Lucia 2B: Qwen3.5-2B, think symbolic image

Lucia 2B is the 2-billion-parameter member of the Lucia family, the thinking lookers: a LoRA adapter of Qwen/Qwen3.5-2B that looks at a picture and writes the row of thought-symbolic-image-captions: a short chain of plain-language thoughts, one named step per line, then the wordings (a caption, a prompt, tags, a critique, by depth), then the questions a careful looker would still ask, each with its answer and what settles it. Every size is taught the same rows under the same recipe; the sizes differ in what they can hold, and the reads below say what this one holds. She holds the form and the medium term at the 9B's level and backs her thoughts a little less often; the size shows on the long specification rows, where she defines more. A fit for cards the 4B does not.

see: A single figure, seated, holding a book, in three-quarter view.
place: The figure sits at the centre, the book held low against the lap.
form: The sitter fills the frame from the knees up, the background a plain dark wash.
colour: Brown ink over graphite, the paper a warm cream.
mood: The lowered eyes and the still hands suggest absorption, a reader lost in the page.
caption: A seated figure reads a book held in the lap, drawn in brown ink over graphite.
prompt: drawing, seated figure reading, three-quarter view, brown ink over graphite, cream paper, plain dark wash
question: thing name -> book [record]
question: medium -> ink and graphite [record]
question: mood tone -> absorbed [chain]
question: sitter identity -> unknown [unknown]

The steps a chain may use, in the order a careful looker takes them: see, name, place, count, relate, form, light, colour, mood, mean, intend, story, compare, change, check, say. A quick look holds 1 to 2 thoughts, a closer look 3 to 5, a long look 6 to 10. Questions end in [record] (the answer is a fact of the picture), [chain] (the chain worked it out) or [unknown]. The prompt line's first term names the kind of picture (painting, print, drawing, photograph).

Loading

This repository holds the LoRA adapter; the full fine-tune of the same recipe is AbstractPhil/qwen3.5-2b-think-symbolic-image-full. Every Lucia loads the same way and is run the same way: thinking off, greedy decoding, no system prompt (the row's form is learned, and a system turn she was not trained with makes her over-write). Needs transformers 5; the adapter also needs peft 0.18 or newer.

The LoRA adapter (AbstractPhil/qwen3.5-2b-think-symbolic-image): the base model, the adapter merged into it, the processor from the adapter repository (the base processor's files are kept there).

import torch
from peft import PeftModel
from transformers import AutoModelForMultimodalLM, AutoProcessor

processor = AutoProcessor.from_pretrained("AbstractPhil/qwen3.5-2b-think-symbolic-image")
model = AutoModelForMultimodalLM.from_pretrained("Qwen/Qwen3.5-2B", dtype=torch.bfloat16, device_map="cuda")
model = PeftModel.from_pretrained(model, "AbstractPhil/qwen3.5-2b-think-symbolic-image").merge_and_unload().eval()

The full fine-tune (AbstractPhil/qwen3.5-2b-think-symbolic-image-full): the model and the processor from the full repository, nothing else to merge.

import torch
from transformers import AutoModelForMultimodalLM, AutoProcessor

processor = AutoProcessor.from_pretrained("AbstractPhil/qwen3.5-2b-think-symbolic-image-full")
model = AutoModelForMultimodalLM.from_pretrained("AbstractPhil/qwen3.5-2b-think-symbolic-image-full", dtype=torch.bfloat16, device_map="cuda").eval()

Then, for either:

from PIL import Image

image = Image.open("picture.jpg").convert("RGB")
prompt = ("Look at this picture and think it through in writing, a closer look, ending with a caption; then write a caption "
          "and a prompt or tags, and the questions a careful looker would still ask.")
messages = [{"role": "user", "content": [{"type": "image", "image": image}, {"type": "text", "text": prompt}]}]
text = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False, enable_thinking=False)
inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=800, do_sample=False)
print(processor.batch_decode(out[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0])

The depth words: "a quick look" (1 to 2 thoughts), "a closer look" (3 to 5), "a long look" (6 to 10). The parser, the scorer and the trainer are in qwen-symbolic-thinking (qwen_symbolic_thinking.chain.parse, chain.score); the same package extracted every number below.

The reads

Read the same way as Lucia v1, the 9B: 60 pictures and 40 specifications no adapter saw, against the dataset's own rows (row shape, said / backed, definitions); then the medium term on two sets no training row came from: 500 National Gallery of Art pictures (100 drawings, prints, paintings, photographs and objects each) and 200 pictures of AIC, NGA, Cleveland and the Met. The 9B's numbers stand beside hers.

What this says: the 2B holds the row's form and the medium term at the 9B's level (every kind within the spread of the 9B's own two seeds, the print row of the museum pictures three under), and backs what it says a little less often (0.28 against 0.30 on the 60-row screen, within that read's seed spread of 0.03 to 0.05). Where the size shows is the specification rows, the long looks written from a text description alone: her thoughts there carry a defining phrase in 57% of rows against the 9B's 35%, and a gloss line in 15% against 8%. On picture rows the mood line comes more often (30% of the 60 rows against 22%). The museum painting row understates every size: watercolours and gouaches on paper are named drawings, as the National Gallery's own catalogue does. The reads are made with qwen_symbolic_thinking.tools (probe_read, museum_read, museum_mismatch, screen_conflicts), so any arm can be read the same way.

How she was trained

Versions

The family

The 9B is Lucia v1; every other size is trained under her recipe and read on the same held-out sets.

License

Apache-2.0, the base model's licence. The rows she was taught come from the datasets above under their own terms.