---
base_model: Qwen/Qwen2-VL-2B-Instruct
library_name: transformers
model_name: qwen_report
tags:
- trl
- sft
- Image-Text-to-Text
- Transformers
- Safetensors
- English
- qwen2_5_vl
- multimodal
licence: license
datasets:
- eltorio/ROCOv2-radiology
pipeline_tag: image-text-to-text
---

# Model Card for qwen_report

This model is a fine-tuned version of [Qwen/Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).

## Quick start

```python
from transformers import AutoModelForVision2Seq, AutoProcessor, BitsAndBytesConfig
import torch
# Hugging Face model id
model_id = "BoghdadyJR/qwen_report"

# BitsAndBytesConfig int-4 config
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,  
)

model = AutoModelForVision2Seq.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.float16,
    quantization_config=bnb_config
)


processor = AutoProcessor.from_pretrained(model_id)
```

## Training procedure

 


This model was trained with SFT.

### Framework versions

- TRL: 0.15.1
- Transformers: 4.48.3
- Pytorch: 2.5.1+cu124
- Datasets: 3.3.1
- Tokenizers: 0.21.0

## Citations



Cite TRL as:
    
```bibtex
@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}
```