ByteDance-Seed/Seed-Coder-8B-Instruct
Text Generation
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Jean Louis PRO
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
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Gemini's proprietary license is a deal-breaker. It's not just about performanceโit's about freedom. Google's terms actively restrict libre use, while models like QwQ 32B and DeepSeek v3 (when properly licensed) respect user rights. Never conflate ethically-licensed AI with corporate traps that forbid modification, redistribution, or independent use.
reacted to
as-cle-bert's
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11 days ago
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1850
One of the biggest challenges I've been facing since I started developing [๐๐๐๐๐ญ๐๐จ๐ฐ๐ง](https://github.com/AstraBert/PdfItDown) was handling correctly the conversion of files like Excel sheets and CSVs: table conversion was bad and messy, almost unusable for downstream tasks๐ซฃ
That's why today I'm excited to introduce ๐ซ๐๐๐๐๐ซ๐ฌ, the new feature of PdfItDown v1.4.0!๐
With ๐ณ๐ฆ๐ข๐ฅ๐ฆ๐ณ๐ด, you can choose among three (for now๐) flavors of text extraction and conversion to PDF:
- ๐๐ผ๐ฐ๐น๐ถ๐ป๐ด, which does a fantastic work with presentations, spreadsheets and word documents๐ฆ
- ๐๐น๐ฎ๐บ๐ฎ๐ฃ๐ฎ๐ฟ๐๐ฒ by LlamaIndex, suitable for more complex and articulated documents, with mixture of texts, images and tables๐ฆ
- ๐ ๐ฎ๐ฟ๐ธ๐๐๐๐ผ๐๐ป by Microsoft, not the best at handling highly structured documents, by extremly flexible in terms of input file format (it can even convert XML, JSON and ZIP files!)โ๏ธ
You can use this new feature in your python scripts (check the attached code snippet!๐) and in the command line interface as well!๐
Have fun and don't forget to star the repo on GitHub โก๏ธ https://github.com/AstraBert/PdfItDown
That's why today I'm excited to introduce ๐ซ๐๐๐๐๐ซ๐ฌ, the new feature of PdfItDown v1.4.0!๐
With ๐ณ๐ฆ๐ข๐ฅ๐ฆ๐ณ๐ด, you can choose among three (for now๐) flavors of text extraction and conversion to PDF:
- ๐๐ผ๐ฐ๐น๐ถ๐ป๐ด, which does a fantastic work with presentations, spreadsheets and word documents๐ฆ
- ๐๐น๐ฎ๐บ๐ฎ๐ฃ๐ฎ๐ฟ๐๐ฒ by LlamaIndex, suitable for more complex and articulated documents, with mixture of texts, images and tables๐ฆ
- ๐ ๐ฎ๐ฟ๐ธ๐๐๐๐ผ๐๐ป by Microsoft, not the best at handling highly structured documents, by extremly flexible in terms of input file format (it can even convert XML, JSON and ZIP files!)โ๏ธ
You can use this new feature in your python scripts (check the attached code snippet!๐) and in the command line interface as well!๐
Have fun and don't forget to star the repo on GitHub โก๏ธ https://github.com/AstraBert/PdfItDown
reacted to
fdaudens's
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11 days ago
Post
3099
Forget everything you know about transcription models - NVIDIA's parakeet-tdt-0.6b-v2 changed the game for me!
Just tested it with Steve Jobs' Stanford speech and was speechless (pun intended). The video isnโt sped up.
3 things that floored me:
- Transcription took just 10 seconds for a 15-min file
- Got a CSV with perfect timestamps, punctuation & capitalization
- Stunning accuracy (correctly captured "Reed College" and other specifics)
NVIDIA also released a demo where you can click any transcribed segment to play it instantly.
The improvement is significant: number 1 on the ASR Leaderboard, 6% error rate (best in class) with complete commercial freedom (cc-by-4.0 license).
Time to update those Whisper pipelines! H/t @Steveeeeeeen for the finding!
Model: nvidia/parakeet-tdt-0.6b-v2
Demo: nvidia/parakeet-tdt-0.6b-v2
ASR Leaderboard: hf-audio/open_asr_leaderboard
Just tested it with Steve Jobs' Stanford speech and was speechless (pun intended). The video isnโt sped up.
3 things that floored me:
- Transcription took just 10 seconds for a 15-min file
- Got a CSV with perfect timestamps, punctuation & capitalization
- Stunning accuracy (correctly captured "Reed College" and other specifics)
NVIDIA also released a demo where you can click any transcribed segment to play it instantly.
The improvement is significant: number 1 on the ASR Leaderboard, 6% error rate (best in class) with complete commercial freedom (cc-by-4.0 license).
Time to update those Whisper pipelines! H/t @Steveeeeeeen for the finding!
Model: nvidia/parakeet-tdt-0.6b-v2
Demo: nvidia/parakeet-tdt-0.6b-v2
ASR Leaderboard: hf-audio/open_asr_leaderboard
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reacted to
AdinaY's
post with ๐
13 days ago
Post
2921
DeepSeek, Alibaba, Skywork, Xiaomi, Bytedance.....
And thatโs just part of the companies from the Chinese community that released open models in April ๐คฏ
zh-ai-community/april-2025-open-releases-from-the-chinese-community-67ea699965f6e4c135cab10f
๐ฌ Video
> MAGI-1 by SandAI
> SkyReels-A2 & SkyReels-V2 by Skywork
> Wan2.1-FLF2V by Alibaba-Wan
๐จ Image
> HiDream-I1 by Vivago AI
> Kimi-VL by Moonshot AI
> InstantCharacter by InstantX & Tencent-Hunyuan
> Step1X-Edit by StepFun
> EasyControl by Shanghai Jiaotong University
๐ง Reasoning
> MiMo by Xiaomi
> Skywork-R1V 2.0 by Skywork
> ChatTS by ByteDance
> Kimina by Moonshot AI & Numina
> GLM-Z1 by Zhipu AI
> Skywork OR1 by Skywork
> Kimi-VL-Thinking by Moonshot AI
๐ Audio
> Kimi-Audio by Moonshot AI
> IndexTTS by BiliBili
> MegaTTS3 by ByteDance
> Dolphin by DataOceanAI
๐ข Math
> DeepSeek Prover V2 by Deepseek
๐ LLM
> Qwen by Alibaba-Qwen
> InternVL3 by Shanghai AI lab
> Ernie4.5 (demo) by Baidu
๐ Dataset
> PHYBench by Eureka-Lab
> ChildMandarin & Seniortalk by BAAI
Please feel free to add if I missed anything!
And thatโs just part of the companies from the Chinese community that released open models in April ๐คฏ
zh-ai-community/april-2025-open-releases-from-the-chinese-community-67ea699965f6e4c135cab10f
๐ฌ Video
> MAGI-1 by SandAI
> SkyReels-A2 & SkyReels-V2 by Skywork
> Wan2.1-FLF2V by Alibaba-Wan
๐จ Image
> HiDream-I1 by Vivago AI
> Kimi-VL by Moonshot AI
> InstantCharacter by InstantX & Tencent-Hunyuan
> Step1X-Edit by StepFun
> EasyControl by Shanghai Jiaotong University
๐ง Reasoning
> MiMo by Xiaomi
> Skywork-R1V 2.0 by Skywork
> ChatTS by ByteDance
> Kimina by Moonshot AI & Numina
> GLM-Z1 by Zhipu AI
> Skywork OR1 by Skywork
> Kimi-VL-Thinking by Moonshot AI
๐ Audio
> Kimi-Audio by Moonshot AI
> IndexTTS by BiliBili
> MegaTTS3 by ByteDance
> Dolphin by DataOceanAI
๐ข Math
> DeepSeek Prover V2 by Deepseek
๐ LLM
> Qwen by Alibaba-Qwen
> InternVL3 by Shanghai AI lab
> Ernie4.5 (demo) by Baidu
๐ Dataset
> PHYBench by Eureka-Lab
> ChildMandarin & Seniortalk by BAAI
Please feel free to add if I missed anything!
reacted to
ZennyKenny's
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14 days ago
Post
2721
I've created a new dataset using the Algorithm of Thoughts architecture proposed by Sel et al. (2023) in a reasoning context. (paper: https://arxiv.org/pdf/2308.10379)
The dataset simulates the discovery phase of a fictitious VC firm called Reasoned Capital and, once expanded, can be used to create models which are able to make complex, subjective financial decisions based on different criteria.
The generation process encourages recursive problem-solving in increasingly complex prompts to encourage models to assess and reevaluate the conclusions and generated opinions of upstream models. Pretty neat stuff, and I'm not aware of this architecture being used in a reasoning context anywhere else.
Check it out: ZennyKenny/synthetic_vc_financial_decisions_reasoning_dataset
The dataset simulates the discovery phase of a fictitious VC firm called Reasoned Capital and, once expanded, can be used to create models which are able to make complex, subjective financial decisions based on different criteria.
The generation process encourages recursive problem-solving in increasingly complex prompts to encourage models to assess and reevaluate the conclusions and generated opinions of upstream models. Pretty neat stuff, and I'm not aware of this architecture being used in a reasoning context anywhere else.
Check it out: ZennyKenny/synthetic_vc_financial_decisions_reasoning_dataset
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15 days ago
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Kimi-Audio ๐๐ง an OPEN audio foundation model released by Moonshot AI
moonshotai/Kimi-Audio-7B-Instruct
โจ 7B
โจ 13M+ hours of pretraining data
โจ Novel hybrid input architecture
โจ Universal audio capabilities (ASR, AQA, AAC, SER, SEC/ASC, end-to-end conversation)
moonshotai/Kimi-Audio-7B-Instruct
โจ 7B
โจ 13M+ hours of pretraining data
โจ Novel hybrid input architecture
โจ Universal audio capabilities (ASR, AQA, AAC, SER, SEC/ASC, end-to-end conversation)
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15 days ago
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๐ Building Better Evaluations: 32K Image Annotations Now Available
Today, we're releasing an expanded version: 32K images annotated with 3.7M responses from over 300K individuals which was completed in under two weeks using the Rapidata Python API.
Rapidata/text-2-image-Rich-Human-Feedback-32k
A few months ago, we published one of our most liked dataset with 13K images based on the @data-is-better-together 's dataset, following Google's research on "Rich Human Feedback for Text-to-Image Generation" (https://arxiv.org/abs/2312.10240). It collected over 1.5M responses from 150K+ participants.
Rapidata/text-2-image-Rich-Human-Feedback
In the examples below, users highlighted words from prompts that were not correctly depicted in the generated images. Higher word scores indicate more frequent issues. If an image captured the prompt accurately, users could select [No_mistakes].
We're continuing to work on large-scale human feedback and model evaluation. If you're working on related research and need large, high-quality annotations, feel free to get in touch: [email protected].
Today, we're releasing an expanded version: 32K images annotated with 3.7M responses from over 300K individuals which was completed in under two weeks using the Rapidata Python API.
Rapidata/text-2-image-Rich-Human-Feedback-32k
A few months ago, we published one of our most liked dataset with 13K images based on the @data-is-better-together 's dataset, following Google's research on "Rich Human Feedback for Text-to-Image Generation" (https://arxiv.org/abs/2312.10240). It collected over 1.5M responses from 150K+ participants.
Rapidata/text-2-image-Rich-Human-Feedback
In the examples below, users highlighted words from prompts that were not correctly depicted in the generated images. Higher word scores indicate more frequent issues. If an image captured the prompt accurately, users could select [No_mistakes].
We're continuing to work on large-scale human feedback and model evaluation. If you're working on related research and need large, high-quality annotations, feel free to get in touch: [email protected].
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Xenova's
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15 days ago
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5951
Introducing the ONNX model explorer: Browse, search, and visualize neural networks directly in your browser. ๐คฏ A great tool for anyone studying Machine Learning! We're also releasing the entire dataset of graphs so you can use them in your own projects! ๐ค
Check it out! ๐
Demo: onnx-community/model-explorer
Dataset: onnx-community/model-explorer
Source code: https://github.com/xenova/model-explorer
Check it out! ๐
Demo: onnx-community/model-explorer
Dataset: onnx-community/model-explorer
Source code: https://github.com/xenova/model-explorer