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---
license: apache-2.0
datasets:
- Vikhrmodels/GrandMaster-PRO-MAX
language:
- ru
- en
base_model:
- Qwen/Qwen2-VL-2B-Instruct
pipeline_tag: text2text-generation
tags:
- multimodal
library_name: transformers
---
# tvl-mini
## Description
This is finetune of Qwen2-VL-2B on russian language.
tvl was trained in bf16
## Data
Train dataset contains:
- GrandMaster-PRO-MAX dataset (60k samples)
- Translated, humanized and merged by image subset of GQA (TODO)
## Bechmarks
### TODO
## Quickstart
Your can simply run [this notebook](https://www.kaggle.com/code/artemdzhalilov/tvl-hand-test) or run code below.
First install qwen-vl-utils and dev version of transformers:
```bash
pip install qwen-vl-utils
pip install --no-cache-dir git+https://github.com/huggingface/transformers@19e6e80e10118f855137b90740936c0b11ac397f
```
And then run:
```bash
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch
model = Qwen2VLForConditionalGeneration.from_pretrained(
"2Vasabi/tvl-mini-0.1", torch_dtype=torch.bfloat16, device_map="auto"
)
processor = AutoProcessor.from_pretrained("2Vasabi/tvl-mini-0.1")
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "https://i.ibb.co/d0QL8s6/images.jpg",
},
{"type": "text", "text": "Кратко опиши что ты видишь на изображении"},
],
}
]
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
generated_ids = model.generate(**inputs, max_new_tokens=1000)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
``` |