---
license: apache-2.0
library_name: transformers
pipeline_tag: feature-extraction
base_model:
- Qwen3.5-0.8B
tags:
- transformers
- multimodal embedding
- qwen
- embedding
- distillation
- vl
- vision-language
- vl-embedding
- image-text-to-text
- sentence-similarity
datasets:
- TIGER-Lab/MMEB-train
- openbmb/VisRAG-Ret-Train-In-domain-data
- openbmb/VisRAG-Ret-Train-Synthetic-data
model-index:
- name: cho-embedding-0.8b
  results:
  - task:
      type: feature-extraction
    dataset:
      name: MMEB-Image
      type: TIGER-Lab/MMEB
    metrics:
    - name: Overall (36 tasks)
      type: accuracy
      value: 60.7
    - name: CLS
      type: accuracy
      value: 54.5
    - name: VQA
      type: accuracy
      value: 59.2
    - name: RET
      type: accuracy
      value: 60.1
    - name: GND
      type: accuracy
      value: 81.9
---

# cho-embedding-0.8b

A multimodal embedding model distilled from Qwen3-VL-Embedding-8B into the Qwen3.5-0.8B architecture. Supports text, images, and multimodal inputs.

## Model Overview

- **Model Type**: Multimodal Embedding
- **Base Architecture**: Qwen3.5-0.8B (GatedDeltaNet)
- **Teacher Model**: Qwen3-VL-Embedding-8B
- **Number of Parameters**: 853M
- **Embedding Dimension**: 1024 (trained MRL at 1024, 256, 64)
- **Context Length**: 4096 tokens
- **Training**: Full fine-tune distillation (KL-divergence + contrastive) on 3.4M multimodal samples

## Performance (MMEB-Image, 36 tasks)

| Model | Size | CLS | VQA | RET | GND | **Overall** |
|-------|------|-----|-----|-----|-----|-------------|
| **cho-embedding-0.8b** | **853M** | **54.5** | **59.2** | **60.1** | **81.9** | **60.7** |
| CAFe-0.5B | 894M | 59.1 | 49.1 | 61.0 | 83.0 | 59.6 |
| LLaVE-0.5B | 894M | 57.4 | 50.3 | 59.8 | 82.9 | 59.1 |
| VLM2Vec-V2.0-2B | 2.2B | 62.9 | 56.4 | 69.6 | 77.1 | 64.9 |
| VLM2Vec-V1-2B | 2.2B | 58.6 | 49.2 | 65.0 | 73.1 | 59.7 |
| VLM2Vec-Phi3.5V | 4.2B | 54.8 | 54.9 | 62.3 | 79.5 | 60.1 |

## Usage

```python
from transformers import AutoModel, AutoProcessor
import torch
import torch.nn.functional as F

model_path = "radi-cho/cho-embedding-0.8b"

model = AutoModel.from_pretrained(model_path, trust_remote_code=True, dtype=torch.bfloat16).to("cuda").eval()
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
processor.tokenizer.padding_side = "right"

def embed(texts, instruction="Represent the user's input."):
    conversations = []
    for text in texts:
        conversations.append([
            {"role": "system", "content": [{"type": "text", "text": instruction}]},
            {"role": "user", "content": [{"type": "text", "text": text}]}
        ])

    formatted = processor.tokenizer.apply_chat_template(
        conversations, add_generation_prompt=False, tokenize=False)
    formatted = [t.rstrip() + "<|endoftext|>" for t in formatted]
    inputs = processor.tokenizer(formatted, padding=True, return_tensors="pt").to("cuda")

    with torch.no_grad():
        outputs = model(**inputs)
        last_hidden = outputs.last_hidden_state
        attn = inputs["attention_mask"]
        last_pos = attn.shape[1] - attn.flip(dims=[1]).argmax(dim=1) - 1
        row_idx = torch.arange(last_hidden.shape[0], device="cuda")
        embeddings = last_hidden[row_idx, last_pos, :1024]
        embeddings = F.normalize(embeddings.float(), p=2, dim=-1)

    return embeddings

# Example
queries = embed(["A dog playing on the beach"], instruction="Find a matching image caption.")
docs = embed(["A golden retriever runs along the shoreline at sunset"])
similarity = (queries @ docs.T).item()
print(f"Similarity: {similarity:.4f}")
```

### With Images

```python
from qwen_vl_utils.vision_process import process_vision_info

conversations = [[
    {"role": "system", "content": [{"type": "text", "text": "Represent the image for retrieval."}]},
    {"role": "user", "content": [{"type": "image", "image": "file:///path/to/image.jpg"}]}
]]

texts = processor.tokenizer.apply_chat_template(conversations, add_generation_prompt=False, tokenize=False)
texts = [t.rstrip() + "<|endoftext|>" for t in texts]
images, _, video_kwargs = process_vision_info(conversations, return_video_metadata=True, return_video_kwargs=True)
inputs = processor(text=texts, images=images, padding=True, return_tensors="pt", **video_kwargs).to("cuda")

with torch.no_grad():
    outputs = model(**inputs)
    # ... extract last token embedding as above
```

## Training Details

- **Method**: Contrastive training on mined hard-negatives and knowledge distillation from Qwen3-VL-Embedding-8B
- **Data**: 3.4M samples (MMEB train diverse + original splits, MSMarco, AllNLI, Quora, VisRAG, private mined samples)
- **Batch Size**: 1024 effective (128/GPU x 8 GPUs)
- **Hardware**: 8x NVIDIA H100 80GB (6720 GPU hours spent on mining, training, and ablations)
- **Training Epochs**: 3

## Citation

Model is released under Apache 2.0. Please cite this work if used in academic publications, preprints, etc.

```bibtex
@misc{choembedding,
  title={cho-embedding-0.8b: Vision-Language Embeddings via Contrastive Hard-negatives Objective},
  author={Cholakov, Radostin},
  year={2026}
}
```