---
language:
- en
license: apache-2.0
tags:
- legal
- immigration
- assistant
- qwen2
- fine-tuned
base_model: Qwen/Qwen2-7B-Instruct
model_type: qwen2
pipeline_tag: text-generation
---

# DoloresAI - Immigration Law Assistant

DoloresAI is a specialized legal assistant fine-tuned on immigration law, designed to provide accurate and helpful information about U.S. immigration processes, visa types, and legal procedures.

## Model Details

- **Base Model**: Qwen/Qwen2-7B-Instruct
- **Model Type**: Qwen2ForCausalLM
- **Parameters**: 7B
- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
- **Vocabulary Size**: 151,665 tokens
- **Precision**: FP16
- **Context Length**: 32,768 tokens
- **Fixed on**: 2026-01-11

## Changes in This Version

This is a fixed version of the DoloresAI merged model with vocabulary mismatch resolved:
- Fixed vocabulary size mismatch between model (151,936) and tokenizer (151,665)
- Model embeddings properly resized to match tokenizer: 151,665 tokens
- Ready for deployment on HuggingFace Inference Endpoints without CUDA errors

## Training

This model was fine-tuned using LoRA adapters on immigration law data and then merged with the base model. The embeddings have been properly resized to match the tokenizer vocabulary size.

## Intended Use

DoloresAI is designed to assist with:
- Immigration process information
- Visa type explanations
- Legal procedure guidance
- Document requirements
- Timeline estimates
- Form instructions

**Important**: This model provides information only and should not be considered legal advice. Always consult with a licensed immigration attorney for specific legal matters.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "JustiGuide/DoloresAI-Merged"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

prompt = "What are the requirements for an H-1B visa?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

## Deployment

### HuggingFace Inference Endpoints

For production deployment, use these environment variables to avoid CUDA errors:

```bash
PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
CUDA_LAUNCH_BLOCKING=1
TORCH_USE_CUDA_DSA=1
TRANSFORMERS_OFFLINE=0
HF_HUB_ENABLE_HF_TRANSFER=1
MODEL_LOAD_TIMEOUT=600
```

Recommended hardware: Nvidia A10G or better

## Verification

The vocabulary sizes have been verified to match:
- Model vocab size: 151,665 ✅
- Tokenizer vocab size: 151,665 ✅
- Match: ✅

## Limitations

- Trained primarily on U.S. immigration law
- Knowledge cutoff based on training data
- Not a replacement for legal counsel
- May require additional context for complex cases

## License

Apache 2.0

## Citation

```bibtex
@misc{doloresai2025,
  title={DoloresAI: Immigration Law Assistant},
  author={JustiGuide},
  year={2025},
  publisher={HuggingFace},
  howpublished={\url{https://huggingface.co/JustiGuide/DoloresAI-Merged}}
}
```

## Model Card Authors

JustiGuide Team

## Model Card Contact

For questions or issues, please open an issue on the model repository.