Redline Judge — Contract Playbook Compliance Adapter
Model Description
Redline Judge is a legal-domain adapter trained for contract clause compliance review and redlining assistance.
It is designed to help identify whether a contract clause is acceptable under a defined compliance playbook and to support span-grounded review workflows for commercial contracts.
This repository contains adapter weights trained with Adaption AutoScientist. It is not a standalone full model and should be loaded on top of the corresponding base model.
Model Details
- Developed by: Hasan Yezir
- Funded by: Adaption AutoScientist Challenge credits
- Shared by: Hasan Yezir
- Model type: PEFT / LoRA adapter for an instruction-tuned causal language model
- Language(s): English
- License: MIT
- Finetuned from model:
"Qwen/Qwen3.5-0.8B
- Training model ID:
adaption_qwen3_5_0_8b_contract_playbook_compli_71ed2144
Model Sources
Intended Use
Direct Use
This adapter is intended for:
- clause-level contract compliance review
- redline triage for commercial agreements
- identifying potentially non-compliant or one-sided clause language
- supporting human reviewers in legal operations workflows
- rapid screening of clause text against a playbook of common compliance/risk patterns
Typical use cases include reviewing clauses involving:
- indemnification
- limitation of liability
- termination
- data protection
- governing law / jurisdiction
- payment terms
- notice / consent language
- confidentiality carveouts
- related commercial contract risk patterns
Downstream Use
Possible downstream applications include:
- contract review copilots
- legal operations dashboards
- procurement workflow assistants
- document intake and risk triage systems
- educational or benchmarking tools for clause-compliance tasks
Out-of-Scope Use
This model is not intended for:
- replacing qualified legal counsel
- providing legal advice to consumers or businesses without human review
- high-stakes autonomous legal decision-making
- criminal law, family law, immigration law, or litigation strategy
- multilingual legal interpretation unless separately validated
- contractual decisions without a human-in-the-loop review process
Bias, Risks, and Limitations
This adapter has important limitations:
- Domain bias: The dataset is oriented toward commercial contract language and may not generalize well to unrelated legal domains.
- Source bias: Real PASS examples were derived from SEC EDGAR contract language, which may bias the model toward public-company drafting conventions.
- Synthetic negative bias: Some REJECT examples were synthetically constructed or transformed to represent risky or non-compliant variants. While useful for training, this may not cover the full diversity of real-world problematic drafting.
- Jurisdictional limitations: The model should not be assumed to reflect all jurisdictions, industries, or negotiation standards.
- Incomplete legal reasoning: A clause can appear acceptable in isolation but become risky in the context of the full agreement.
- Not legal advice: Outputs should always be reviewed by a qualified legal professional before use in real contractual decisions.
Recommendations
Users should treat this model as a review aid, not a final decision-maker.
For production or high-stakes use, keep a human reviewer in the loop and validate performance on your own contract templates, jurisdictions, and policy standards.
How to Get Started
This repository contains adapter weights, so you must load it on top of the correct base model.
1from transformers import AutoModelForCausalLM, AutoTokenizer
2from peft import PeftModel
3
4base_model_id = ""Qwen/Qwen3.5-0.8B"
5adapter_id = "yeziR4/redline-judge-qwen-0.8b-adapter"
6
7tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
8base_model = AutoModelForCausalLM.from_pretrained(
9 base_model_id,
10 trust_remote_code=True,
11 device_map="auto"
12)
13
14model = PeftModel.from_pretrained(base_model, adapter_id)
15
16prompt = """Review the following contract clause for playbook compliance.
17Return a compliance decision and briefly explain the issue if non-compliant.
18
19Clause:
20The Company may withhold payment without prior written notice or consent.
21"""
22
23inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
24outputs = model.generate(**inputs, max_new_tokens=200)
25print(tokenizer.decode(outputs[0], skip_special_tokens=True))
