A LoRA adapter for meta-llama/Llama-3.1-8B-Instruct, fine-tuned on the Spider text-to-SQL benchmark dataset. Built end-to-end on a single NVIDIA Grace Blackwell GB10 (DGX Spark, 128 GB unified memory) with the same NF4 train, NVFP4 serve, EAGLE-3 speculative decoding stack used across the Pylox Forge portfolio.

This is a small-dataset seed adapter intended as a portfolio demonstration of code-domain fine-tuning. The fine-tune-quality measurement against the base model is below the 50/50 line on this volume of data (see Evaluation), and is documented honestly. A larger refresh round on real schema-grounded query pairs is the recommended path to production-grade text-to-SQL.

Model details

Adapter: LoRA (PEFT, rank 32, alpha 64, dropout 0.1)
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Base model: meta-llama/Llama-3.1-8B-Instruct
Recommended serving base: nvidia/Llama-3.1-8B-Instruct-NVFP4
Speculative head: RedHatAI/Llama-3.1-8B-Instruct-speculator.eagle3
License: Llama 3.1 Community License
Hardware: NVIDIA Grace Blackwell GB10 (DGX Spark, 128 GB UMA)

Training data and technique

Source: xlangai/spider (cross-domain text-to-SQL benchmark)
Examples after preprocessing: 697
Format: Standard chat messages format with assistant-only loss; schema injected into the system prompt, natural-language question in the user turn, SQL query as the assistant target
Method: NF4 QLoRA SFT (4-bit NormalFloat base, bfloat16 compute, double quantization)
Hyperparameters: 3 epochs, cosine LR (2e-4 peak), max_seq_length 2048, sequence packing enabled, NEFTune noise alpha 5, paged_adamw_8bit optimizer, gradient accumulation 16

Evaluation

Numbers are pulled directly from the local benchmark JSON. No invented values.

Throughput on Grace Blackwell GB10

Metric	Value
Sustained throughput	25.9 tok/s
Single-user throughput	39.6 tok/s
Concurrent batch-8 throughput	270.1 tok/s
TTFT p50	414.4 ms
TTFT p95	514.3 ms
End-to-end latency p50	3852 ms

Cost (NVFP4 serving)

Metric	Value
Cost per 1M output tokens	$0.5363
Comparable OpenAI GPT-4o output	$10 per 1M
Savings factor	18.6x cheaper than GPT-4o

Capability preservation (academic, lm-evaluation-harness, 500 samples each)

Benchmark	Score
HellaSwag (common sense)	78.0%
TruthfulQA (hallucination resistance)	30.0%
MMLU-Pro	not run

Fine-tune quality vs base (LLM judge, pairwise)

Metric	Value
Win rate vs `meta-llama/Llama-3.1-8B-Instruct`	14.0%

Honest read: at 697 training examples the adapter does not beat the base model in pairwise judge quality. Spider EM (execution match) on the held-out dev set has not yet been measured against an actual database; that is the appropriate domain-specific eval and is the recommended next step. The pipeline runs end-to-end and the artifacts ship, but production text-to-SQL on a real schema requires a substantially larger and more schema-diverse corpus.

Safety (with Pylox safety gateway, 50-prompt red team)

Metric	Value
Adversarial block rate	75.56%
False positive rate on benign controls	20.0%

The 20% false positive rate on benign controls indicates over-blocking on benign analytical SQL prompts. A DPO alignment pass with refusal-behavior pairs that distinguishes "DROP TABLE users" (block) from "SELECT count(*) FROM users WHERE deleted = true" (allow) would reduce this. Recommended next step before this adapter routes real production traffic.

Quickstart

PEFT (research / batch)

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

base_id = "meta-llama/Llama-3.1-8B-Instruct"
adapter_id = "pyloxsystems/sql-spider-llama-3.1-8b-lora"

tokenizer = AutoTokenizer.from_pretrained(base_id)
model = AutoModelForCausalLM.from_pretrained(
    base_id, torch_dtype=torch.bfloat16, device_map="auto"
)
model = PeftModel.from_pretrained(model, adapter_id)

schema = """
CREATE TABLE orders (id INT PRIMARY KEY, customer_id INT, total NUMERIC, created_at TIMESTAMP);
CREATE TABLE customers (id INT PRIMARY KEY, name TEXT, country TEXT);
"""
messages = [
    {"role": "system", "content": f"You translate natural language to PostgreSQL SQL.\n\nSchema:\n{schema}"},
    {"role": "user", "content": "Total revenue from Canadian customers in the last 30 days?"},
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=256, do_sample=False)
print(tokenizer.decode(out[0], skip_special_tokens=True))

vLLM with NVFP4 base and EAGLE-3 speculative decoding

bash

vllm serve nvidia/Llama-3.1-8B-Instruct-NVFP4 \
    --enable-lora \
    --lora-modules sql-spider=pyloxsystems/sql-spider-llama-3.1-8b-lora \
    --speculative-config '{
        "method": "eagle3",
        "model": "RedHatAI/Llama-3.1-8B-Instruct-speculator.eagle3",
        "num_speculative_tokens": 5
    }'

Intended use

Translate natural-language questions to SQL against a provided schema
Generalize to schemas unseen during training (Spider's distribution)
PostgreSQL / SQLite / MySQL syntax
Pipeline demonstration for evaluating the Pylox Forge stack on a code vertical

Out of scope

Mutating queries (DELETE / DROP / TRUNCATE) without an external read-only role enforcement
Schemas with deeply nested JSON / JSONB columns (untested generalization)
Dialect-heavy SQL: Snowflake-specific, BigQuery-specific, Redshift-specific without a refresh round
Production routing without an execution sandbox and a query validator
Production text-to-SQL without a substantially larger fine-tune corpus

Limitations

697 training examples is small relative to production-grade text-to-SQL fine-tunes. Fine-tune quality vs base is below the 50/50 line.
Spider's training distribution is primarily Postgres and SQLite; weak on schemas with complex nested types or non-relational columns.
Spider EM has not yet been measured. Recommended next step.
20% false positive rate on benign safety controls. DPO refresh recommended.
No built-in execution sandbox. Orchestrator must validate before running.

License

Inherits the Llama 3.1 Community License from the base model.

Citation

bibtex

@misc{pylox_sql_spider_2026,
  author       = {Girard, Emilio},
  title        = {Pylox Text-to-SQL 8B (sql-spider)},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/pyloxsystems/sql-spider-llama-3.1-8b-lora}}
}

Pylox Forge is a solo-operated LLM fine-tuning lab on NVIDIA Grace Blackwell. Site: pyloxforge.com. Other adapters: pyloxsystems on Hugging Face.