metadata
tags:
- unsprawl
- lora
- qlora
- legal-ai
- entity-extraction
- judge
license: agpl-3.0
base_model: unsloth/Qwen3-4B-Instruct-2507-bnb-4bit
library_name: peft
pipeline_tag: text-generation
Unsprawl-4B-SFT
Fine-tuned LoRA adapter for the Unsprawl platform's local inference pipeline.
Training
- Base model: Qwen3-4B-Instruct (4-bit quantized via Unsloth)
- Method: QLoRA (r=16, alpha=32, 0.81% trainable params)
- Data: 195 curated pairs (entity extraction, strategy generation, quality judging, tone classification)
- Training: 1 epoch, batch size 8, AdamW 8-bit, lr=2e-4
- Final loss: 1.69 (from 2.65 starting)
- Hardware: NVIDIA RTX 3060 12GB, 164 seconds
Tasks
| Task | Description |
|---|---|
| Entity extraction | Structured JSON entity extraction from legal documents |
| Quality judging | 5-dimension scoring with APPROVE/REJECT verdicts |
| Tone classification | 6-category tone classification (NEUTRAL, INFLAMMATORY, etc.) |
| Claim verification | Factual claim extraction for KG cross-reference |
Usage
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="Mansib/Unsprawl-4B-SFT",
max_seq_length=4096,
load_in_4bit=True,
)
FastLanguageModel.for_inference(model)
Part of Unsprawl
Unsprawl is a domain-agnostic, mission-driven compound AI platform for autonomous infrastructure resilience. This adapter enables local inference for structured tasks, reducing API dependency on cloud LLMs.