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)

Usage

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

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.

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