved
``base_model``.
Returns
-------
LLMFineTuned
An instance with ``base_model`` set to the argmin of
``zs_results`` (restricted to the four panel LLMs).
Raises
------
ValueError
If ``zs_results`` is empty or contains no recognised panel
short-names (``llama_scout``, ``gemma4``, ``qwen35``).
"""
if not zs_results:
raise ValueError(
"from_zs_winner: zs_results is empty; cannot pick a winner."
)
valid = {
k: float(v)
for k, v in zs_results.items()
if k in _BASE_MODEL_ID
}
if not valid:
raise ValueError(
f"from_zs_winner: zs_results keys {sorted(zs_results)} "
f"contain no recognised panel short-name; expected any of "
f"{sorted(_BASE_MODEL_ID)}."
)
winner = min(valid, key=lambda k: valid[k])
return cls(
task=task, config=config, base_model=winner, **kwargs,
)
def fit(self, X: Any, y: Any, *, seed: int = 42) -> "Method":
"""Run LoRA SFT on (X, y) framed as (instruction, response) pairs.
In ``dry_run=True`` mode this is a no-op (no HF deps needed). In
normal mode it loads the base model with bnb NF4, applies a LoRA
adapter, runs SFT via TRL's ``SFTTrainer``, and stores the
merged adapter dir on ``self._adapter_dir``.
"""
_seed_from_env(seed)
# Capture T1 horizon from y so ``predict`` emits matching length
# trajectories. The default ``_t1_horizon = 21`` is wrong for the
# canonical T1 task whose horizon is 252 trading days.
if self.task == "T1" and isinstance(y, np.ndarray) and y.ndim == 2:
self._t1_horizon = int(y.shape[1])
# Capture the T3/T6 fitted-field set so predict-time prompts can
# declare the same fields the adapter was trained on.
if self.task in ("T3", "T6"):
if isinstance(y, pd.DataFrame) and not y.empty and "field" in y.columns:
self._fitted_fields_per_ticker = {
str(t): sorted(grp["field"].astype(str).unique().tolist())
for t, grp in y.groupby("ticker", sort=False)
}
self._fitted_fields_global = sorted(
y["field"].astype(str).unique().tolist()
)
if self.dry_run:
return self
# Build (instruction, response) pairs for the task.
pairs = self._build_pairs(X, y)
if not pairs:
raise RuntimeError(
f"LLMFineTuned.fit({self.task}): no training pairs constructed."
)
texts = [
f"### Instruction:\n{instr}\n\n### Response:\n{resp}"
for instr, resp in pairs
]
try:
import torch
from transformers import (
AutoModelForCausalLM, AutoTokenizer,
BitsAndBytesConfig, TrainingArguments,
)
from peft import (
LoraConfig, get_peft_model, prepare_model_for_kbit_training,
)
from trl import SFTTrainer
from datasets import Dataset as HFDataset
except ImportError as exc:
raise RuntimeError(
"LLMFineTuned.fit requires transformers + peft + trl + "
f"bitsandbytes + datasets. Underlying error: {exc!r}"
) from exc
quant_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=torch.bfloat16,
)
model_id = self.config.model_id
# Gemma-4 special-case: full MM checkpoint → text-only language model.
if "gemma-4" in model_id.lower():
from transformers import (
Gemma4ForCausalLM,
Gemma4ForConditionalGeneration,
)
full_model = Gemma4ForConditionalGeneration.from_pretrained(
model_id,
quantization_config=quant_config,
device_map="auto",
trust_remote_code=True,
torch_dtype=torch.bfloat16,
attn_implementation="eager",
)
text_config = full_model.config.text_config
model = Gemma4ForCausalLM(text_config)
model.model = full_model.model.language_model
if hasattr(full_model, "lm_head"):
model.lm_head = full_model.lm_head
model.config._name_or_path = model_id
del full_model
else:
model = AutoModelForCausalLM.from_pretrained(
model_id,
quantization_config=quant_config,
device_map="auto",
trust_remote_code=True,
torch_dtype=torch.bfloat16,
attn_implementation="eager",
)
tokenizer = AutoTokenizer.from_pretrained(
model_id, trust_remote_code=True,
)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
model = prepare_model_for_kbit_training(model)
lora_cfg = LoraConfig(
r=self.config.lora_r,
lora_alpha=self.config.lora_alpha,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
model = get_peft_model(model, lora_cfg)
# Output dir under a process-local checkpoint root so multiple
# (task, seed) fits don't collide.
ckpt_root = pathlib.Path(
os.environ.get(
"MACROLENS_CHECKPOINT_ROOT",
str(pathlib.Path.home() / ".cache" / "macrolens" / "llm_ft"),
)
)
model_short = model_id.split("/")[-1].lower().replace("-", "_")
output_dir = ckpt_root / f"{model_short}_{self.task}_seed{seed}"
output_dir.mkdir(parents=True, exist_ok=True)
train_dataset = HFDataset.from_dict({"text": texts})
training_args = TrainingArguments(
output_dir=str(output_dir),
num_train_epochs=self.config.epochs,
per_device_train_batch_size=4,
gradient_accumulation_steps=8,
learning_rate=self.config.learning_rate,
weight_decay=0.01,
warmup_ratio=0.1,
logging_steps=50,
save_strategy="epoch",
save_total_limit=1,
report_to="none",
fp16=False,
bf16=True,
gradient_checkpointing=True,
optim="paged_adamw_8bit",
max_grad_norm=0.3,
seed=seed,
)
trainer = SFTTrainer(
model=model,
args=training_args,
train_dataset=train_dataset,
)
trainer.train()
# Persist the adapter inline so ``predict`` can re-load it with
# the same artifact ``save`` will expose downstream.
adapter_dir = output_dir / "adapter"
tokenizer_dir = output_dir / "tokenizer"
model.save_pretrained(str(adapter_dir))
tokenizer.save_pretrained(str(tokenizer_dir))
self._model = model
self._tokenizer = tokenizer
self._adapter_dir = adapter_dir
self._tokenizer_dir = tokenizer_dir
return self
def _build_pairs(self, X: Any, y: Any) -> list[tuple[str, str]]:
"""Dispatch to the per-task pair builder."""
if self.task == "T1":
X_arr = np.asarray(X, dtype=np.float32)
close_idx = (
self._t1_close_idx
if self._t1_close_idx is not None
else _find_close_idx_from_array(X_arr)
)
return _t1_pairs(X_arr, np.asarray(y, dtype=np.float32),
close_idx=close_idx)
if self.task in ("T2", "T5"):
return _t2_t5_pairs(X, np.asarray(y, dtype=np.float64),
task=self.task)
if self.task in ("T3", "T6"):
return _t3_t6_pairs(X, y, task=self.task)
if self.task == "T4":
return _t4_pairs(X, np.asarray(y, dtype=np.float32))
if self.task == "T7":
return _t7_pairs(X, y)
raise ValueError(f"Unknown task: {self.task!r}")
# ── predict (adapter inference) ──────────────────────────────────────
def predict(self, X: Any) -> np.ndarray | pd.DataFrame:
if self.task == "T1":
return self._predict_t1(X)
if self.task == "T2":
return self._predict_t2_t5(X, task="T2")
if self.task == "T3":
return self._predict_t3_t6(X, task="T3")
if self.task == "T4":
return self._predict_t4(X)
if self.task == "T5":
return self._predict_t2_t5(X, task="T5")
if self.task == "T6":
return self._predict_t3_t6(X, task="T6")
if self.task == "T7":
return self._predict_t7(X)
raise ValueError(f"Unknown task: {self.task!r}")
def _generate(self, prompt: str, *, max_new_tokens: int = 256) -> str:
"""Run a single-prompt generate.
Resolution order:
1. Injected ``self.engine`` (an
:class:`methods._openai_engine.OpenAIChatEngine` against a
vLLM ``--enable-lora`` endpoint serving the adapter as
``model_id``). Preferred path; HTTP, no GPUs in this process.
2. In-process ``self._model`` / ``self._tokenizer`` (set by
``fit`` / ``load`` with peft + bnb). Legacy path kept for
backwards compatibility when no HTTP endpoint is available.
3. ``self._dry_engine`` (when ``dry_run=True``).
"""
# Wrap instruction-tuned prompt envelope so the SFT-trained
# adapter sees the same text shape at fit and predict time.
full_prompt = f"### Instruction:\n{prompt}\n\n### Response:\n"
# 1. Real OpenAI-compatible engine (preferred).
if self.engine is not None and hasattr(self.engine, "chat_complete"):
messages = [{"role": "user", "content": full_prompt}]
return str(self.engine.chat_complete(
messages, max_tokens=max_new_tokens, temperature=0.0,
))
# 3. Dry-run fallback (smoke tests).
if self.dry_run:
assert self._dry_engine is not None
return self._dry_engine.generate(prompt)
# 2. In-process peft model fallback.
if self._model is None or self._tokenizer is None:
raise RuntimeError(
"LLMFineTuned.predict: no engine injected, model/tokenizer "
"not loaded, and dry_run=False. Either inject an "
"OpenAIChatEngine via the engine= ctor kwarg, call "
".fit(...) / .load(...) first, or set dry_run=True."
)
import torch
inputs = self._tokenizer(
full_prompt, return_tensors="pt", truncation=True, max_length=2048,
)
dev = next(self._model.parameters()).device
inputs = {k: v.to(dev) for k, v in inputs.items()}
with torch.no_grad():
outputs = self._model.generate(
**inputs,
max_new_tokens=max_new_tokens,
do_sample=False,
pad_token_id=self._tokenizer.eos_token_id,
)
return self._tokenizer.decode(
outputs[0][inputs["input_ids"].shape[1]:],
skip_special_tokens=True,
)
# ── Per-task predictors (mirror :mod:`methods.llm_ts_reason`) ──
def _predict_t1(self, X: np.ndarray) -> np.ndarray:
if not isinstance(X, np.ndarray) or X.ndim != 3:
raise ValueError(
f"T1 X must be (N, lookback, F) np.ndarray, got "
f"shape={getattr(X, 'shape', None)} type={type(X).__name__}"
)
n, lookback, _ = X.shape
horizon = int(self._t1_horizon)
if n == 0:
self.last_predict_meta = {"task": "T1", "n_attempted": 0,
"n_parse_errors": 0}
return np.zeros((0, horizon), dtype=np.float32)
close_idx = (
self._t1_close_idx
if self._t1_close_idx is not None
else _find_close_idx_from_array(X)
)
preds = np.full((n, horizon), np.nan, dtype=np.float32)
n_errors = 0
# Trajectory output requires more tokens than a single scalar:
# budget ~12 tokens per horizon step plus brackets/separators.
max_new_tokens = max(64, 12 * horizon + 16)
for i in range(n):
history = X[i, :, close_idx]
prompt = _t1_predict_prompt(history, lookback, horizon)
response = self._generate(prompt, max_new_tokens=max_new_tokens)
traj = _parse_horizon_list(response, horizon)
if traj is None:
n_errors += 1
continue
preds[i, :] = traj
self.last_predict_meta = {
"task": "T1", "n_attempted": int(n),
"n_parse_errors": int(n_errors),
"horizon": horizon, "close_idx": int(close_idx),
}
return preds
def _predict_t2_t5(
self, X: pd.DataFrame, *, task: str,
) -> np.ndarray:
if not isinstance(X, pd.DataFrame):
raise ValueError(
f"{task} X must be a DataFrame, got {type(X).__name__}"
)
n = len(X)
if n == 0:
self.last_predict_meta = {"task": task, "n_attempted": 0,
"n_parse_errors": 0}
return np.zeros(0, dtype=np.float32)
if task == "T2":
instructions = []
for _, row in X.iterrows():
sector = row.get("sector", "Unknown")
revenue = _safe_float(row.get("stmt_revenue", 0))
net_income = _safe_float(row.get("stmt_net_income", 0))
total_assets = _safe_float(row.get("stmt_total_assets", 0))
employees = row.get("fullTimeEmployees", "N/A")
instructions.append(
f"You are a financial analyst. Estimate the total "
f"equity market capitalization of this company.\n\n"
f"Sector: {sector}\nRevenue: ${revenue:,.0f}\n"
f"Net Income: ${net_income:,.0f}\n"
f"Total Assets: ${total_assets:,.0f}\n"
f"Employees: {employees}"
)
else:
# Match `_t2_t5_pairs` (T5 branch) exactly.
stmt_cols = [c for c in X.columns if c.startswith("stmt_")]
instructions = []
for _, row in X.iterrows():
sector = row.get("sector", "Unknown")
industry = row.get("industry", "Unknown")
items = []
for c in stmt_cols:
val = row.get(c)
if pd.notna(val):
try:
items.append(f"{c}: ${float(val):,.0f}")
except (TypeError, ValueError):
continue
block = (
"\n".join(items) if items
else "No financial statement data available"
)
instructions.append(
f"You are a private equity analyst. Given ONLY financial "
f"statement data (no market price), estimate the market "
f"capitalization of this company.\n\n"
f"Sector: {sector}\nIndustry: {industry}\n{block}"
)
preds = np.full(n, np.nan, dtype=np.float64)
n_errors = 0
for i, prompt in enumerate(instructions):
response = self._generate(prompt, max_new_tokens=64)
v = _parse_first_number(response)
if v is None or v <= 0:
n_errors += 1
continue
preds[i] = float(v)
self.last_predict_meta = {
"task": task, "n_attempted": int(n),
"n_parse_errors": int(n_errors),
}
return preds
def _predict_t3_t6(
self, X: pd.DataFrame, *, task: str,
) -> pd.DataFrame:
if not isinstance(X, pd.DataFrame):
raise ValueError(
f"{task} X must be a DataFrame, got {type(X).__name__}"
)
n = len(X)
if n == 0:
self.last_predict_meta = {"task": task, "n_attempted": 0,
"n_parse_errors": 0}
return pd.DataFrame(
columns=["ticker", "fiscal_year", "field", "pred"]
)
# Field set: per-ticker if fitted, else global, else default panel.
global_fields = (
self._fitted_fields_global
or list(_DEFAULT_T3_T6_FIELDS)
)
rows: list[dict[str, Any]] = []
n_errors = 0
for _, row in X.iterrows():
ticker = str(row.get("ticker", "?"))
fy = row.get("fiscal_year", None)
fields_for_row = (
self._fitted_fields_per_ticker.get(ticker)
or global_fields
)
fields_str = ", ".join(fields_for_row)
if task == "T3":
sector = row.get("sector", "Unknown")
revenue = _safe_float(row.get("stmt_revenue", 0))
net_income = _safe_float(row.get("stmt_net_income", 0))
# Match `_t3_pairs` exactly so the adapter sees the same
# instruction text at predict time as it did during SFT.
instr = (
f"You are a financial analyst. Given {ticker}'s known "
f"fundamentals (sector={sector}, revenue=${revenue:,.0f}, "
f"net_income=${net_income:,.0f}), predict these XBRL "
f"fields: [{fields_str}]"
)
else:
description = row.get(
"company_description", f"A company with ticker {ticker}",
)
sector = row.get("sector", "Unknown")
industry = row.get("industry", "Unknown")
# Match `_t6_pairs` exactly.
instr = (
f"Given this company description: '{description}', "
f"sector: '{sector}', industry: '{industry}', generate "
f"plausible financial statement values for these XBRL "
f"fields: [{fields_str}]"
)
response = self._generate(instr, max_new_tokens=512)
parsed = _extract_json_object(response)
if parsed is None:
n_errors += 1
continue
for field, val in parsed.items():
try:
rows.append({
"ticker": ticker, "fiscal_year": fy,
"field": str(field), "pred": float(val),
})
except (TypeError, ValueError):
continue
self.last_predict_meta = {
"task": task, "n_attempted": int(n),
"n_parse_errors": int(n_errors),
}
return pd.DataFrame(
rows, columns=["ticker", "fiscal_year", "field", "pred"]
)
def _predict_t4(self, X: Any) -> np.ndarray:
if isinstance(X, dict):
event_type = np.asarray(X.get("event_type", []))
event_desc = np.asarray(X.get("event_description", []))
elif isinstance(X, pd.DataFrame):
event_type = (
X["event_type"].to_numpy()
if "event_type" in X.columns else np.array([])
)
event_desc = (
X["event_description"].to_numpy()
if "event_description" in X.columns
else np.array([""] * len(event_type))
)
else:
raise ValueError(
f"T4 X must be DataFrame or dict, got {type(X).__name__}"
)
n = int(len(event_type))
if n == 0:
self.last_predict_meta = {"task": "T4", "n_attempted": 0,
"n_parse_errors": 0}
return np.zeros(0, dtype=np.float32)
preds = np.full(n, np.nan, dtype=np.float32)
n_errors = 0
for i in range(n):
et_s = str(event_type[i]) if event_type[i] is not None else "unknown"
ed_s = str(event_desc[i])[:200] if event_desc[i] is not None else ""
instr = (
f"You are a financial analyst. Given the scenario:\n"
f"- Event type: {et_s}\n"
+ (f"- Description: {ed_s}\n" if ed_s else "")
+ "\nPredict the stock return (%) following this event."
)
response = self._generate(instr, max_new_tokens=64)
v = _parse_first_number(response)
if v is None:
n_errors += 1
continue
preds[i] = float(v)
self.last_predict_meta = {
"task": "T4", "n_attempted": int(n),
"n_parse_errors": int(n_errors),
}
return preds
def _predict_t7(self, X: pd.DataFrame) -> pd.DataFrame:
if not isinstance(X, pd.DataFrame):
raise ValueError(
f"T7 X must be a DataFrame, got {type(X).__name__}"
)
n = len(X)
if n == 0:
self.last_predict_meta = {"task": "T7", "n_attempted": 0,
"n_parse_errors": 0}
return pd.DataFrame(
columns=["address", "pred_rent", "pred_price"]
)
rows: list[dict[str, Any]] = []
n_errors = 0
for _, row in X.iterrows():
addr = row.get("address", None)
city = row.get("city", "Unknown")
state = row.get("state", "Unknown")
property_type = row.get("property_type", "Unknown")
sqft = row.get("sqft", "N/A")
beds = row.get("bedrooms", row.get("beds", "N/A"))
baths = row.get("bathrooms", row.get("baths", "N/A"))
year_built = row.get("year_built", "N/A")
instr = (
f"Estimate AS OF 2026-04-11. Given this property: "
f"location={city}, {state}, type={property_type}, "
f"sqft={sqft}, beds={beds}, baths={baths}, "
f"year_built={year_built}. Estimate the monthly rent and "
f"sale price."
)
response = self._generate(instr, max_new_tokens=128)
parsed = _extract_json_object(response)
if parsed is None:
n_errors += 1
rows.append({"address": addr, "pred_rent": np.nan,
"pred_price": np.nan})
continue
ci = {str(k).lower(): v for k, v in parsed.items()}
try:
rent_val = float(ci.get("rent", 0) or 0)
except (TypeError, ValueError):
rent_val = np.nan
try:
price_val = float(ci.get("price", 0) or 0)
except (TypeError, ValueError):
price_val = np.nan
rows.append({"address": addr, "pred_rent": rent_val,
"pred_price": price_val})
self.last_predict_meta = {
"task": "T7", "n_attempted": int(n),
"n_parse_errors": int(n_errors),
}
return pd.DataFrame(
rows, columns=["address", "pred_rent", "pred_price"]
)
# ── HF save / load hooks ─────────────────────────────────────────────
def _manifest(self) -> dict[str, Any]:
m = super()._manifest()
adapter_sha = ""
if self._adapter_dir is not None and self._adapter_dir.exists():
adapter_sha = _sha256_dir(self._adapter_dir)
m["sha256s"] = {"adapter": adapter_sha}
return m
def _hf_save(self, path: pathlib.Path) -> None:
"""Persist the LoRA adapter + tokenizer to ``path``.
Layout::
path/manifest.json # Method ABC
path/adapter/ # peft.PeftModel.save_pretrained
path/tokenizer/ # tokenizer.save_pretrained
path/adapter.sha256 # plain-text hash recorded in manifest
"""
if self.dry_run or self._adapter_dir is None:
# Dry-run mode: write a placeholder so load() can detect mode.
(path / "DRY_RUN").write_text("1\n")
return
import shutil
target_adapter = path / "adapter"
if target_adapter.exists():
shutil.rmtree(target_adapter)
shutil.copytree(self._adapter_dir, target_adapter)
if self._tokenizer_dir is not None and self._tokenizer_dir.exists():
target_tok = path / "tokenizer"
if target_tok.exists():
shutil.rmtree(target_tok)
shutil.copytree(self._tokenizer_dir, target_tok)
sha = _sha256_dir(target_adapter)
(path / "adapter.sha256").write_text(sha + "\n")
def _hf_load(self, path: pathlib.Path) -> None:
"""Reload adapter + tokenizer; rebuild a merged model in memory.
In dry_run mode this short-circuits (the placeholder marker file
is detected and ``self._dry_engine`` is re-established).
"""
if (path / "DRY_RUN").exists():
self.dry_run = True
self._dry_engine = _DryRunFTEngine()
return
try:
import torch
from transformers import (
AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig,
)
from peft import PeftModel
except ImportError as exc:
raise RuntimeError(
"LLMFineTuned.load requires transformers + peft + "
f"bitsandbytes. Underlying error: {exc!r}"
) from exc
adapter_dir = path / "adapter"
tokenizer_dir = path / "tokenizer"
if not adapter_dir.exists():
raise FileNotFoundError(
f"adapter directory missing at {adapter_dir}"
)
quant_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=torch.bfloat16,
)
model_id = self.config.model_id
if "gemma-4" in model_id.lower():
from transformers import Gemma4ForCausalLM
base = Gemma4ForCausalLM.from_pretrained(
model_id, quantization_config=quant_config,
device_map="auto", trust_remote_code=True,
torch_dtype=torch.bfloat16, attn_implementation="eager",
)
else:
base = AutoModelForCausalLM.from_pretrained(
model_id, quantization_config=quant_config,
device_map="auto", trust_remote_code=True,
torch_dtype=torch.bfloat16, attn_implementation="eager",
)
peft_model = PeftModel.from_pretrained(base, str(adapter_dir))
tok_src = tokenizer_dir if tokenizer_dir.exists() else model_id
tokenizer = AutoTokenizer.from_pretrained(
str(tok_src), trust_remote_code=True,
)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
self._model = peft_model
self._tokenizer = tokenizer
self._adapter_dir = adapter_dir
self._tokenizer_dir = tokenizer_dir if tokenizer_dir.exists() else None
__all__ = ["LLMFineTuned"]
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