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A Family of Dynamic UltraFast Small Language Models Ready for Embodied Artificial General Intelligence!

Recent Activity

prithivMLmods  new activity 4 days ago
SmallDoge/Doge-60M:ImportError: cannot import name 'LossKwargs' from 'transformers.utils'
JingzeShi  new activity 5 days ago
SmallDoge/Doge-60M:ImportError: cannot import name 'LossKwargs' from 'transformers.utils'
JingzeShi  updated a model 7 days ago
SmallDoge/Doge-40M-MoE-checkpoint
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prithivMLmods 
posted an update 1 day ago
prithivMLmods 
posted an update 3 days ago
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4501
Excited to introduce the Tiny VLMs Lab App for experiencing 15+ multimodal VLMs, ranging from a 250M parameter model to a 4B parameter model, for tasks like OCR, reasoning, small models for single-shot answering, and captioning (abliterated), across a broad range of visual categories including images with complex, sensitive, or nuanced content, while handling varying aspect ratios and resolutions.🧪

🤗 Space/App: prithivMLmods/Tiny-VLMs-Lab

✦︎ Also introducing prithivMLmods/Qwen2.5-VL-3B-Abliterated-Caption-it, tailored for Abliterated Captioning / Uncensored Image Captioning. This release comes as a lighter alternative to the existing Qwen2.5-VL-7B-Abliterated-Caption-it prithivMLmods/Qwen2.5-VL-7B-Abliterated-Caption-it model, making it usable on mid-range GPUs and even experimental on T4 GPUs.

✦︎ Collection: prithivMLmods/vl-abliterated-caption-68a0443b63182e97a15c47a3
✦︎ GitHub: https://github.com/PRITHIVSAKTHIUR/Tiny-VLMs-Lab
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To know more about it, visit the app page or the respective model page!!
prithivMLmods 
in SmallDoge/Doge-60M 4 days ago

ImportError: cannot import name 'LossKwargs' from 'transformers.utils'

9
#2 opened 4 months ago by
Alan109440
JingzeShi 
in SmallDoge/Doge-60M 5 days ago

ImportError: cannot import name 'LossKwargs' from 'transformers.utils'

9
#2 opened 4 months ago by
Alan109440
prithivMLmods 
posted an update 7 days ago
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3098
Try Liquid AI's all-new multimodal models: LFM2-VL-1.6B & LFM2-VL-450M! Demo with the Gradio UI and ReportLab support and both models are runnable on T4 GPU!

↗ LFM2-VL-1.6B-LiquidAI : https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/LFM2-VL-1.6B-LiquidAI/LFM2-VL-1.6B_ReportLab.ipynb

↗ LFM2-VL-450M-LiquidAI : https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/LFM2-VL-450M-LiquidAI/LFM2-VL-450M_ReportLab.ipynb

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To know more about it, visit the multimodal outpost notebooks !!
  • 1 reply
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JingzeShi 
updated 3 models 7 days ago
JingzeShi 
published a model 8 days ago
JingzeShi 
updated a model 8 days ago
JingzeShi 
published a model 8 days ago
JingzeShi 
updated a dataset 9 days ago
JingzeShi 
published a dataset 9 days ago
JingzeShi 
updated a dataset 9 days ago
prithivMLmods 
posted an update 10 days ago
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4343
On the verge of releasing Poseidon-Reasoning-5M, a dataset built to excel in general thought processes, mathematics, and science across a diverse mixture of domains, I’m also dropping the Gargantua-R1-Compact dataset, a collection of over six million high-quality reasoning QA pair traces. 🤗🚀

✦ Gargantua-R1-Compact : prithivMLmods/Gargantua-R1-Compact

from datasets import load_dataset

dataset = load_dataset("prithivMLmods/Gargantua-R1-Compact", split="train")

Additionally, I’m adding the mini version of Gargantua — the Gargantua-R1-Wee : prithivMLmods/Gargantua-R1-Wee

from datasets import load_dataset

dataset = load_dataset("prithivMLmods/Gargantua-R1-Wee", split="train")

The composition spans 73.93% core mathematical reasoning involving problems, proofs, and computational challenges, 12.11% across diverse scientific domains such as physics, chemistry, biology, and interdisciplinary topics, 11.35% in competitive coding covering algorithms and data structures, 1.37% in academic science focusing on research-level methodology, 0.95% in creative and analytical reasoning through logic puzzles and problem-solving tasks, 0.25% in specialized technical areas like MLOps, LLMs, diffusion models, and CUDA, and 0.06% involving data from graphs and charts converted into structured JSON formats. Designed with both rich contextual depth and formal structural clarity, Gargantua-R1-Compact is an optimal resource for advancing research in symbolic reasoning, interpretability, and high-precision question answering in mathematical domains.

✦ Collection : prithivMLmods/gargantua-r1-mod-6896bfd7834e82b89ad2b38b


To know more about it, visit the dataset card of the respective dataset. !!
prithivMLmods 
posted an update 11 days ago
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2183
I've added the demo of the openbmb/MiniCPM-V-4 model to the Hugging Face Space:
prithivMLmods/Multimodal-VLM-Thinking

✨ MiniCPM-V 4.0 is the latest efficient model in the MiniCPM-V series. The model is built based on SigLIP2-400M and MiniCPM4-3B, with a total of 4.1B parameters. It inherits the strong single-image, multi-image, and video understanding performance of MiniCPM-V 2.6 with largely improved efficiency.

✨ With only 4.1B parameters, MiniCPM-V 4.0 achieves an average score of 69.0 on OpenCompass, a comprehensive evaluation of 8 popular benchmarks. This performance surpasses GPT-4.1-mini-20250414, MiniCPM-V 2.6 (8.1B parameters, OpenCompass 65.2), and Qwen2.5-VL-3B-Instruct (3.8B parameters, OpenCompass 64.5). It also shows good performance in multi-image and video understanding.

The community GPU grant was given by Hugging Face — special thanks to them. 🤗🚀

To know more about it, visit the model card of the respective model. !!
Evanwu50020 
authored a paper 13 days ago
JingzeShi 
authored a paper 13 days ago
wubingheng 
authored a paper 13 days ago
KingNish 
posted an update 14 days ago