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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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