CHARM: Calibrating Reward Models With Chatbot Arena Scores
Paper β’ 2504.10045 β’ Published
AI & ML interests
LLM, LORA, text generation, prompt-tuning
Recent Activity
View all activity
pinzhenchenΒ
authored 6
papers about 1 month ago
XL-Instruct: Synthetic Data for Cross-Lingual Open-Ended Generation
Paper β’ 2503.22973 β’ Published
DocHPLT: A Massively Multilingual Document-Level Translation Dataset
Paper β’ 2508.13079 β’ Published β’ 1
MatheMagic: Generating Dynamic Mathematics Benchmarks Robust to Memorization
Paper β’ 2510.05962 β’ 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 β’ 2511.01066 β’ Published β’ 3
Reinforcement Learning Elicits Contextual Learning of Unseen Language Translation
Paper β’ 2606.06428 β’ Published β’ 25
prithivMLmodsΒ
posted an update about 1 month ago
Post
7792
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.π
β wan2.2-i2v-fast : prithivMLmods/wan2.2-i2v-fast
β github: https://github.com/prithivsakthiur/wan2.2-i2v-fast
β collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
β€· To learn more, visit the app page or the respective model pages.
β wan2.2-i2v-fast : prithivMLmods/wan2.2-i2v-fast
β github: https://github.com/prithivsakthiur/wan2.2-i2v-fast
β collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
β€· To learn more, visit the app page or the respective model pages.
prithivMLmodsΒ
posted an update about 2 months ago
Post
2258
Dropping the collection of Qwen 3.5/3.6 MTP GGUF quants. π€
π Collection 1: https://huggingface.co/collections/prithivMLmods/mtp-qwen-35-36-moe-stable
π Collection 2: https://huggingface.co/collections/prithivMLmods/mtp-qwen-35-36-stable
> To learn more, visit the respective model pages.
π Collection 1: https://huggingface.co/collections/prithivMLmods/mtp-qwen-35-36-moe-stable
π Collection 2: https://huggingface.co/collections/prithivMLmods/mtp-qwen-35-36-stable
> To learn more, visit the respective model pages.
prithivMLmodsΒ
posted an update 2 months ago
Post
6286
PiD β 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!
π€ Space: prithivMLmods/PiD-Image-Upscaler
π Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
π€ > To learn more, visit the app page or the respective model pages.
π€ Space: prithivMLmods/PiD-Image-Upscaler
π Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
π€ > To learn more, visit the app page or the respective model pages.
prithivMLmodsΒ
posted an update 2 months ago
Post
5621
I've made 8 Spaces in the Qwen-Image-Edit series, and out of them, 5 Spaces reached βSpace of the Weekβ! 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! π€β€οΈ
π 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! π€β€οΈ
π Spaces: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
- 4 replies
Ujjwal-TyagiΒ
posted an update 3 months ago
Post
482
6 Open-Source Libraries to FineTune LLMs
1. Unsloth
GitHub: https://github.com/unslothai/unsloth
β Fastest way to fine-tune LLMs locally
β Optimized for low VRAM (even laptops)
β Plug-and-play with Hugging Face models
2. Axolotl
GitHub: https://github.com/OpenAccess-AI-Collective/axolotl
β Flexible LLM fine-tuning configs
β Supports LoRA, QLoRA, multi-GPU
β Great for custom training pipelines
3. TRL (Transformer Reinforcement Learning)
GitHub: https://github.com/huggingface/trl
β RLHF, DPO, PPO for LLM alignment
β Built on Hugging Face ecosystem
β Essential for post-training optimization
4. DeepSpeed
GitHub: https://github.com/microsoft/DeepSpeed
β Train massive models efficiently
β Memory + speed optimization
β Industry standard for scaling
5. LLaMA-Factory
GitHub: https://github.com/hiyouga/LLaMA-Factory
β All-in-one fine-tuning UI + CLI
β Supports multiple models (LLaMA, Qwen, etc.)
β Beginner-friendly + powerful
6. PEFT
GitHub: https://github.com/huggingface/peft
β Fine-tune with minimal compute
β LoRA, adapters, prefix tuning
β Best for cost-efficient training
1. Unsloth
GitHub: https://github.com/unslothai/unsloth
β Fastest way to fine-tune LLMs locally
β Optimized for low VRAM (even laptops)
β Plug-and-play with Hugging Face models
2. Axolotl
GitHub: https://github.com/OpenAccess-AI-Collective/axolotl
β Flexible LLM fine-tuning configs
β Supports LoRA, QLoRA, multi-GPU
β Great for custom training pipelines
3. TRL (Transformer Reinforcement Learning)
GitHub: https://github.com/huggingface/trl
β RLHF, DPO, PPO for LLM alignment
β Built on Hugging Face ecosystem
β Essential for post-training optimization
4. DeepSpeed
GitHub: https://github.com/microsoft/DeepSpeed
β Train massive models efficiently
β Memory + speed optimization
β Industry standard for scaling
5. LLaMA-Factory
GitHub: https://github.com/hiyouga/LLaMA-Factory
β All-in-one fine-tuning UI + CLI
β Supports multiple models (LLaMA, Qwen, etc.)
β Beginner-friendly + powerful
6. PEFT
GitHub: https://github.com/huggingface/peft
β Fine-tune with minimal compute
β LoRA, adapters, prefix tuning
β Best for cost-efficient training
- 1 reply
prithivMLmodsΒ
posted an update 3 months ago
Post
5962
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.
π€ Demo: prithivMLmods/Multimodal-Edge-Node
π GitHub: https://github.com/PRITHIVSAKTHIUR/Multimodal-Edge-Node
β Multimodal Apps Collections: https://huggingface.co/collections/prithivMLmods/hall-of-multimodal-apps
π€ > To learn more, visit the app page or the respective model pages.
π€ Demo: prithivMLmods/Multimodal-Edge-Node
π GitHub: https://github.com/PRITHIVSAKTHIUR/Multimodal-Edge-Node
β Multimodal Apps Collections: https://huggingface.co/collections/prithivMLmods/hall-of-multimodal-apps
π€ > To learn more, visit the app page or the respective model pages.
Ujjwal-TyagiΒ
posted an update 3 months ago
Post
422
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.
prithivMLmodsΒ
posted an update 3 months ago
Post
1942
Now, a collection of various compression schemes for Qwen3.6 and the abliterated version 1 of dense models is available on the Hub. Check it out via the links below. π
π Qwen3.6-MoE: https://huggingface.co/collections/prithivMLmods/qwen36-35b-a3b-compressions
π Qwen3.6-27B Compressions: https://huggingface.co/collections/prithivMLmods/qwen36-27b-compressions
π€ > To learn more, visit the app page or the respective model pages.
π Qwen3.6-MoE: https://huggingface.co/collections/prithivMLmods/qwen36-35b-a3b-compressions
π Qwen3.6-27B Compressions: https://huggingface.co/collections/prithivMLmods/qwen36-27b-compressions
π€ > To learn more, visit the app page or the respective model pages.
babytreeccΒ
authored a
paper 3 months ago
Ujjwal-TyagiΒ
posted an update 3 months ago
Post
3973
We are hiring at Shirova AI. We need AI researchers and engineers to work in our research lab. Shirova AI is a research lab in India, so we can help our researchers move to nearby workspaces or let them work from home without ever coming to the lab. We're building our founding team, so the pay will be good. You can learn, so don't hesitate to mail us at: careers@shirova.com
prithivMLmodsΒ
posted an update 3 months ago
Post
4241
HY-World-2.0 β A Multi-Modal World Model for Reconstructing, Generating, and Simulating 3D Worlds is now available on Spaces, and it works both as native Gradio components and in Gradio server mode.
> HY-World-2.0-Demo: prithivMLmods/HY-World-2.0-Demo
> HY-World-2.0 [Server Mode]: prithivMLmods/HY-World-2.0-Demo
> Featuring 3D reconstruction and Gaussian splats with the Rerun viewer, along with camera poses, depth maps, and surface normals.
> In Server Mode, Gradio is served via FastAPI, with FastAPI remaining the top-level server.
> Model: tencent/HY-World-2.0
> GitHub: https://github.com/PRITHIVSAKTHIUR/HY-World-2.0-Demo
π€To learn more, visit the app page or the respective model pages.
> HY-World-2.0-Demo: prithivMLmods/HY-World-2.0-Demo
> HY-World-2.0 [Server Mode]: prithivMLmods/HY-World-2.0-Demo
> Featuring 3D reconstruction and Gaussian splats with the Rerun viewer, along with camera poses, depth maps, and surface normals.
> In Server Mode, Gradio is served via FastAPI, with FastAPI remaining the top-level server.
> Model: tencent/HY-World-2.0
> GitHub: https://github.com/PRITHIVSAKTHIUR/HY-World-2.0-Demo
π€To learn more, visit the app page or the respective model pages.
prithivMLmodsΒ
posted an update 4 months ago
Post
6262
A new comparator on Spaces showcases Standard FLUX.2 Decoder vs. FLUX.2 Small Decoder. The Small Decoder is ~1.4Γ faster, uses ~1.4Γ less VRAM, and maintains near-identical image quality. It has ~28M parameters with narrower channels [96, 192, 384, 384] vs. [128, 256, 512, 512], and the demo supports sequence generation by running both decoders simultaneously and comparing the results side by side.
π€ Comparator: https://huggingface.co/spaces/prithivMLmods/Flux.2-4B-Decoder-Comparator
π FLUX.2-small-decoder: black-forest-labs/FLUX.2-small-decoder
π GitHub: https://github.com/PRITHIVSAKTHIUR/Flux.2-4B-Encoder-Comparator
π Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
π€ > App built on the Gradio SDK. To learn more, visit the app page or the respective model pages.
π€ Comparator: https://huggingface.co/spaces/prithivMLmods/Flux.2-4B-Decoder-Comparator
π FLUX.2-small-decoder: black-forest-labs/FLUX.2-small-decoder
π GitHub: https://github.com/PRITHIVSAKTHIUR/Flux.2-4B-Encoder-Comparator
π Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
π€ > App built on the Gradio SDK. To learn more, visit the app page or the respective model pages.
prithivMLmodsΒ
posted an update 4 months ago
Post
4273
Now, a collection of various compression schemes for Gemma 4 and the abliterated version 1 of dense models is available on the Hub. Check it out via the links below. π
πGemma 4 Compression(s)- https://huggingface.co/collections/prithivMLmods/gemma-4-compressions
πGemma 4 Uncensored [MAX] + Compression(s) - [`Ξ² ]- https://huggingface.co/collections/prithivMLmods/gemma-4-uncensored-max-compressions
πGemma 4 Compression(s) - MoE- https://huggingface.co/collections/prithivMLmods/gemma-4-compressions-moe
πGemma-4 F32 GGUF- https://huggingface.co/collections/prithivMLmods/gemma-4-f32-gguf
π€ > To learn more, visit the app page or the respective model pages.
πGemma 4 Compression(s)- https://huggingface.co/collections/prithivMLmods/gemma-4-compressions
πGemma 4 Uncensored [MAX] + Compression(s) - [`Ξ² ]- https://huggingface.co/collections/prithivMLmods/gemma-4-uncensored-max-compressions
πGemma 4 Compression(s) - MoE- https://huggingface.co/collections/prithivMLmods/gemma-4-compressions-moe
πGemma-4 F32 GGUF- https://huggingface.co/collections/prithivMLmods/gemma-4-f32-gguf
π€ > To learn more, visit the app page or the respective model pages.
prithivMLmodsΒ
posted an update 4 months ago
Post
2374
Now the demo for image detection based on SAM3 and Gemma-4 (*Filter) is available on Spaces, using full-fledged Transformers inference with multimodal reasoning for processed images. It also supports video segmentation (mask), video segmentation (annotation), and image click segmentation.
π€ Demo Space: prithivMLmods/SAM3-Gemma4-CUDA
π₯½ SAM3: facebook/sam3
π gemma-4-E2B-it: google/gemma-4-E2B-it
To learn more, visit the app page or the respective model pages.
π€ Demo Space: prithivMLmods/SAM3-Gemma4-CUDA
π₯½ SAM3: facebook/sam3
π gemma-4-E2B-it: google/gemma-4-E2B-it
To learn more, visit the app page or the respective model pages.
- 1 reply