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AGViveiros
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Model Details
Model Provider
AGViveiros
Model Tree
Qwen/Qwen2.5-VL-3B-Instruct
Input Modalities
Output Modalities
Supported Functionality
LantErn extends Qwen2.5-VL-3B-Instruct with
Latent Visual Reasoning (LVR) tokens. Instead of always verbalising what it sees, the model can emit
compressed visual embeddings (<|lvr_start|>…<|lvr_end|>) during its chain-of-thought, enabling
non-verbalized visual reasoning interleaved with text.
Special tokens:
| Token | Role |
|---|---|
<lvr_start> | Begin a latent visual reasoning block |
<lvr_sep> | Placeholder replaced by compressed visual embeddings (8 tokens) |
<lvr_end> | End a latent visual reasoning block |
Codebase: github.com/GuilhermeViveiros/LantErn
bash
git clone https://github.com/GuilhermeViveiros/LantErn.gitcd LantErnpip install -r requirements.txtpip install -e .
python
import torchfrom PIL import Imagefrom qwen_vl_utils import process_vision_infofrom src.lantern_generate.generate import generate as lantern_generatefrom src.models import load_model# ── 1. Load model + processor ─────────────────────────────────────────────────device = "cuda" if torch.cuda.is_available() else "cpu"model, processor = load_model("AGViveiros/LanteRn-3B-Tetris", compute_dtype=torch.bfloat16, use_cache=True)model.eval().to(device)processor.tokenizer.padding_side = "left"# ── 2. Build inputs ───────────────────────────────────────────────────────────image = Image.open("path/to/image.jpg").convert("RGB")question = "Your question here"messages = [{"role": "user","content": [{"type": "image", "image": image},{"type": "text", "text": question},],}]text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)image_inputs, _ = process_vision_info(messages)inputs = processor(text=[text], images=image_inputs, return_tensors="pt").to(device)prompt_len = inputs["input_ids"].shape[1]# ── 3. Generate with latent visual reasoning ──────────────────────────────────output = model.generate(**inputs,max_new_tokens=512,do_sample=False,custom_generate=lantern_generate,use_cache=True,return_dict_in_generate=True,)generated = output.sequences[0][prompt_len:]print(processor.decode(generated, skip_special_tokens=False))
bibtex
@article{viveiros2026holding,title={What's Holding Back Latent Visual Reasoning?},author={Viveiros, Andr{\'e} G and Gon{\c{c}}alves, Nuno and Martins, Andr{\'e} FT and Lindemann, Matthias},journal={arXiv preprint arXiv:2605.18445},year={2026}}