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prithivMLmods
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Model Details
Model Provider
prithivMLmods
Model Tree
Qwen/Qwen3-VL-4B-Instruct
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import Qwen3VLForConditionalGeneration, AutoProcessorfrom qwen_vl_utils import process_vision_infoimport torch model = Qwen3VLForConditionalGeneration.from_pretrained( "prithivMLmods/oMEGA-4B-SpatialThink-0804", torch_dtype="auto", device_map="auto") processor = AutoProcessor.from_pretrained( "prithivMLmods/oMEGA-4B-SpatialThink-0804") messages = [ { "role": "user", "content": [ { "type": "image", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", }, { "type": "text", "text": "Provide a detailed caption and reasoning for this image." }, ], }] text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True) image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt",).to("cuda") generated_ids = model.generate( **inputs, max_new_tokens=128) generated_ids_trimmed = [ out[len(inp):] for inp, out in zip(inputs.input_ids, generated_ids)] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False) print(output_text)| Setting | Value |
|---|---|
| Base Model | Qwen/Qwen3-VL-4B-Instruct |
| Training Method | Supervised Fine-Tuning (SFT) |
| Primary Objective | Spatial Reasoning with Concise Notes for Unfiltered Vision Tasks |
| Secondary Objective | Efficient Spatial Reasoning and Image Captioning |
| Training Framework | TRL + Transformers |
| Training Precision | BF16 |
Qwen/Qwen3-VL-4B-Instruct: Base vision-language model used for this project.
SpaceThinker by remyxai: A spatial reasoning dataset used to improve concise visual reasoning capabilities.
OpenCaption-FineGrained: A fine-grained image captioning dataset used to enhance detailed visual understanding and caption generation.
TRL - Transformers Reinforcement Learning: Used for supervised fine-tuning and multimodal training.
Transformers: Provides the model architecture, training, and inference framework for multimodal transformer models.
from transformers import Qwen3VLForConditionalGeneration, AutoProcessorfrom qwen_vl_utils import process_vision_infoimport torch model = Qwen3VLForConditionalGeneration.from_pretrained( "prithivMLmods/oMEGA-4B-SpatialThink-0804", torch_dtype="auto", device_map="auto") processor = AutoProcessor.from_pretrained( "prithivMLmods/oMEGA-4B-SpatialThink-0804") messages = [ { "role": "user", "content": [ { "type": "image", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", }, { "type": "text", "text": "Provide a detailed caption and reasoning for this image." }, ], }] text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True) image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt",).to("cuda") generated_ids = model.generate( **inputs, max_new_tokens=128) generated_ids_trimmed = [ out[len(inp):] for inp, out in zip(inputs.input_ids, generated_ids)] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False) print(output_text)