Text Generation
PEFT
Safetensors
Turkish
English
text-to-sql
turkish
banking
risk-management
lora
distillation
small-language-model
slm-rd
Eval Results (legacy)
Instructions to use eqlx-ai/tr-risk-sql-gemma-e4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use eqlx-ai/tr-risk-sql-gemma-e4b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-4-E4B-it") model = PeftModel.from_pretrained(base_model, "eqlx-ai/tr-risk-sql-gemma-e4b") - Notebooks
- Google Colab
- Kaggle
File size: 6,927 Bytes
ae943b1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 | """Inference loop around the served model: execute, repair, vote. No training, no reference answers.
For a sql case the loop does what a deployed system can do with its own database:
1. ask the model for k candidate queries (k=1: one query),
2. check each: one SELECT, transpiles to Oracle/PostgreSQL, runs on the world's first database,
3. a candidate that fails gets the error message back and up to `repairs` more tries,
4. among the candidates that run, the result most of them agree on wins (ties: the earliest).
Report cases (summary + chart) pass through with one call. The runner only writes outputs
({id, sample, output, latency_s}) and a trace; `evalrun.py score` scores them with the frozen verifier.
uv run python code/infer_loop.py --cases eval/cases/dev.jsonl --endpoint http://localhost:18000/v1 --model rft1 \
--out runs/loop/k1r2/outputs.jsonl --n 4 --k 1 --repairs 2
"""
from __future__ import annotations
import argparse
import json
import sys
import time
import urllib.request
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
ROOT = Path(__file__).resolve().parent.parent
sys.path.insert(0, str(ROOT / "eval"))
import verify as V
import worldlib
REPAIR = ("Bu sorgu çalışmadı. Hata: {error}\n"
"Şemaya ve kurallara uyan, düzeltilmiş TEK bir SELECT sorgusu yaz. Cevabı yalnızca bir ```sql bloğu içinde ver.")
def check(world: str, output: str) -> tuple[str | None, list | None]:
"""(error, rows): the error a deployed system would see for this answer, or the rows it returns."""
sql = V.extract_sql(output)
if sql is None:
return "cevapta SELECT sorgusu yok", None
why = V.check_statement(sql)
if why:
return why, None
try:
return None, worldlib.run(world, worldlib.SEEDS[0], sql)[2]
except worldlib.QueryError as e:
return str(e), None
def result_key(rows: list) -> str:
"""Two queries vote together when they return the same cells, whatever the row and column order."""
def cell(v):
v = worldlib.norm(v)
return f"{v:.9g}" if isinstance(v, float) else repr(v)
return json.dumps(sorted(sorted(cell(v) for v in r) for r in rows))
def solve_sql(chat, messages: list, world: str, k: int = 1, repairs: int = 0, temperature: float = 0.0) -> tuple[str, dict]:
"""chat(messages, temperature) -> text. Returns the chosen answer and a trace of every step."""
trace: dict = {"candidates": []}
valid = []
for i in range(k):
out = chat(messages, temperature)
err, rows = check(world, out)
steps = [{"output": out, "error": err}]
tries = 0
while err and tries < repairs:
tries += 1
out = chat([*messages, {"role": "assistant", "content": out}, {"role": "user", "content": REPAIR.format(error=err[:300])}], temperature)
err, rows = check(world, out)
steps.append({"output": out, "error": err})
trace["candidates"].append(steps)
if err is None and rows is not None:
valid.append((i, out, result_key(rows)))
if not valid:
trace["chosen"] = None
return trace["candidates"][0][-1]["output"], trace
votes: dict = {}
for _, _, key in valid:
votes[key] = votes.get(key, 0) + 1
i, out, key = max(valid, key=lambda c: (votes[c[2]], -c[0]))
trace["chosen"], trace["votes"], trace["valid"] = i, votes[key], len(valid)
return out, trace
def make_chat(endpoint: str, model: str, max_tokens: int, top_p: float, timeout: float = 180.0):
url = endpoint.rstrip("/") + "/chat/completions"
def chat(messages: list, temperature: float) -> str:
body = {"model": model, "messages": messages, "temperature": temperature, "top_p": top_p, "max_tokens": max_tokens,
"chat_template_kwargs": {"enable_thinking": False}}
req = urllib.request.Request(url, data=json.dumps(body).encode(), headers={"Content-Type": "application/json"})
for attempt in range(3):
try:
with urllib.request.urlopen(req, timeout=timeout) as r:
return json.loads(r.read())["choices"][0]["message"].get("content") or ""
except Exception:
if attempt == 2:
raise
time.sleep(2 * (attempt + 1))
return ""
return chat
def run_case(chat, case: dict, sample: int, k: int, repairs: int, temperature: float) -> tuple[dict, dict]:
t0 = time.time()
if case.get("kind") == "sql":
out, trace = solve_sql(chat, case["messages"], case["world"], k, repairs, temperature)
else:
out, trace = chat(case["messages"], temperature), {}
row = {"id": case["id"], "sample": sample, "output": out, "latency_s": round(time.time() - t0, 3)}
return row, {"id": case["id"], "sample": sample, **trace}
def main():
ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
ap.add_argument("--cases", required=True)
ap.add_argument("--endpoint", required=True)
ap.add_argument("--model", required=True)
ap.add_argument("--out", required=True, help="outputs.jsonl; the trace goes next to it as trace.jsonl")
ap.add_argument("--n", type=int, default=1, help="scored samples per case")
ap.add_argument("--k", type=int, default=1, help="candidates per sample (voting when > 1)")
ap.add_argument("--repairs", type=int, default=0)
ap.add_argument("--temperature", type=float, default=1.0)
ap.add_argument("--top-p", type=float, default=0.95)
ap.add_argument("--max-tokens", type=int, default=1024)
ap.add_argument("--concurrency", type=int, default=32)
a = ap.parse_args()
cases = [json.loads(line) for line in Path(a.cases).read_text().splitlines() if line.strip()]
for w in sorted({c["world"] for c in cases if c.get("kind") == "sql"}):
worldlib.connect(w, worldlib.SEEDS[0]) # build the databases before the threads start
chat = make_chat(a.endpoint, a.model, a.max_tokens, a.top_p)
jobs = [(c, s) for c in cases for s in range(a.n)]
with ThreadPoolExecutor(a.concurrency) as ex:
done = list(ex.map(lambda j: run_case(chat, j[0], j[1], a.k, a.repairs, a.temperature), jobs))
out = Path(a.out)
out.parent.mkdir(parents=True, exist_ok=True)
out.write_text("".join(json.dumps(r, ensure_ascii=False) + "\n" for r, _ in done))
(out.parent / "trace.jsonl").write_text("".join(json.dumps(t, ensure_ascii=False) + "\n" for _, t in done))
sql = [t for _, t in done if "candidates" in t]
print(json.dumps({"outputs": len(done), "sql": len(sql), "repaired": sum(any(len(c) > 1 for c in t["candidates"]) for t in sql),
"no_valid_candidate": sum(t["chosen"] is None for t in sql), "k": a.k, "repairs": a.repairs, "temperature": a.temperature}))
if __name__ == "__main__":
main()
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