Training Agents to Evolve with Their Harness: TaoLive Digital Avatar Agent Technical Report
Paper โข 2608.15763 โข Published โข 45
AI & ML interests
LLM, LORA, text generation, prompt-tuning
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yuuhanย
submitted a
paper to Daily Papers 3 days ago
yuuhanย
authored a
paper 3 days ago
prithivMLmodsย
posted an update 3 days ago
Post
2374
ImageShield-MMCF โ Multimodal Content Filter is a multimodal content-safety classifier built on top of Qwen3.5 and is now available on Hugging Face!
This is the preview initial version (v1.0) of the model, designed to classify visual content as Safe or Unsafe, with a particular focus on detecting Non-Consensual Intimate Imagery (NCII) and other potentially sensitive visual content.
The demo is implemented in the prithivMLmods/opencaption-4b-vl-sft Space, which serves as an active content-safety layer for computer vision tasks. It helps block NCII content generation and paves the way for more meaningful and responsible creativity.
โน ImageShield-MMCF-0.8B: prithivMLmods/ImageShield-MMCF-0.8B
โน ImageShield-MMCF-2B: prithivMLmods/ImageShield-MMCF-2B
This is the preview initial version (v1.0) of the model, designed to classify visual content as Safe or Unsafe, with a particular focus on detecting Non-Consensual Intimate Imagery (NCII) and other potentially sensitive visual content.
The demo is implemented in the prithivMLmods/opencaption-4b-vl-sft Space, which serves as an active content-safety layer for computer vision tasks. It helps block NCII content generation and paves the way for more meaningful and responsible creativity.
โน ImageShield-MMCF-0.8B: prithivMLmods/ImageShield-MMCF-0.8B
โน ImageShield-MMCF-2B: prithivMLmods/ImageShield-MMCF-2B
- 2 replies
prithivMLmodsย
posted an update 12 days ago
Post
5231
The Qwen3.8 27B demo for object grounding is now available on Hugging Face Spaces.
It features three tasks: Object Detection (Bounding Boxes), Point Localization (Keypoints), and Spatial Guidance (Path Mapping).
Try it now: prithivMLmods/Qwen3.8-27B-Object-Detection
It features three tasks: Object Detection (Bounding Boxes), Point Localization (Keypoints), and Spatial Guidance (Path Mapping).
Try it now: prithivMLmods/Qwen3.8-27B-Object-Detection
pinzhenchenย
authored a
paper 19 days ago
pinzhenchenย
submitted a
paper to Daily Papers 19 days ago
prithivMLmodsย
posted an update about 1 month ago
Post
5525
Made a demo for Text/Image-to-3D Video and Image-to-3D Video asset generation using TRELLIS.2. It is paired with Z-Image-Turbo to accelerate the input image preprocessing pipeline, streamlining the Image-to-3D workflow. The generated GLB (GL Transmission Format) files are converted into MP4 (MPEG-4) videos, making them easy to preview and share. Try it now on Hugging Face Spaces.๐ค
โ Image-to-3D-Video-Asset-Generator: prithivMLmods/Image-to-3D-Video-Asset-Generator
โ collection: https://huggingface.co/collections/prithivMLmods/multimodal-implementations
โ github: https://github.com/PRITHIVSAKTHIUR/Image-to-3D-Video-Asset-Generator
โคท To learn more, visit the app page or the respective model pages.
โ Image-to-3D-Video-Asset-Generator: prithivMLmods/Image-to-3D-Video-Asset-Generator
โ collection: https://huggingface.co/collections/prithivMLmods/multimodal-implementations
โ github: https://github.com/PRITHIVSAKTHIUR/Image-to-3D-Video-Asset-Generator
โคท To learn more, visit the app page or the respective model pages.
pinzhenchenย
authored 6
papers 2 months ago
CHARM: Calibrating Reward Models With Chatbot Arena Scores
Paper โข 2504.10045 โข Published โข 1
XL-Instruct: Synthetic Data for Cross-Lingual Open-Ended Generation
Paper โข 2503.22973 โข Published
DocHPLT: A Massively Multilingual Document-Level Translation Dataset
Paper โข 2508.13079 โข Published โข 1
MatheMagic: Generating Dynamic Mathematics Benchmarks Robust to Memorization
Paper โข 2510.05962 โข Published
HPLT 3.0: Very Large-Scale Multilingual Resources for LLM and MT. Mono- and Bi-lingual Data, Multilingual Evaluation, and Pre-Trained Models
Paper โข 2511.01066 โข Published โข 3
Reinforcement Learning Elicits Contextual Learning of Unseen Language Translation
Paper โข 2606.06428 โข Published โข 25
prithivMLmodsย
posted an update 3 months ago
Post
7959
Wan2.2-I2V-Fast with highly upscaled sequential frame sampling is now available as a Spaces demo, built using Wan2.2-I2V and FLUX.2-Klein. Try the demo using the links below.๐
โ wan2.2-i2v-fast : prithivMLmods/Wan2.2-Fast
โ github: https://github.com/prithivsakthiur/wan2.2-i2v-fast
โ collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
โคท To learn more, visit the app page or the respective model pages.
โ wan2.2-i2v-fast : prithivMLmods/Wan2.2-Fast
โ github: https://github.com/prithivsakthiur/wan2.2-i2v-fast
โ collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
โคท To learn more, visit the app page or the respective model pages.
prithivMLmodsย
posted an update 3 months ago
Post
2292
Dropping the collection of Qwen 3.5/3.6 MTP GGUF quants. ๐ค
๐ Collection 1: https://huggingface.co/collections/prithivMLmods/mtp-qwen-35-36-moe-stable
๐ Collection 2: https://huggingface.co/collections/prithivMLmods/mtp-qwen-35-36-stable
> To learn more, visit the respective model pages.
๐ Collection 1: https://huggingface.co/collections/prithivMLmods/mtp-qwen-35-36-moe-stable
๐ Collection 2: https://huggingface.co/collections/prithivMLmods/mtp-qwen-35-36-stable
> To learn more, visit the respective model pages.
prithivMLmodsย
posted an update 3 months ago
Post
6324
PiD โ Pixel Diffusion Decoder Image Edit Upscale and Image Generation Upscale, an all-in-one demo, is now live on Spaces! Great improvements in realism-based image generation and editing are powered by FLUX.2-Klein, while image generation is paired with Z-Image, and upscaling is enabled by default!
๐ค Space: prithivMLmods/PiD-Image-Upscaler
๐ Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
๐ค > To learn more, visit the app page or the respective model pages.
๐ค Space: prithivMLmods/PiD-Image-Upscaler
๐ Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
๐ค > To learn more, visit the app page or the respective model pages.
prithivMLmodsย
posted an update 3 months ago
Post
5642
I've made 8 Spaces in the Qwen-Image-Edit series, and out of them, 5 Spaces reached โSpace of the Weekโ! A few Spaces are still topping the list even after many months.
Cumulatively, the series has crossed 8.2 million+ ZeroGPU runs and nearly 4 million visitors overall.
Thanks for all the community support! ๐คโค๏ธ
๐ Spaces: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
Cumulatively, the series has crossed 8.2 million+ ZeroGPU runs and nearly 4 million visitors overall.
Thanks for all the community support! ๐คโค๏ธ
๐ Spaces: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
- 4 replies
Ujjwal-Tyagiย
posted an update 4 months ago
Post
555
6 Open-Source Libraries to FineTune LLMs
1. Unsloth
GitHub: https://github.com/unslothai/unsloth
โ Fastest way to fine-tune LLMs locally
โ Optimized for low VRAM (even laptops)
โ Plug-and-play with Hugging Face models
2. Axolotl
GitHub: https://github.com/OpenAccess-AI-Collective/axolotl
โ Flexible LLM fine-tuning configs
โ Supports LoRA, QLoRA, multi-GPU
โ Great for custom training pipelines
3. TRL (Transformer Reinforcement Learning)
GitHub: https://github.com/huggingface/trl
โ RLHF, DPO, PPO for LLM alignment
โ Built on Hugging Face ecosystem
โ Essential for post-training optimization
4. DeepSpeed
GitHub: https://github.com/microsoft/DeepSpeed
โ Train massive models efficiently
โ Memory + speed optimization
โ Industry standard for scaling
5. LLaMA-Factory
GitHub: https://github.com/hiyouga/LLaMA-Factory
โ All-in-one fine-tuning UI + CLI
โ Supports multiple models (LLaMA, Qwen, etc.)
โ Beginner-friendly + powerful
6. PEFT
GitHub: https://github.com/huggingface/peft
โ Fine-tune with minimal compute
โ LoRA, adapters, prefix tuning
โ Best for cost-efficient training
1. Unsloth
GitHub: https://github.com/unslothai/unsloth
โ Fastest way to fine-tune LLMs locally
โ Optimized for low VRAM (even laptops)
โ Plug-and-play with Hugging Face models
2. Axolotl
GitHub: https://github.com/OpenAccess-AI-Collective/axolotl
โ Flexible LLM fine-tuning configs
โ Supports LoRA, QLoRA, multi-GPU
โ Great for custom training pipelines
3. TRL (Transformer Reinforcement Learning)
GitHub: https://github.com/huggingface/trl
โ RLHF, DPO, PPO for LLM alignment
โ Built on Hugging Face ecosystem
โ Essential for post-training optimization
4. DeepSpeed
GitHub: https://github.com/microsoft/DeepSpeed
โ Train massive models efficiently
โ Memory + speed optimization
โ Industry standard for scaling
5. LLaMA-Factory
GitHub: https://github.com/hiyouga/LLaMA-Factory
โ All-in-one fine-tuning UI + CLI
โ Supports multiple models (LLaMA, Qwen, etc.)
โ Beginner-friendly + powerful
6. PEFT
GitHub: https://github.com/huggingface/peft
โ Fine-tune with minimal compute
โ LoRA, adapters, prefix tuning
โ Best for cost-efficient training
- 1 reply
prithivMLmodsย
posted an update 4 months ago
Post
5975
Multimodal-Edge Demo, a node-based inference canvas demo, is now live on Spaces. It features node-based Transformers for fast inference across 10+ edge-device multimodal models on the Hub, all within a single space. The series includes models from Qwen3.5, Qwen3-VL, Gemma 4, and the LFM 2.5 VL model series, with support for reasoning and grounding tasks.
๐ค Demo: prithivMLmods/Multimodal-Edge-Node
๐ GitHub: https://github.com/PRITHIVSAKTHIUR/Multimodal-Edge-Node
โ Multimodal Apps Collections: https://huggingface.co/collections/prithivMLmods/hall-of-multimodal-apps
๐ค > To learn more, visit the app page or the respective model pages.
๐ค Demo: prithivMLmods/Multimodal-Edge-Node
๐ GitHub: https://github.com/PRITHIVSAKTHIUR/Multimodal-Edge-Node
โ Multimodal Apps Collections: https://huggingface.co/collections/prithivMLmods/hall-of-multimodal-apps
๐ค > To learn more, visit the app page or the respective model pages.
Ujjwal-Tyagiย
posted an update 4 months ago
Post
487
This is the best set of AI and ML books and a full guide to learning machine learning from the ground up. This is my study material that I used, so I thought it would be helpful to share it with others. Like, share, and add it to your collection at Ujjwal-Tyagi/ai-ml-foundations-book-collection.