Bagel Canvas Tangram Instruct

--- license: apache-2.0 tags: - image-generation - multimodal - bagel - tangram - instruction-following base_model: ByteDance-Seed/BAGEL-7B-MoT datasets: - eozlu/TangramData-Formatted-Colored pipeline_tag: image-text-to-image --- # BAGEL-Canvas-Tangram-Instruct This is a fine-tuned version of [BAGEL-Canvas](https://huggingface.co/ByteDance-Seed/BAGEL-7B-MoT) trained on the [TangramData-Formatted-Colored](https://huggingface.co/datasets/eozlu/TangramData-Formatted-Colored) dataset for tangram puzzle generation with color-aware instructions. ## Model Details - **Base Model:** ByteDance-Seed/BAGEL-7B-MoT (BAGEL-Canvas) - **Training Method:** LoRA fine-tuning (rank=16, alpha=32) - **Training Steps:** 2700 - **Target Modules:** q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj ## Usage ```python import torch from PIL import Image # Clone the BAGEL repository for the inferencer # git clone https://github.com/ByteDance-Seed/BAGEL.git from inferencer import InterleaveInferencer # Initialize the inferencer inferencer = InterleaveInferencer( model_path="eozlu/bagel-canvas-tangram-instruct", precision="bf16", use_flash_attn=True, ) # Generate a tangram image prompt = "Create a tangram puzzle of a cat using red, blue, and green pieces." result = inferencer.generate( text=prompt, think=True, max_new_tokens=512, do_sample=True, temperature=0.7, ) # The result contains the generated image image = result["image"] image.save("tangram_output.png") ``` ## Training Details ### Dataset The model was fine-tuned on TangramData-Formatted-Colored, a dataset of tangram puzzles with: - Colored SVG tangram pieces - Natural language descriptions - Spatial reasoning annotations ### Hyperparameters - Learning Rate: 1e-5 - Batch Size: 1 (with gradient accumulation) - LoRA Rank: 16 - LoRA Alpha: 32 - Warmup Steps: 100 - Weight Decay: 0.01 ## License This model is released under the Apache 2.0 license, following the original BAGEL model license. ## Citation If you use this model, please cite: ```bibtex @article{bagel2024, title={BAGEL: Bootstrapped Agentic Generation for Enhanced Learning}, author={ByteDance Seed Team}, year={2024} } ``` ## Acknowledgments - ByteDance Seed Team for the original BAGEL model - The tangram dataset contributors

Source context: 24 downloads · 0 likes · Pipeline image-text-to-image · Repo eozlu/bagel-canvas-tangram-instruct

Source context: 3 downloads · 0 likes · Pipeline image-text-to-image · Repo eozlu/bagel-canvas-tangram-instruct