PrimeIntellect/INTELLECT-3
Text Generation
•
107B
•
Updated
•
14.8k
•
182
Hyungyu seo
hgseo
·
AI & ML interests
None yet
Recent Activity
liked
a model
11 days ago
PrimeIntellect/INTELLECT-3
liked
a dataset
11 days ago
PrimeIntellect/SYNTHETIC-2
liked
a Space
28 days ago
bigcode/bigcode-models-leaderboard
Organizations
liked
a
model
11 days ago
liked
a
dataset
11 days ago
liked
a
Space
28 days ago
liked
a
model
29 days ago
liked
2
datasets
about 1 month ago
liked
a
model
about 1 month ago
liked
a
model
2 months ago
reacted to
Sri-Vigneshwar-DJ's
post with 🚀
2 months ago
Post
3331
🚀 Qwen3-Omni for Marketing: A Game-Changer
Just wanted to share something exciting I've been exploring—Qwen3-Omni and how it's transforming marketing workflows.
What makes it special? At Hawky.ai we are started experimenting with Qwen3 recently for Analysis and Optimization.
Unlike traditional tools that look at text, images, or audio separately, Qwen3-Omni analyzes everything together. It handles 119 languages, processes 40-minute audio sequences, and understands both images and videos—all at once.
The cool part? It's 2-3x faster than similar models thanks to its MoE architecture.
Real applications I'm seeing:
Ad Analysis: It scores video ads by combining visual elements, audio tone, and text—giving 25% better CTR predictions than single-mode tools.
Campaign Localization: Drop in one ad, get 10 localized versions with native voiceovers in under a minute. Perfect for testing across markets.
Market Research: Feed it competitor content, podcasts, or UGC videos. It extracts actionable insights like "3-second hooks boost retention by 15%" and saves about 70% of analysis time.
Quality Checks: Automatically catches lip-sync errors and audio-visual mismatches.
Full technical breakdown: https://huggingface.co/blog/Sri-Vigneshwar-DJ/hawky-aiqwen3-omni-advanced-architecture-and-marke
Has anyone else been experimenting with multimodal models for marketing? Would love to hear what you're building!
#MultimodalAI #MarTech #OpenSource
Just wanted to share something exciting I've been exploring—Qwen3-Omni and how it's transforming marketing workflows.
What makes it special? At Hawky.ai we are started experimenting with Qwen3 recently for Analysis and Optimization.
Unlike traditional tools that look at text, images, or audio separately, Qwen3-Omni analyzes everything together. It handles 119 languages, processes 40-minute audio sequences, and understands both images and videos—all at once.
The cool part? It's 2-3x faster than similar models thanks to its MoE architecture.
Real applications I'm seeing:
Ad Analysis: It scores video ads by combining visual elements, audio tone, and text—giving 25% better CTR predictions than single-mode tools.
Campaign Localization: Drop in one ad, get 10 localized versions with native voiceovers in under a minute. Perfect for testing across markets.
Market Research: Feed it competitor content, podcasts, or UGC videos. It extracts actionable insights like "3-second hooks boost retention by 15%" and saves about 70% of analysis time.
Quality Checks: Automatically catches lip-sync errors and audio-visual mismatches.
Full technical breakdown: https://huggingface.co/blog/Sri-Vigneshwar-DJ/hawky-aiqwen3-omni-advanced-architecture-and-marke
Has anyone else been experimenting with multimodal models for marketing? Would love to hear what you're building!
#MultimodalAI #MarTech #OpenSource
upvoted
a
paper
3 months ago
liked
a
model
3 months ago
upvoted
a
collection
3 months ago
liked
a
model
3 months ago
liked
a
dataset
3 months ago
reacted to
prithivMLmods's
post with ❤️
3 months ago
Post
7224
Introducing Gliese-OCR-7B-Post1.0, a document content-structure retrieval VLM designed for content extraction(OCRs) and summarization. This is the third model in the Camel Doc OCR VLM series, following Camel-Doc-OCR-062825. The new version fixes formal table reconstruction issues in both En and Zh, achieving optimal performance for long-context inferences. This model also shows significant improvements in LaTeX and Markdown rendering for OCR tasks.
🤗 Gliese-OCR-7B-Post1.0 : prithivMLmods/Gliese-OCR-7B-Post1.0
📌 Gliese-Post1.0 Collection : prithivMLmods/gliese-post10-68c52c4a6ca4935f5259a6d7
⬅️ Previous Versions : prithivMLmods/Camel-Doc-OCR-062825
🧨 Gliese-OCR-7B-Post1.0 (4-bit) Notebook Demo on T4 : prithivMLmods/Gliese-OCR-7B-Post1.0
📖 GitHub [Gliese-OCR-7B-Post1.0(4-bit)-reportlab] : https://tinyurl.com/ys7zuerc
Other Collections:
➔ Multimodal Implementations : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0
➔ Multimodal VLMs - Aug'25 : prithivMLmods/multimodal-vlms-aug25-68a56aac39fe8084f3c168bd
➔ Multimodal VLMs - July'25 : prithivMLmods/multimodal-vlms-until-july25-688312e6b840e1e156f13027
.
.
.
To know more about it, visit the app page or the respective model page!!
🤗 Gliese-OCR-7B-Post1.0 : prithivMLmods/Gliese-OCR-7B-Post1.0
📌 Gliese-Post1.0 Collection : prithivMLmods/gliese-post10-68c52c4a6ca4935f5259a6d7
⬅️ Previous Versions : prithivMLmods/Camel-Doc-OCR-062825
🧨 Gliese-OCR-7B-Post1.0 (4-bit) Notebook Demo on T4 : prithivMLmods/Gliese-OCR-7B-Post1.0
📖 GitHub [Gliese-OCR-7B-Post1.0(4-bit)-reportlab] : https://tinyurl.com/ys7zuerc
Other Collections:
➔ Multimodal Implementations : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0
➔ Multimodal VLMs - Aug'25 : prithivMLmods/multimodal-vlms-aug25-68a56aac39fe8084f3c168bd
➔ Multimodal VLMs - July'25 : prithivMLmods/multimodal-vlms-until-july25-688312e6b840e1e156f13027
.
.
.
To know more about it, visit the app page or the respective model page!!
- 2 replies
reacted to
tomaarsen's
post with ❤️
3 months ago
Post
5692
ModernBERT goes MULTILINGUAL! One of the most requested models I've seen, The Johns Hopkins University's CLSP has trained state-of-the-art massively multilingual encoders using the ModernBERT architecture: mmBERT.
Model details:
- 2 model sizes:
- jhu-clsp/mmBERT-small
- jhu-clsp/mmBERT-base
- Uses the ModernBERT architecture, but with the Gemma2 multilingual tokenizer (so: flash attention, alternating global/local attention, unpadding/sequence packing, etc.)
- Maximum sequence length of 8192 tokens, on the high end for encoders
- Trained on 1833 languages using DCLM, FineWeb2, and many more sources
- 3 training phases: 2.3T tokens pretraining on 60 languages, 600B tokens mid-training on 110 languages, and 100B tokens decay training on all 1833 languages.
- Both models are MIT Licensed, and the full datasets and intermediary checkpoints are also publicly released
Evaluation details:
- Very competitive with ModernBERT at equivalent sizes on English (GLUE, MTEB v2 English after finetuning)
- Consistently outperforms equivalently sized models on all Multilingual tasks (XTREME, classification, MTEB v2 Multilingual after finetuning)
- In short: beats commonly used multilingual base models like mDistilBERT, XLM-R (multilingual RoBERTa), multilingual MiniLM, etc.
- Additionally: the ModernBERT-based mmBERT is much faster than the alternatives due to its architectural benefits. Easily up to 2x throughput in common scenarios.
Check out the full blogpost with more details. It's super dense & gets straight to the point: https://huggingface.co/blog/mmbert
Based on these results, mmBERT should be the new go-to multilingual encoder base models at 300M and below. Do note that the mmBERT models are "base" models, i.e. they're currently only trained to perform Mask Filling. They'll need to be finetuned for downstream tasks like semantic search, classification, clustering, etc.
Model details:
- 2 model sizes:
- jhu-clsp/mmBERT-small
- jhu-clsp/mmBERT-base
- Uses the ModernBERT architecture, but with the Gemma2 multilingual tokenizer (so: flash attention, alternating global/local attention, unpadding/sequence packing, etc.)
- Maximum sequence length of 8192 tokens, on the high end for encoders
- Trained on 1833 languages using DCLM, FineWeb2, and many more sources
- 3 training phases: 2.3T tokens pretraining on 60 languages, 600B tokens mid-training on 110 languages, and 100B tokens decay training on all 1833 languages.
- Both models are MIT Licensed, and the full datasets and intermediary checkpoints are also publicly released
Evaluation details:
- Very competitive with ModernBERT at equivalent sizes on English (GLUE, MTEB v2 English after finetuning)
- Consistently outperforms equivalently sized models on all Multilingual tasks (XTREME, classification, MTEB v2 Multilingual after finetuning)
- In short: beats commonly used multilingual base models like mDistilBERT, XLM-R (multilingual RoBERTa), multilingual MiniLM, etc.
- Additionally: the ModernBERT-based mmBERT is much faster than the alternatives due to its architectural benefits. Easily up to 2x throughput in common scenarios.
Check out the full blogpost with more details. It's super dense & gets straight to the point: https://huggingface.co/blog/mmbert
Based on these results, mmBERT should be the new go-to multilingual encoder base models at 300M and below. Do note that the mmBERT models are "base" models, i.e. they're currently only trained to perform Mask Filling. They'll need to be finetuned for downstream tasks like semantic search, classification, clustering, etc.
reacted to
Kseniase's
post with 👍
3 months ago
Post
7042
10 Latest Preference Optimization Techniques
Models need feedback on what makes outputs “good” or “bad.” Policy optimization (PO) turns preferences and rewards into actual training signals. This field is evolving quickly, moving far beyond classics like PPO and GRPO. So here is our overview of 10 newest PO methods:
1. Pref-GRPO → Pref-GRPO: Pairwise Preference Reward-based GRPO for Stable Text-to-Image Reinforcement Learning (2508.20751)
Stabilizes text-to-image reinforcement learning (RL) with pairwise preference rewards and a unified UNIGENBENCH benchmark
2. PVPO (Policy with Value Preference Optimization) → PVPO: Pre-Estimated Value-Based Policy Optimization for Agentic Reasoning (2508.21104)
This critic-free RL method uses a pre-trained model as a reference anchor to reduce bias and guide learning, selecting high-value examples through data pre-sampling
3. DCPO (Dynamic Clipping Policy Optimization) → DCPO: Dynamic Clipping Policy Optimization (2509.02333)
Uses dynamic clipping, which adjusts probability limits per token for better token exploration, and smooth reward standardization to balance rewards over training steps and prevent wasted updates
4. ARPO (Agentic Reinforced Policy Optimization) → Agentic Reinforced Policy Optimization (2507.19849)
Optimizes multi-turn LLM agents that use external tools. It uses an entropy-based adaptive rollout to explore post-tool use and an advantage attribution method to better assign credit across steps, leading to more efficient tool use with fewer resources
5. GRPO-RoC (Group Relative Policy Optimization with Resampling-on-Correct) → rStar2-Agent: Agentic Reasoning Technical Report (2508.20722)
Oversamples rollouts, then resamples them to keep diverse mistakes and only the highest-quality correct answers. It reduces noises and ends up with stronger reasoning in a code environment
Read further below ⬇️
If you like this, also subscribe to the Turing post: https://www.turingpost.com/subscribe
Models need feedback on what makes outputs “good” or “bad.” Policy optimization (PO) turns preferences and rewards into actual training signals. This field is evolving quickly, moving far beyond classics like PPO and GRPO. So here is our overview of 10 newest PO methods:
1. Pref-GRPO → Pref-GRPO: Pairwise Preference Reward-based GRPO for Stable Text-to-Image Reinforcement Learning (2508.20751)
Stabilizes text-to-image reinforcement learning (RL) with pairwise preference rewards and a unified UNIGENBENCH benchmark
2. PVPO (Policy with Value Preference Optimization) → PVPO: Pre-Estimated Value-Based Policy Optimization for Agentic Reasoning (2508.21104)
This critic-free RL method uses a pre-trained model as a reference anchor to reduce bias and guide learning, selecting high-value examples through data pre-sampling
3. DCPO (Dynamic Clipping Policy Optimization) → DCPO: Dynamic Clipping Policy Optimization (2509.02333)
Uses dynamic clipping, which adjusts probability limits per token for better token exploration, and smooth reward standardization to balance rewards over training steps and prevent wasted updates
4. ARPO (Agentic Reinforced Policy Optimization) → Agentic Reinforced Policy Optimization (2507.19849)
Optimizes multi-turn LLM agents that use external tools. It uses an entropy-based adaptive rollout to explore post-tool use and an advantage attribution method to better assign credit across steps, leading to more efficient tool use with fewer resources
5. GRPO-RoC (Group Relative Policy Optimization with Resampling-on-Correct) → rStar2-Agent: Agentic Reasoning Technical Report (2508.20722)
Oversamples rollouts, then resamples them to keep diverse mistakes and only the highest-quality correct answers. It reduces noises and ends up with stronger reasoning in a code environment
Read further below ⬇️
If you like this, also subscribe to the Turing post: https://www.turingpost.com/subscribe
- 1 reply
liked
a
dataset
3 months ago
upvoted
a
collection
3 months ago
liked
a
dataset
4 months ago