---
library_name: transformers
tags: []
---

# Model Card for letxbe/mistral-7b-v03-BoundingDocs-rephrased

`letxbe/mistral-7b-v03-BoundingDocs-rephrased` is a fine-tuned Mistral-7B-v0.3 for the Document Question Answering task. It was trained on `BoundingDocs` using the `rephrased` version of the questions.


## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** LetXBe
- **Model Type**: LLM
- **Languages**: Multilingual
- **License**: CC BY 4.0
- **Finetuned From**: `Mistral-7B-v0.3`
- **Input Format**: Text using custom prompt
- **Output Format**: JSON



## 🚀 How to Use


### Prompt

The model should be prompted with this prompt:
```python

TEMPLATE_PROMPT = '''<|startdocument|>
{DOCUMENT}
<|enddocument|>

<|starttask|>
Answer the following question about the document:
Question: "{QUESTION}"
Answer completing the following format:

'''json
{"value": ""}
'''

<|endtask|>
'''
```

where DOCUMENT is the textual content of the document page.

### Inference Example

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("letxbe/mistral-7b-v03-BoundingDocs-rephrased")
model = AutoModelForCausalLM.from_pretrained("letxbe/mistral-7b-v03-BoundingDocs-rephrased")

# Encode input
input_text = "Your prompt"
inputs = tokenizer(input_text, return_tensors="pt")

# Generate response
outputs = model.generate(**inputs)

# Decode and print
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```