---
license: apache-2.0
language: 
  - en
tags:
  - peft
  - lora
  - sft
  - legal
  - finance
  - adaption
  - instruction-tuning
base_model: togethercomputer/gpt-oss-20b-bf16
---

# 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.

```python
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")