Sherlock-4B-QLoRA

Sherlock-4B-QLoRA

Work in progress: This adapter is still under active development.

QLoRA adapter for structured information extraction: (JSON schema + text) → JSON. Missing fields become null; unrelated text is ignored.

• Base model: Qwen/Qwen3-4B-Instruct-2507
• Training dataset: derogab/Sherlock-Case-Files
• Method: NF4 QLoRA (rank 16, alpha 32, dropout 0.05)
• Task: text-generation

Usage

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507")
model = PeftModel.from_pretrained(base, "derogab/Sherlock-4B-QLoRA")
tokenizer = AutoTokenizer.from_pretrained("derogab/Sherlock-4B-QLoRA")

Benchmark

Sherlock is evaluated against the base model on structured extraction quality. Rates are percentages; Δ is in percentage points (higher is better). The 95% CI of Δ is a Newcombe score interval from the aggregate counts.

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