Characteristics and capabilities of Figure 02:
Hardware aspect:
The appearance adopts an exoskeleton structure, integrating the power supply and computing power wiring inside the body, improving reliability and packaging compactness.
It is equipped with a fourth-generation hand device, with 16 degrees of freedom and strength comparable to that of humans, capable of carrying up to 25 kilograms of weight, and can flexibly perform various human-like tasks.
It has 6 RGB cameras (located on the head, chest and back respectively), and has "superhuman" vision.
The internal battery pack capacity has increased to 2.25 kWh. Its founder hopes that it can achieve an actual effective working time of more than 20 hours per day (but currently the official website shows that the battery life is only 5 hours. The 20 hours might be the inferred limit working time of "charging + working").

I've built a space for creating prompts for FLUX

gokaygokay/FLUX-Prompt-Generator


You can create long prompts from images or simple words. Enhance your short prompts with prompt enhancer. You can configure various settings such as artform, photo type, character details, scene details, style, and artist to create tailored prompts.

And you can combine all of them with custom prompts using llms (Mixtral, Mistral, Llama 3, and Mistral-Nemo).

The UI is a bit complex, but it includes almost everything you need. Choosing random option is the most fun!

And i've created some other spaces for using FLUX models with captioners and enhancers.

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gokaygokay/FLUX.1-dev-with-Captioner

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gokaygokay/FLUX.1-Schnell-with-Captioner

💫 Easy Usage. MiniCPM-Llama3-V 2.5 can be easily used in various ways: (1) llama.cpp and ollama support for efficient CPU inference on local devices, (2) GGUF format quantized models in 16 sizes, (3) efficient LoRA fine-tuning with only 2 V100 GPUs, (4) streaming output, (5) quick local WebUI demo setup with Gradio and Streamlit, and (6) interactive demos on HuggingFace Spaces.

🚀 Efficient Deployment. MiniCPM-Llama3-V 2.5 systematically employs model quantization, CPU optimizations, NPU optimizations and compilation optimizations, achieving high-efficiency deployment on edge devices. For mobile phones with Qualcomm chips, we have integrated the NPU acceleration framework QNN into llama.cpp for the first time. After systematic optimization, MiniCPM-Llama3-V 2.5 has realized a 150-fold acceleration in multimodal large model end-side image encoding and a 3-fold increase in language decoding speed.

🌏 Multilingual Support. Thanks to the strong multilingual capabilities of Llama 3 and the cross-lingual generalization technique from VisCPM, MiniCPM-Llama3-V 2.5 extends its bilingual (Chinese-English) multimodal capabilities to over 30 languages including German, French, Spanish, Italian, Korean, Japanese etc. All Supported Languages.

🏆 Trustworthy Behavior. Leveraging the latest RLAIF-V method (the newest technology in the RLHF-V [CVPR'24] series), MiniCPM-Llama3-V 2.5 exhibits more trustworthy behavior. It achieves 10.3% hallucination rate on Object HalBench, lower than GPT-4V-1106 (13.6%), achieving the best-level performance within the open-source community. Data released.

💪 Strong OCR Capabilities. MiniCPM-Llama3-V 2.5 can process images with any aspect ratio and up to 1.8 million pixels (e.g., 1344x1344), achieving an 700+ score on OCRBench, surpassing proprietary models such as GPT-4o, GPT-4V-0409, Qwen-VL-Max and Gemini Pro. Based on recent user feedback, MiniCPM-Llama3-V 2.5 has now enhanced full-text OCR extraction, table-to-markdown conversion, and other high-utility capabilities, and has further strengthened its instruction-following and complex reasoning abilities, enhancing multimodal interaction experiences.

🔥 Leading Performance. MiniCPM-Llama3-V 2.5 has achieved an average score of 65.1 on OpenCompass, a comprehensive evaluation over 11 popular benchmarks. With only 8B parameters, it surpasses widely used proprietary models like GPT-4V-1106, Gemini Pro, Claude 3 and Qwen-VL-Max and greatly outperforms other Llama 3-based MLLMs.

MiniCPM-Llama3-V 2.5 is the latest model in the MiniCPM-V series. The model is built on SigLip-400M and Llama3-8B-Instruct with a total of 8B parameters. It exhibits a significant performance improvement over MiniCPM-V 2.0. Notable features of MiniCPM-Llama3-V 2.5 include:

[2024.05.20] We open-soure MiniCPM-Llama3-V 2.5, it has improved OCR capability and supports 30+ languages, representing the first end-side MLLM achieving GPT-4V level performance! We provide efficient inference and simple fine-tuning. Try it now!

[2024.05.23] 🔍 We've released a comprehensive comparison between Phi-3-vision-128k-instruct and MiniCPM-Llama3-V 2.5, including benchmarks evaluations, multilingual capabilities, and inference efficiency 🌟📊🌍🚀. Click here to view more details.

[2024.05.24] We release the MiniCPM-Llama3-V 2.5 gguf, which supports llama.cpp inference and provides a 6~8 token/s smooth decoding on mobile phones. Try it now!

[2024.05.25] MiniCPM-Llama3-V 2.5 now supports streaming outputs and customized system prompts. Try it at here

[2024.06.03] Now, you can run MiniCPM-Llama3-V 2.5 on multiple low VRAM GPUs(12 GB or 16 GB) by distributing the model's layers across multiple GPUs. For more details, Check this link.

[2024.05.23] 🔥🔥🔥 MiniCPM-V tops GitHub Trending and HuggingFace Trending! Our demo, recommended by Hugging Face Gradio’s official account, is available here. Come and try it out!

[2024.05.28] 🚀🚀🚀 MiniCPM-Llama3-V 2.5 now fully supports its feature in llama.cpp and ollama! Please pull the latest code of our provided forks (llama.cpp, ollama). GGUF models in various sizes are available here. MiniCPM-Llama3-V 2.5 series is not supported by the official repositories yet, and we are working hard to merge PRs. Please stay tuned! You can visit our GitHub repository for more information!
[2024.05.28] 💫 We now support LoRA fine-tuning for MiniCPM-Llama3-V 2.5, using only 2 V100 GPUs! See more statistics here.

A GPT-4V Level Multimodal LLM on Your Phone [2024.08.10] 🚀🚀🚀 MiniCPM-Llama3-V 2.5 is now fully supported by official llama.cpp! GGUF models of various sizes are available here.
[2024.08.06] 🔥🔥🔥 We open-source MiniCPM-V 2.6, which outperforms GPT-4V on single image, multi-image and video understanding. It advances popular features of MiniCPM-Llama3-V 2.5, and can support real-time video understanding on iPad. Try it now!

This is the int4 quantized version of MiniCPM-V 2.6.
Running with int4 version would use lower GPU memory (about 7GB).

Usage
Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.10：