ClauseGuard-Legal-Audit (LoRA Adapter)

Model Details

Model Description

ClauseGuard-Legal-Audit is a specialized Low-Rank Adaptation (LoRA) model fine-tuned for advanced commercial contract analysis. Trained on a premium, sentence-bounded instruction-tuning dataset, this model is engineered to execute precise clause extraction, granular risk tier assessments with legal justifications, and plain-prose business summaries from complex legal documents.

It was developed as part of the Adaption Labs AutoScientist Challenge to benchmark high-tier, agent-trained legal reasoning capabilities.

• Developed by: Asad Ullah Dogar
• Model type: Causal Language Model (LoRA Adapter)
• Language(s) (NLP): English
• License: Apache 2.0
• Finetuned from base model: togethercomputer/gpt-oss-20b-bf16
• Training Platform: Adaption Labs (AutoScientist Engine)

Model Sources

• Dataset Repository: [adaption-ClauseGuard_Legal_Audit_SFT_v1.0]

Uses

Direct Use

This model is designed for legal professionals, compliance officers, and AI researchers building legal-tech applications. It excels at:

• Extracting specific clauses from dense commercial contracts.
• Providing granular risk tier assessments (e.g., High, Medium, Low) based on explicit legal text.
• Generating step-by-step reasoning traces and legal justifications for its assessments.
• Translating complex legal jargon into plain-prose business summaries.

Out-of-Scope Use

This model is an AI assistant and does not provide literal or formal legal representation. It should not be used as a standalone replacement for a licensed attorney. All critical legal assessments must be verified by a human professional. It is not designed for conversational chit-chat outside of the legal/financial domain.

Bias, Risks, and Limitations

Like all language models, ClauseGuard may occasionally hallucinate or misinterpret highly ambiguous legal phrasing. While it has been fine-tuned using a "Zero Hallucination Constraint" methodology to output "Unavailable" when data is missing, users must still conduct rigorous human-in-the-loop verification for high-stakes audits.

How to Get Started with the Model

Use the code below to load the base model and the PEFT LoRA adapters.

from peft import PeftModel
from transformers import AutoModelForCausalLM

# Load the 20B base model
base_model = AutoModelForCausalLM.from_pretrained("togethercomputer/gpt-oss-20b-bf16")

# Load the ClauseGuard LoRA adapter
model = PeftModel.from_pretrained(base_model, "asadullahdogarr/ClauseGuard-Legal-Audit")