YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
tvl-mini
Description
This is LORA finetune of Qwen2-VL-2B on russian language.
Data
Dataset contains:
- GrandMaster-PRO-MAX dataset (68k samples)
- Visual Reasoning (36k samples) #Training in progress
- Captioning (34k samples) #Training in progress
- Knowledgeable VQA (35k samples) #Training in progress
- VQA (80k samples) #Training in progress
- Classification (21k samples) #Training in progress
- Conversations (11k samples) #Training in progress
Bechmarks
TODO
Quickstart
Your can simply run this notebook or run code below.
First install qwen-vl-utils and dev version of transformers:
pip install qwen-vl-utils
pip install --no-cache-dir git+https://github.com/huggingface/transformers@19e6e80e10118f855137b90740936c0b11ac397f
And then run:
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("Qwen/Qwen2-VL-2B-Instruct")
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)
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