noctrex/PaddleOCR-VL-1.5-GGUF
Image-to-Text β’ 0.5B β’ Updated
β’ 3.22k β’ 3
Jean Louis
JLouisBiz
AI & ML interests
- LLM for sales, marketing, promotion
- LLM for Website Revision System
- increasing quality of communication with customers
- helping clients access information faster
- saving people from financial troubles
Recent Activity
new activity
about 1 hour ago
noctrex/PaddleOCR-VL-1.5-GGUF:π Thank you for the PaddleOCR GGUF files! liked
a model about 1 hour ago
noctrex/PaddleOCR-VL-1.5-GGUF liked
a model about 3 hours ago
BAAI/bge-reranker-v2-m3 Organizations
π Thank you for the PaddleOCR GGUF files!
π₯ 1
#1 opened about 1 hour ago
by
JLouisBiz
liked a model about 1 hour ago
liked a model about 3 hours ago
replied to AINovice2005's post about 11 hours ago
Would this software liberate users from proprietary CUDA?
liked a Space 1 day ago
reacted to prithivMLmods's post with π₯ 1 day ago
Post
3840
The Qwen3.5 Multimodal Understanding Demo, powered by Qwen3.5-2B, is now available on HF Spaces! It is a lightweight model designed for fast image and video reasoning. Built with Gradio, the demo showcases Image QA, Video QA, object detection, and 2D point tracking, along with real-time token streaming.
π€ Demo: prithivMLmods/Qwen-3.5-HF-Demo
β Collection: https://huggingface.co/collections/prithivMLmods/multimodal-implementations
π Qwen3.5-2B: Qwen/Qwen3.5-2B
To learn more, visit the app page or the respective model pages.
π€ Demo: prithivMLmods/Qwen-3.5-HF-Demo
β Collection: https://huggingface.co/collections/prithivMLmods/multimodal-implementations
π Qwen3.5-2B: Qwen/Qwen3.5-2B
To learn more, visit the app page or the respective model pages.
reacted to SeaWolf-AI's post with π₯π 1 day ago
Post
4646
ALL Bench Leaderboard β Structural Problems in AI Benchmarking and the Case for Unified Evaluation
FINAL-Bench/all-bench-leaderboard
The AI benchmark ecosystem has three structural problems. Major benchmarks like MMLU have surpassed 90%, losing discriminative power. Most leaderboards publish unverified self-reported scores β our cross-verification found Claude Opus 4.6's ARC-AGI-2 listed as 37.6% (actual: 68.8%), Gemini 3.1 Pro as 88.1% (actual: 77.1%). OpenAI's own audit confirmed 59.4% of SWE-bench Verified tasks are defective, yet it remains widely used.
ALL Bench addresses this by comparing 91 models across 6 modalities (LLM Β· VLM Β· Agent Β· Image Β· Video Β· Music) with 3-tier confidence badges (ββ cross-verified Β· β single-source Β· ~ self-reported). Composite scoring uses a 5-Axis Framework and replaces SWE-Verified with contamination-resistant LiveCodeBench.
Key finding: metacognition is the largest blind spot. FINAL Bench shows Error Recovery explains 94.8% of self-correction variance, yet only 9 of 42 models are even measured. The 9.2-point spread (Kimi K2.5: 68.71 β rank 9: 59.5) is 3Γ the GPQA top-model spread, suggesting metacognition may be the single biggest differentiator among frontier models today.
VLM cross-verification revealed rank reversals β Claude Opus 4.6 leads MMMU-Pro (85.1%) while Gemini 3 Flash leads MMMU (87.6%), producing contradictory rankings between the two benchmarks.
π Article: https://huggingface.co/blog/FINAL-Bench/all-bench
π¦ Dataset: FINAL-Bench/ALL-Bench-Leaderboard
β‘ GitHub: https://github.com/final-bench/ALL-Bench-Leaderboard
π Leaderboard: FINAL-Bench/all-bench-leaderboard
𧬠FINAL Bench: FINAL-Bench/Metacognitive
FINAL-Bench/all-bench-leaderboard
The AI benchmark ecosystem has three structural problems. Major benchmarks like MMLU have surpassed 90%, losing discriminative power. Most leaderboards publish unverified self-reported scores β our cross-verification found Claude Opus 4.6's ARC-AGI-2 listed as 37.6% (actual: 68.8%), Gemini 3.1 Pro as 88.1% (actual: 77.1%). OpenAI's own audit confirmed 59.4% of SWE-bench Verified tasks are defective, yet it remains widely used.
ALL Bench addresses this by comparing 91 models across 6 modalities (LLM Β· VLM Β· Agent Β· Image Β· Video Β· Music) with 3-tier confidence badges (ββ cross-verified Β· β single-source Β· ~ self-reported). Composite scoring uses a 5-Axis Framework and replaces SWE-Verified with contamination-resistant LiveCodeBench.
Key finding: metacognition is the largest blind spot. FINAL Bench shows Error Recovery explains 94.8% of self-correction variance, yet only 9 of 42 models are even measured. The 9.2-point spread (Kimi K2.5: 68.71 β rank 9: 59.5) is 3Γ the GPQA top-model spread, suggesting metacognition may be the single biggest differentiator among frontier models today.
VLM cross-verification revealed rank reversals β Claude Opus 4.6 leads MMMU-Pro (85.1%) while Gemini 3 Flash leads MMMU (87.6%), producing contradictory rankings between the two benchmarks.
π Article: https://huggingface.co/blog/FINAL-Bench/all-bench
π¦ Dataset: FINAL-Bench/ALL-Bench-Leaderboard
β‘ GitHub: https://github.com/final-bench/ALL-Bench-Leaderboard
π Leaderboard: FINAL-Bench/all-bench-leaderboard
𧬠FINAL Bench: FINAL-Bench/Metacognitive
reacted to Lyte's post with π 1 day ago
Post
3649
Introducing Nanochat Moroccan
Nanochat Moroccan is the first language model family built specifically for Moroccan Darija.
This project brings together a small family of models and datasets centered on Darija, with the goal of building something genuinely useful for a language that is still underserved in AI.
1. Models
- KandirResearch/Nanochat-Moroccan-Base-0.7B
- KandirResearch/Nanochat-Moroccan-Instruct-0.7B-pt-raw
- KandirResearch/Nanochat-Moroccan-Instruct-0.7B
2. Data
- Lyte/darija-pretraining-corpus
- Lyte/darija-pretraining-corpus-nanochat
- Lyte/Moroccan-Darija-Instruct-573K
- GemMaroc/TULU-3-50k-darija-english
3. Collection
- https://huggingface.co/collections/KandirResearch/nanochat-the-first-moroccan-darija-language-model-family
Moroccan Darija is spoken by millions of people, yet it remains underrepresented in language technology. Nanochat Moroccan is a step toward building tools that take the language seriously.
You are welcome to try it and chat with it here:
Lyte/Nanochat-Moroccco-Instruct
Nanochat Moroccan is the first language model family built specifically for Moroccan Darija.
This project brings together a small family of models and datasets centered on Darija, with the goal of building something genuinely useful for a language that is still underserved in AI.
1. Models
- KandirResearch/Nanochat-Moroccan-Base-0.7B
- KandirResearch/Nanochat-Moroccan-Instruct-0.7B-pt-raw
- KandirResearch/Nanochat-Moroccan-Instruct-0.7B
2. Data
- Lyte/darija-pretraining-corpus
- Lyte/darija-pretraining-corpus-nanochat
- Lyte/Moroccan-Darija-Instruct-573K
- GemMaroc/TULU-3-50k-darija-english
3. Collection
- https://huggingface.co/collections/KandirResearch/nanochat-the-first-moroccan-darija-language-model-family
Moroccan Darija is spoken by millions of people, yet it remains underrepresented in language technology. Nanochat Moroccan is a step toward building tools that take the language seriously.
You are welcome to try it and chat with it here:
Lyte/Nanochat-Moroccco-Instruct
liked 2 Spaces 2 days ago
liked a model 2 days ago
liked 4 models 4 days ago
The example python code just doesn't work anymore?
1
#9 opened 2 months ago
by
TheoryOfMadness
reacted to NJX-njx's post with π 8 days ago
Post
7412
Recently, I have open-sourced an AI emotional companion product based on openclaw, called opensoul.
On this platform, you can create a "soulmate" that matches your personality, and configure it with the skills, tools you want it to have, as well as the platforms it can integrate with (such as Telegram, Discord, etc.).
You can even create group chats, invite multiple agents and your friends to chat about recent events, discuss projects together, and so on.
On the one hand, I hope it can better accompany you in daily life by virtue of its unique memory mechanism, self-feedback and iteration mechanism, and the modeling of users' emotions. On the other hand, I also hope it can help you better handle your work with its unique skills, tools and ability to deal with complex task scenarios.
Although the entire product has taken shape, I think there are still many areas that need adjustment and optimization. I also hope to rely on the strength of the community to do a good job in AI emotional companionship.
This is the project introduction URL: https://opensoul-web.vercel.app
This is the GitHub project URL: https://github.com/NJX-njx/opensoul
@AdinaY @lilianweng@burtenshaw@clem
let's just do it
On this platform, you can create a "soulmate" that matches your personality, and configure it with the skills, tools you want it to have, as well as the platforms it can integrate with (such as Telegram, Discord, etc.).
You can even create group chats, invite multiple agents and your friends to chat about recent events, discuss projects together, and so on.
On the one hand, I hope it can better accompany you in daily life by virtue of its unique memory mechanism, self-feedback and iteration mechanism, and the modeling of users' emotions. On the other hand, I also hope it can help you better handle your work with its unique skills, tools and ability to deal with complex task scenarios.
Although the entire product has taken shape, I think there are still many areas that need adjustment and optimization. I also hope to rely on the strength of the community to do a good job in AI emotional companionship.
This is the project introduction URL: https://opensoul-web.vercel.app
This is the GitHub project URL: https://github.com/NJX-njx/opensoul
@AdinaY @lilianweng@burtenshaw@clem
let's just do it
- 24 replies
replied to Reality123b's post 8 days ago
Code: DEPLOYMENT_NOT_FOUND
is taking long to get any answer