|
|
--- |
|
|
license: apache-2.0 |
|
|
datasets: |
|
|
- ZTE-AIM/Curr-ReFT-data |
|
|
base_model: |
|
|
- Qwen/Qwen2.5-VL-3B-Instruct |
|
|
- Qwen/Qwen2.5-VL-7B-Instruct |
|
|
pipeline_tag: image-text-to-text |
|
|
--- |
|
|
|
|
|
## Curr-ReFT-data |
|
|
[\[📂 GitHub\]](https://github.com/ding523/Curr_REFT) |
|
|
[\[🤗 HF Dataset\]](https://huggingface.co/datasets/ZTE-AIM/Curr-ReFT-data) |
|
|
## Curr-ReFT-model |
|
|
[\[🤗 Curr-ReFT-3B\]](https://huggingface.co/ZTE-AIM/3B-Curr-ReFT) |
|
|
[\[🤗 Curr-ReFT-7B\]](https://huggingface.co/ZTE-AIM/7B-Curr-ReFT) |
|
|
## Model Overview |
|
|
|
|
|
This is a multimodal large language model fine-tuned from Qwen2.5-VL using our innovative **Curr-ReFT** methodology. The model has undergone a two-stage training process: first through Curriculum Reinforcement Learning, which gradually increases task complexity, followed by Rejected Sample based Self-improvement to maintain foundational capabilities. |
|
|
The model significantly enhances vision-language understanding and reasoning capabilities, making it exceptionally well-suited for complex tasks such as visual reasoning, detailed image understanding, and multimodal problem-solving. With its robust ability to perform sophisticated multimodal reasoning, Curr-ReFT emerges as a powerful AI assistant capable of addressing a wide range of challenges across diverse domains with improved accuracy and contextual awareness. |
|
|
|
|
|
## Training Configuration |
|
|
- Framework: The training process uses the open-source **R1-V** library, with **Qwen2.5-VL-Instruct** as the base model. This model comes in three variants: 3B, 7B. |
|
|
|
|
|
The training configuration for grpo is as follows: |
|
|
```python |
|
|
max_pixels 401408 |
|
|
per_device_train_batch_size: 1 |
|
|
gradient_accumulation_steps: 1 |
|
|
learning_rate: 1.0e-5 |
|
|
|
|
|
num_train_epochs: 1.0 |
|
|
lr_scheduler_type: cosine |
|
|
bf16: true |
|
|
flash_attn: fa2 |
|
|
``` |
|
|
|
|
|
## Usage |
|
|
|
|
|
You can load the model using the Hugging Face `transformers` library: |
|
|
|
|
|
```python |
|
|
from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration |
|
|
import torch |
|
|
from qwen_vl_utils import process_vision_info |
|
|
|
|
|
MODEL_ID = "Curr-ReFT-3B" |
|
|
processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True) |
|
|
model = Qwen2_5_VLForConditionalGeneration.from_pretrained( |
|
|
MODEL_ID, |
|
|
trust_remote_code=True, |
|
|
torch_dtype=torch.bfloat16 |
|
|
).to("cuda").eval() |
|
|
|
|
|
messages = [ |
|
|
{ |
|
|
"role": "user", |
|
|
"content": [ |
|
|
{"type": "image", "image": "<your image path>"}, |
|
|
{"type": "text", "text": "Hint: Please answer the question and provide the final answer at the end. Question: Which number do you have to write in the last daisy?"}, |
|
|
], |
|
|
} |
|
|
] |
|
|
|
|
|
# Preparation for inference |
|
|
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(model.device) |
|
|
|
|
|
generated_ids = model.generate(**inputs, max_new_tokens=4096) |
|
|
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) |
|
|
``` |
|
|
|
|
|
|
|
|
|
|
|
# Institution |
|
|
- ZTE-AIM |
|
|
- University of Science and Technology of China |
|
|
|
|
|
## Model Contact |
|
|
- [email protected] |
|
|
- [email protected] |
|
|
- [email protected] |
|
|
|
|
|
|
