GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
lucasoc
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
lucasoc
Model Tree
Qwen/Qwen2.5-VL-3B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Avaliação rigorosa realizada sobre as 373 figuras científicas reais do conjunto de teste independente oficial do desafio:
| Posição | Modelo / Backbone | Método / Adaptação | Subpasta no Repo | Tabelas Válidas (valid_table) | RMS Numérico (icdar_rms) | Estrutura TEDS (teds) | Score Final ICDAR | Hardware Recomendado |
|---|---|---|---|---|---|---|---|---|
| 🥇 1º | LightOnOCR-3-4B (Novo Recorde) | LoRA + Loss v3 | lightonocr_3_4b/ | 98.39% | 0.6212 | 0.4252 | 0.5232 🏆 | ~8 GB VRAM |
| 🥈 2º | Qwen2.5-VL-7B | LoRA + Loss v2 | qwen2_5_vl_7b/ | 99.73% | 0.6026 | 0.4396 | 0.5211 🚀 | ~14 GB VRAM |
| 🥉 3º | Qwen2.5-VL-3B | LoRA + Loss v1 | root / | 99.73% | 0.5830 | 0.4068 | 0.4949 | < 8 GB VRAM |
| 4º | Qwen3-VL-8B | LoRA + Loss v3 | qwen3_vl_8b/ | 99.73% | 0.6076 | 0.3668 | 0.4872 | ~16 GB VRAM |
| Ref | 1º Colocado Oficial ICDAR 2026 (Peking) | Ensemble / Multi-VLM | - | ~99.0% | 0.5810 | 0.4552 | 0.5181 | Multi-GPU Cluster |
| Ref | 2º Colocado Oficial ICDAR 2026 (Samsung) | Prompting Especializado | - | ~98.5% | 0.5230 | 0.3972 | 0.4601 | Multi-GPU Cluster |
| Base | Qwen2.5-VL-3B (Zero-Shot) | Sem Treinamento | - | 88.74% | 0.4812 | 0.2316 | 0.3564 | < 8 GB VRAM |
text
lucasoc/sci-image-models/├── README.md # Visão geral e leaderboard comparativo├── adapter_config.json # Adapter Qwen2.5-VL-3B (raiz)├── adapter_model.safetensors # Pesos LoRA Qwen2.5-VL-3B├── lightonocr_3_4b/ # 🏆 NOVO RECORDE ABSOLUTO (52.32%)│ ├── adapter_config.json│ ├── adapter_model.safetensors # 88 MB│ ├── test_report.json # Relatório completo de teste│ └── README.md├── qwen2_5_vl_7b/ # SOTA Qwen2.5-VL-7B (52.11%)│ ├── adapter_config.json│ ├── adapter_model.safetensors # 177 MB│ ├── test_report.json│ └── README.md└── qwen3_vl_8b/ # Qwen3-VL-8B (48.72%) ├── adapter_config.json ├── adapter_model.safetensors # 176 MB ├── test_report.json └── README.mdpython
import torchfrom PIL import Imagefrom transformers import AutoProcessor, AutoModelForCausalLMfrom peft import PeftModel # Carregar processador e base modelprocessor = AutoProcessor.from_pretrained("lucasoc/sci-image-models", subfolder="lightonocr_3_4b")base_model = AutoModelForCausalLM.from_pretrained( "lightonai/LightOnOCR-3-4B", torch_dtype=torch.float16, device_map="auto")model = PeftModel.from_pretrained(base_model, "lucasoc/sci-image-models", subfolder="lightonocr_3_4b")model.eval()python
import torchfrom PIL import Imagefrom transformers import AutoProcessor, Qwen2_5_VLForConditionalGenerationfrom peft import PeftModelfrom qwen_vl_utils import process_vision_info # Para Qwen2.5-VL-7B use subfolder="qwen2_5_vl_7b" e base "Qwen/Qwen2.5-VL-7B-Instruct"# Para Qwen3-VL-8B use subfolder="qwen3_vl_8b" e base "Qwen/Qwen3-VL-8B-Instruct" subfolder = "qwen2_5_vl_7b"base_model_name = "Qwen/Qwen2.5-VL-7B-Instruct" processor = AutoProcessor.from_pretrained("lucasoc/sci-image-models", subfolder=subfolder)base_model = Qwen2_5_VLForConditionalGeneration.from_pretrained( base_model_name, torch_dtype=torch.float16, device_map="auto")model = PeftModel.from_pretrained(base_model, "lucasoc/sci-image-models", subfolder=subfolder)model.eval() # Processamento da figuraimage = Image.open("exemplo_grafico_cientifico.png").convert("RGB")messages = [ { "role": "user", "content": [ {"type": "image", "image": image}, {"type": "text", "text": "Extract all numerical data points from this scientific figure into a Markdown table."} ] }]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("cuda") with torch.inference_mode(): outputs = model.generate(**inputs, max_new_tokens=1024) result = processor.batch_decode(outputs, skip_special_tokens=True)[0] print(result)col_count_penalty_weight = 2.0), reforço de cabeçalho (header_weight = 4.5) e terminação antecipada direcional sobre <eos> para evitar sobregeração de linhas vazias. Alcançou o recorde absoluto de 0.5232 com o LightOnOCR-3-4B.Repositório completo no GitHub: lucasdocunha/sci-image-markdown
lucasoc/sci-image-models/├── README.md # Visão geral e leaderboard comparativo├── adapter_config.json # Adapter Qwen2.5-VL-3B (raiz)├── adapter_model.safetensors # Pesos LoRA Qwen2.5-VL-3B├── lightonocr_3_4b/ # 🏆 NOVO RECORDE ABSOLUTO (52.32%)│ ├── adapter_config.json│ ├── adapter_model.safetensors # 88 MB│ ├── test_report.json # Relatório completo de teste│ └── README.md├── qwen2_5_vl_7b/ # SOTA Qwen2.5-VL-7B (52.11%)│ ├── adapter_config.json│ ├── adapter_model.safetensors # 177 MB│ ├── test_report.json│ └── README.md└── qwen3_vl_8b/ # Qwen3-VL-8B (48.72%) ├── adapter_config.json ├── adapter_model.safetensors # 176 MB ├── test_report.json └── README.mdimport torchfrom PIL import Imagefrom transformers import AutoProcessor, AutoModelForCausalLMfrom peft import PeftModel # Carregar processador e base modelprocessor = AutoProcessor.from_pretrained("lucasoc/sci-image-models", subfolder="lightonocr_3_4b")base_model = AutoModelForCausalLM.from_pretrained( "lightonai/LightOnOCR-3-4B", torch_dtype=torch.float16, device_map="auto")model = PeftModel.from_pretrained(base_model, "lucasoc/sci-image-models", subfolder="lightonocr_3_4b")model.eval()import torchfrom PIL import Imagefrom transformers import AutoProcessor, Qwen2_5_VLForConditionalGenerationfrom peft import PeftModelfrom qwen_vl_utils import process_vision_info # Para Qwen2.5-VL-7B use subfolder="qwen2_5_vl_7b" e base "Qwen/Qwen2.5-VL-7B-Instruct"# Para Qwen3-VL-8B use subfolder="qwen3_vl_8b" e base "Qwen/Qwen3-VL-8B-Instruct" subfolder = "qwen2_5_vl_7b"base_model_name = "Qwen/Qwen2.5-VL-7B-Instruct" processor = AutoProcessor.from_pretrained("lucasoc/sci-image-models", subfolder=subfolder)base_model = Qwen2_5_VLForConditionalGeneration.from_pretrained( base_model_name, torch_dtype=torch.float16, device_map="auto")model = PeftModel.from_pretrained(base_model, "lucasoc/sci-image-models", subfolder=subfolder)model.eval() # Processamento da figuraimage = Image.open("exemplo_grafico_cientifico.png").convert("RGB")messages = [ { "role": "user", "content": [ {"type": "image", "image": image}, {"type": "text", "text": "Extract all numerical data points from this scientific figure into a Markdown table."} ] }]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("cuda") with torch.inference_mode(): outputs = model.generate(**inputs, max_new_tokens=1024) result = processor.batch_decode(outputs, skip_special_tokens=True)[0] print(result)