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Model Details
Model Provider
Laborator
Model Tree
Qwen/Qwen3-VL-2B-Instruct
Input Modalities
Output Modalities
Supported Functionality
A LoRA adapter on top of Qwen/Qwen3-VL-2B-Instruct. The base model is unchanged; this is a small set of trained low-rank weights you load via PEFT.
q_proj, k_proj, v_proj, o_proj) and MLP (up_proj, gate_proj, down_proj) projections| Source | Coins | License |
|---|---|---|
| The Metropolitan Museum of Art | 1188 | CC0 1.0 |
| Cleveland Museum of Art | 519 | CC0 1.0 |
| Total | 1707 |
Text reference: Nomisma.org core concepts (nomisma.org/id/*), CC BY 3.0.
Every image is CC0 public-domain and struck on/before 1925, which clears modern national-mint design copyright worldwide.
Educational coin identification and description. Useful for collectors, students, archaeologists, and anyone trying to put a name to a coin in a drawer or a find tray.
Not a numismatic appraisal. This adapter does not estimate market value, condition grade, or authenticity. For valuation, consult a qualified numismatist.
Qwen/Qwen3-VL-2B-Instruct model.python
import torchfrom transformers import AutoProcessor, Qwen3VLForConditionalGeneration, BitsAndBytesConfigfrom peft import PeftModelfrom PIL import Image BASE = "Qwen/Qwen3-VL-2B-Instruct"ADAPTER = "Laborator/ai-numismatist-qwen3vl-2b-coins-lora" bnb = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16,) processor = AutoProcessor.from_pretrained(BASE)model = Qwen3VLForConditionalGeneration.from_pretrained( BASE, quantization_config=bnb, device_map="auto", torch_dtype=torch.bfloat16,)model = PeftModel.from_pretrained(model, ADAPTER)model.eval() image = Image.open("coin.jpg").convert("RGB")messages = [{"role": "user", "content": [ {"type": "image", "image": image}, {"type": "text", "text": "Identify this coin. State its type, culture, and approximate date."},]}] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt",).to(model.device) out = model.generate(**inputs, max_new_tokens=200, do_sample=False)print(processor.batch_decode(out[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0])This adapter is released under Apache 2.0, inheriting the license of the base model.
Full source, training script, and inference pipeline: https://github.com/SergheiBrinza/ai-numismatist
Qwen/Qwen3-VL-2B-Instruct by the Qwen Team / Alibaba — Apache 2.0import torchfrom transformers import AutoProcessor, Qwen3VLForConditionalGeneration, BitsAndBytesConfigfrom peft import PeftModelfrom PIL import Image BASE = "Qwen/Qwen3-VL-2B-Instruct"ADAPTER = "Laborator/ai-numismatist-qwen3vl-2b-coins-lora" bnb = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16,) processor = AutoProcessor.from_pretrained(BASE)model = Qwen3VLForConditionalGeneration.from_pretrained( BASE, quantization_config=bnb, device_map="auto", torch_dtype=torch.bfloat16,)model = PeftModel.from_pretrained(model, ADAPTER)model.eval() image = Image.open("coin.jpg").convert("RGB")messages = [{"role": "user", "content": [ {"type": "image", "image": image}, {"type": "text", "text": "Identify this coin. State its type, culture, and approximate date."},]}] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt",).to(model.device) out = model.generate(**inputs, max_new_tokens=200, do_sample=False)print(processor.batch_decode(out[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0])