Claude Opus 5.5

Link the HF Article write-up

52cf7fc verified about 3 hours ago

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license: cc-by-nc-4.0
base_model: CohereLabs/tiny-aya-global
library_name: peft
pipeline_tag: text-generation
language:
  - en
tags:
  - text-to-sql
  - agent
  - tool-use
  - lora
  - qlora
  - agenttune
  - auditkit
datasets:
  - satish9177/tiny-aya-sql-agent-data
  - trl-lab/SQaLe-2-text-to-SQL-Queries

Tiny Aya SQL agent (A1500, supervised fine-tuning only)

A LoRA adapter for Cohere's Tiny Aya Global, trained with QLoRA on 1,500 verified multi-turn SQL agent episodes through Lexsi AgentTune.

Its role: the plain fine-tuning model, the starting point for the submitted model B. B adds one round of self-training.
Which to use: use B for the submission's results, and this one to compare plain fine-tuning with B.
Write-up: Teaching Tiny Aya to query databases (HF Article)
Code, plans and full results: github.com/satish9177/aya-db-agent
Data, trajectories and logs: satish9177/tiny-aya-sql-agent-data

Results (strict)

Set	Base (untuned)	A1500 (this model)	B
SQaLe dev (95)	2	37	40
SQaLe final (144, unseen databases)	2.8%	31.3%	30.6%
BIRD Mini-Dev (493)	0.0%	1.6%	1.4%
Shop retention, dev (40)	0	40	40
Shop retention, multi-hop (40)	0	1	0

On the final sets: A1500 and B are the same within noise.
Behaviour: the same strengths and limits as B. It is a clean tool user for one- and two-table questions. It is weak on 3+ tables, multi-step questions and large schemas, and it never recovers after a failed query.

Training


Data	1,500 verified episodes: 1,265 SQaLe questions + 235 shop-database episodes (1,425 train / 75 validation)
How the episodes were made	the reference query path replayed against the real database; no teacher model
Quantization	4-bit NF4, double quantization, fp16 compute
LoRA	rank 16, alpha 32, dropout 0.05; q, k, v, o, gate, up and down projections
Optimization	2 epochs, learning rate 2e-4, effective batch 16, 180 updates; inputs up to 4,096 tokens
Loss	the model's own turns only; tool outputs are never trained
Hardware and cost	one Tesla T4; training plus dev check about $2.36 by rate

Training records are in training/.

How to load

Use the same code as on the B model card, with "satish9177/tiny-aya-sql-agent-a1500" as the adapter. The base model is pinned at revision af89d219b53ed9b13b8a4645f9c8028973510324. Load the tokenizer from its tokenizer.json, as that card explains.

Licence and credits

Licence: CC-BY-NC-4.0, following Tiny Aya Global by Cohere Labs. Non-commercial use only.
AgentTune and AuditKIT: Lexsi Labs, source-available and non-commercial.
SQaLe: Wolff, Gomm and Hulsebos (2026). Its card declares MIT.