README.md

metadata

license: apache-2.0
base_model: Qwen/Qwen3-0.6B
tags:
  - legal
  - sft
  - lora
  - trl
  - eula
datasets:
  - AxelDlv00/EULAI
language:
  - fr
pipeline_tag: text-generation

You lie? EULAI!

Local AI Browser Assistant for Legal Document Analysis

Axel Delaval • 28 January 2026

EULAI - Fine-tuned Qwen3-0.6B for EULA Analysis

This model is a fine-tuned version of Qwen/Qwen3-0.6B using the LoRA (Low-Rank Adaptation) method. It specializes in analyzing contracts (EULAs) and privacy policies to extract structured risk-based summaries.

Training Configuration

Details extracted from the training configuration:

• Base Model: Qwen/Qwen3-0.6B
• Method: PEFT / LoRA
• LoRA Hyperparameters:
  • r: 16
  • alpha: 32
  • dropout: 0.05
  • target_modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• Training:
  • epochs: 3
  • learning_rate: 1e-4
  • max_length: 2048
  • packing: True

Usage (Inference)

To use this model, you need to load the adapter on top of the original base model.

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Qwen/Qwen3-0.6B"
adapter_id = "AxelDlv00/EULAI"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
model = PeftModel.from_pretrained(model, adapter_id)

prompt = "We collect your GPS data continuously even when the application is closed."
messages = [{"role": "user", "content": prompt}]

inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, enable_thinking=False, return_tensors="pt").to("cuda")

outputs = model.generate(inputs, max_new_tokens=512, temperature=0.1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Output Format

The model generates structured bullet points in this format:

- [BLOCKER/BAD/GOOD/NEUTRAL] : Title : Explanation