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Model: zhibinlan/UME-R1-2B Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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library_name: transformers
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license: apache-2.0
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pipeline_tag: image-text-to-text
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tags:
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- Sentence Similarity
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- Embedding
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- zero-shot-image-classification
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- video-text-to-text
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---
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# UME-R1-2B
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## Model Summary
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The model has undergone a cold-start SFT stage and an RL stage of training, and is capable of embedding text, images, multiple images, and videos. In particular, UME-R1 can generate either discriminative or generative embeddings as needed, and the generative embeddings possess the potential for test-time scaling.
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- **Repository:** [UME-R1](https://github.com/XMUDeepLIT/UME-R1)
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- **Paper:** [UME-R1](https://arxiv.org/abs/2511.00405)
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## Train/Eval Data
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- Train data: https://huggingface.co/datasets/zhibinlan/UME-sft-train
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- Eval data: https://huggingface.co/datasets/TIGER-Lab/MMEB-V2
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## Model Performance
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UME-R1 significantly outperforms discriminative embeddings and can provide discriminative or generative representations as needed. Its oracle performance—selecting the best between discriminative and generative—far exceeds using either mode alone.
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<img src="./figures/main_result.png" alt="MMEB-V2" width="1200" height="auto">
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<!--  -->
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In addition, UME-R1 can produce improved embedding representations through repeated sampling, indicating that generative embeddings also hold strong promise for inference-time scaling.
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<img src="./figures/scaling.png" alt="pass@k" width="1200" height="auto">
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### Quick Start
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First clone our github
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```bash
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git clone https://github.com/DeepLearnXMU/UME-R1
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cd UME-R1
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bash setup.sh
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```
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Below, we provide simple examples to show how to use UME-R1 with 🤗 Transformers.
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Example of obtaining generative embeddings:
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```python
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from transformers import Qwen2VLForConditionalGeneration,AutoProcessor
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from qwen_vl_utils import process_vision_info
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import torch
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model = Qwen2VLForConditionalGeneration.from_pretrained(
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"zhibinlan/UME-R1-2B",
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torch_dtype=torch.bfloat16,
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attn_implementation="flash_attention_2",
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device_map="cuda:0",
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)
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processor = AutoProcessor.from_pretrained("zhibinlan/UME-R1-2B")
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prompt = '''Represent the above input text, images, videos, or any combination of the three as embeddings.
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First output the thinking process in <think> </think> tags and then summarize the entire input in a word or sentence.
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Finally, use the <gen_emb> tag to represent the entire input.'''
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": "assets/example.jpg",
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},
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{"type": "text", "text": "Represent the given image with the following question: What is in the image?\n<disc_emb>\n" + prompt},
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],
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}
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]
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# Preparation for inference
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text = processor.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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image_inputs, video_inputs = process_vision_info(messages)
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inputs = processor(
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text=[text],
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images=image_inputs,
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videos=video_inputs,
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padding=True,
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return_tensors="pt",
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)
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inputs = inputs.to(model.device)
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# Inference: Generation of the output
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generated_output = model.generate(**inputs, max_new_tokens=8192, output_hidden_states=True, return_dict_in_generate=True, use_cache=True)
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# Post-process the output
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generated_ids = generated_output.sequences
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hidden_states = generated_output.hidden_states
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generated_ids_trimmed = [
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out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
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]
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def get_embedding_idx(generated_ids_trimmed, EMBEDDING_TOKEN_ID):
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embedding_idx = []
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for i, out_ids in enumerate(generated_ids_trimmed):
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embed_exist = False
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for j in range(len(out_ids) - 1, -1, -1):
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if out_ids[j] == EMBEDDING_TOKEN_ID:
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embedding_idx.append(j + 1)
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embed_exist = True
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break
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if not embed_exist:
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embedding_idx.append(-1)
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return embedding_idx
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def normalize_reps(reps):
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reps = torch.nn.functional.normalize(reps, p=2, dim=-1)
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return reps
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# Get the last hidden state of the <gen_emb> token
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embedding_idx = get_embedding_idx(generated_ids_trimmed, processor.tokenizer.get_vocab()["<gen_emb>"])
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embedding_reps = hidden_states[embedding_idx[0]][-1].squeeze(1)
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# Normalize the representations
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embedding_reps = normalize_reps(embedding_reps)
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output_text = processor.batch_decode(
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generated_ids_trimmed, skip_special_tokens=False, clean_up_tokenization_spaces=False
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)
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```
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<details>
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<summary>Example of obtaining discriminative embeddings</summary>
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```python
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from transformers import Qwen2VLForConditionalGeneration,AutoProcessor
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from qwen_vl_utils import process_vision_info
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import torch
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pretrained_path = "zhibinlan/UME-R1-2B"
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# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
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model = Qwen2VLForConditionalGeneration.from_pretrained(
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pretrained_path,
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torch_dtype=torch.bfloat16,
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attn_implementation="flash_attention_2",
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device_map="cuda:0",
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)
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# default processor
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processor = AutoProcessor.from_pretrained(pretrained_path)
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": "UME-R1/assets/example.jpg",
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},
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{"type": "text", "text": "Represent the given image with the following question: What is in the image?\n<disc_emb>\n"},
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],
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}
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]
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# Preparation for inference
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text = processor.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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image_inputs, video_inputs = process_vision_info(messages)
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inputs = processor(
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text=[text],
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images=image_inputs,
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videos=video_inputs,
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padding=True,
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return_tensors="pt",
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)
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inputs = inputs.to(model.device)
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def get_embedding_idx(generated_ids_trimmed, EMBEDDING_TOKEN_ID):
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embedding_idx = []
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# Search from the last token forward
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for i, out_ids in enumerate(generated_ids_trimmed):
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embed_exist = False
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for j in range(len(out_ids) - 1, -1, -1):
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if out_ids[j] == EMBEDDING_TOKEN_ID:
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embedding_idx.append(j)
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embed_exist = True
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break
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if not embed_exist:
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embedding_idx.append(-1)
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return embedding_idx
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def normalize_reps(reps):
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# Normalize the representations
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reps = torch.nn.functional.normalize(reps, p=2, dim=-1)
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return reps
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output = model(**inputs, output_hidden_states=True, return_dict=True)
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hidden_states = output.hidden_states[-1][0]
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# print("output.hidden_states shape: ", hidden_states.shape)
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embedding_idx = get_embedding_idx(inputs['input_ids'], processor.tokenizer.get_vocab()["<disc_emb>"])
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# Get the last hidden state of the <gen_emb> token
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embedding_reps = hidden_states[embedding_idx[0]]
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# Normalize the representations
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embedding_reps = normalize_reps(embedding_reps)
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```
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</details>
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<details>
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<summary>Multi image inference</summary>
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```python
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# Messages containing multiple images and a text query
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "image", "image": "file:///path/to/image1.jpg"},
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{"type": "image", "image": "file:///path/to/image2.jpg"},
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{"type": "text", "text": "Represent the given images."},
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],
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}
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]
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```
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</details>
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<details>
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<summary>Video inference</summary>
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```python
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# Messages containing a images list as a video and a text query
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "video",
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"video": [
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"file:///path/to/frame1.jpg",
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"file:///path/to/frame2.jpg",
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"file:///path/to/frame3.jpg",
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"file:///path/to/frame4.jpg",
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],
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},
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{"type": "text", "text": "Represent this video."},
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],
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}
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]
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# Messages containing a local video path and a text query
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "video",
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"video": "file:///path/to/video1.mp4",
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"max_pixels": 360 * 420,
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"fps": 1.0,
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},
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{"type": "text", "text": "Represent this video."},
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],
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}
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]
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# Messages containing a video url and a text query
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "video",
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"video": "https://path/to/video.mp4",
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"min_pixels": 4 * 28 * 28,
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"max_pixels": 256 * 28 * 28,
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"total_pixels": 20480 * 28 * 28,
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},
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{"type": "text", "text": "Represent this video."},
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],
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}
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]
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image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True)
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inputs = processor(
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text=[text],
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images=image_inputs,
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videos=video_inputs,
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fps=fps,
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padding=True,
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return_tensors="pt",
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**video_kwargs,
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)
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```
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</details>
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For more usage tips, please refer to our [Github page](https://github.com/DeepLearnXMU/UME-R1).
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## Citation
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If you find our work useful, please consider citing it.
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```
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@article{lan2025ume,
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title={UME-R1: Exploring Reasoning-Driven Generative Multimodal Embeddings},
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author={Lan, Zhibin and Niu, Liqiang and Meng, Fandong and Zhou, Jie and Su, Jinsong},
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journal={arXiv preprint arXiv:2511.00405},
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year={2025}
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}
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```
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added_tokens.json
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added_tokens.json
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{
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"<disc_emb>": 151658,
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"<gen_emb>": 151657,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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chat_template.json
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chat_template.json
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{
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"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
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}
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49
config.json
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config.json
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{
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"_name_or_path": "zhibinlan/UME-R1-2B",
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"architectures": [
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"Qwen2VLForConditionalGeneration"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 1536,
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"image_token_id": 151655,
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"initializer_range": 0.02,
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"intermediate_size": 8960,
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"max_position_embeddings": 32768,
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"max_window_layers": 28,
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"model_type": "qwen2_vl",
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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"num_key_value_heads": 2,
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"rms_norm_eps": 1e-06,
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"rope_scaling": {
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"mrope_section": [
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16,
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24,
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24
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],
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"rope_type": "default",
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"type": "default"
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},
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"rope_theta": 1000000.0,
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"sliding_window": 32768,
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.49.0",
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"use_cache": false,
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"use_sliding_window": false,
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"video_token_id": 151656,
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"vision_config": {
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"hidden_size": 1536,
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"in_chans": 3,
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"model_type": "qwen2_vl",
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"spatial_patch_size": 14,
|
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"torch_dtype": "bfloat16"
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},
|
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"vision_end_token_id": 151653,
|
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"vision_start_token_id": 151652,
|
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"vision_token_id": 151654,
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"vocab_size": 151936
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}
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||||
3
figures/main_result.png
Executable file
3
figures/main_result.png
Executable file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:6e1f7225c42487f587fec7fd03db9e1818a6193db83ab196bf49fa8ab8da17af
|
||||
size 270714
|
||||
3
figures/scaling.png
Executable file
3
figures/scaling.png
Executable file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:a1ebb391fd4aa2e9c44718b464c2a80051de8fdbd34c0fdab159bc390add8133
|
||||
size 294729
|
||||
6
generation_config.json
Normal file
6
generation_config.json
Normal file
@@ -0,0 +1,6 @@
|
||||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 151643,
|
||||
"eos_token_id": 151645,
|
||||
"transformers_version": "4.49.0"
|
||||
}
|
||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:ad6536ef701debe1be798a5dc7a71dce6facefd8390f9f8955dbe75b8f6c0f90
|
||||
size 4418050848
|
||||
29
preprocessor_config.json
Normal file
29
preprocessor_config.json
Normal file
@@ -0,0 +1,29 @@
|
||||
{
|
||||
"do_convert_rgb": true,
|
||||
"do_normalize": true,
|
||||
"do_rescale": true,
|
||||
"do_resize": true,
|
||||
"image_mean": [
|
||||
0.48145466,
|
||||
0.4578275,
|
||||
0.40821073
|
||||
],
|
||||
"image_processor_type": "Qwen2VLImageProcessor",
|
||||
"image_std": [
|
||||
0.26862954,
|
||||
0.26130258,
|
||||
0.27577711
|
||||
],
|
||||
"max_pixels": 2359296,
|
||||
"merge_size": 2,
|
||||
"min_pixels": 784,
|
||||
"patch_size": 14,
|
||||
"processor_class": "Qwen2VLProcessor",
|
||||
"resample": 3,
|
||||
"rescale_factor": 0.00392156862745098,
|
||||
"size": {
|
||||
"longest_edge": 2359296,
|
||||
"shortest_edge": 784
|
||||
},
|
||||
"temporal_patch_size": 2
|
||||
}
|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:5287235572b0af97db0b322e328806b84a3a1a89754e473c31c022af0a7122ee
|
||||
size 11420905
|
||||
161
tokenizer_config.json
Normal file
161
tokenizer_config.json
Normal file
@@ -0,0 +1,161 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<gen_emb>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "<disc_emb>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 12288,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "right",
|
||||
"processor_class": "Qwen2VLProcessor",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user