This is the selected LoRA adapter from the qwen35-4b-sft-factory-longhorizon-20260711 campaign. It is an experimental data-engineering agent checkpoint, not a claimed Spider 2.0 solution.

Immutable provenance

Quaero commit: f7bdf119fb573577756da28978e39d28bcfdb5b0
Retrain commit: 00f8982c3ebff6faaaafc1cfeb069009f424ac5f
Base revision: 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a
Adapter SHA-256: a8504dc05c3151818ff9988847c220be730242a34e0d406b42e1f8d11344008a
Training corpus: 10,034 machine-verifiable factory trajectories
W&B: https://wandb.ai/teilo/quaero/runs/2thgo6lc

Training initially failed at step 38 with microbatch size 2. It restarted at microbatch size 1 with gradient checkpointing from adapter weights only; optimizer, scaler, and RNG state were not restored. This weakens exact-continuation claims and is intentionally disclosed.

Frozen factory selection gate

Same 8 tasks, four rollouts each, seed 123, 64 turns, 512 action tokens, temperature 0.8, top-p 0.95, no oracle:

Candidate	Passed
Raw base	0/32
Previous adapter	10/32
Step 628	16/32
Step 1256	21/32
Step 1884	19/32
Final	24/32

The final checkpoint was the unique gate leader and was selected before Spider evaluation.

Spider 2.0-DBT dev evaluation

The single scoreable run on the predeclared 16-task dev split scored 0/16 with the official scorer. All 16 tasks terminated on repeated-action detection and none submitted a result. Therefore this artifact is a negative benchmark result despite its factory-gate improvement. Do not infer leaderboard competitiveness from the factory score.

The companion private evaluation repository contains the frozen selection record, transcripts, official score, diagnostics, taxonomy, preflight, and operational incident logs. No Spider heldout task was used for training or checkpoint selection.

Measured failure modes

Under the 32-episode factory gate, the selected checkpoint submitted 27/32 episodes with 24 positive rewards, 4 repeated-action cutoffs, 0 token-cap hits, and 0 turn exhaustions. On the 16-task Spider dev split, all 16 episodes ended in repeated-action cutoffs with no submission.

Usage

This adapter is an agent policy for the quaero openenv_v1 dbt/DuckDB tool contract (list_files, read_file, query, build_spec, write_file, dbt, validate, submit). It emits JSON tool actions and expects environment observations in return; it is not a general chat or one-shot text-to-SQL model. Generate with enable_thinking=False.

The environment implementing this contract is open source at github.com/teilomillet/quaero-dbt — including UserWorkspaceRuntime for driving this adapter against your own dbt project, and the verifier that mirrors the official Spider 2.0-DBT scoring semantics.

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

BASE_REV = "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a"
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-4B", revision=BASE_REV)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-4B", revision=BASE_REV)
model = PeftModel.from_pretrained(base, "teilomillet/quaero-qwen35-4b-sft-factory-longhorizon-20260711")

Context

The full story — why this adapter climbs the synthetic factory gate and still scores zero on real Spider 2.0-DBT instances, and what that measures — is written up at teilo.xyz/posts/quaero-spider2-negative-result.