New Trends for Modern Machine Translation with Large Reasoning Models
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minghaowu
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2 days ago
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1485
SEA-VL🔥 an OPEN dataset bridge the AI culture gap in Southeast Asia!
Paper: Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia (2503.07920)
✨1.28M+ culturally relevant images
✨85% accuracy in auto-collected images
✨Tracking underrepresented SEA languages & traditions
Paper: Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia (2503.07920)
✨1.28M+ culturally relevant images
✨85% accuracy in auto-collected images
✨Tracking underrepresented SEA languages & traditions
hzhwcmhf
updated
a
model
3 days ago
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1720
Open Sora 2.0 is out 🔥
hpcai-tech/open-sora-20-67cfb7efa80a73999ccfc2d5
✨ 11B with Apache2.0
✨ Low training cost - $200k
✨ open weights, code and training workflow
hpcai-tech/open-sora-20-67cfb7efa80a73999ccfc2d5
✨ 11B with Apache2.0
✨ Low training cost - $200k
✨ open weights, code and training workflow
Post
1214
Spark TTS 🔊New OPEN TTS model that can generate any voice with just seconds of audio!
Released by SparkAudio community🔥
Model👉 SparkAudio/Spark-TTS-0.5B
Paper👉 Spark-TTS: An Efficient LLM-Based Text-to-Speech Model with Single-Stream Decoupled Speech Tokens (2503.01710)
✨ Supports English & Chinese
✨ BiCodec Speech Codec: Enables precise voice control by separating semantics & speaker attributes
Released by SparkAudio community🔥
Model👉 SparkAudio/Spark-TTS-0.5B
Paper👉 Spark-TTS: An Efficient LLM-Based Text-to-Speech Model with Single-Stream Decoupled Speech Tokens (2503.01710)
✨ Supports English & Chinese
✨ BiCodec Speech Codec: Enables precise voice control by separating semantics & speaker attributes
Post
1301
R1-Omni🔥RLVR-Powered Multimodal LLM released by Alibaba
Model: StarJiaxing/R1-Omni-0.5B
Paper: R1-Omni: Explainable Omni-Multimodal Emotion Recognition with Reinforcing Learning (2503.05379)
✨0.5B with Apache2.0
✨ Improve emotion recognition with visual and audio cues
Model: StarJiaxing/R1-Omni-0.5B
Paper: R1-Omni: Explainable Omni-Multimodal Emotion Recognition with Reinforcing Learning (2503.05379)
✨0.5B with Apache2.0
✨ Improve emotion recognition with visual and audio cues
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#4 opened 6 days ago
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jklj077
clefourrier
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4 days ago
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1593
Gemma3 family is out! Reading the tech report, and this section was really interesting to me from a methods/scientific fairness pov.
Instead of doing over-hyped comparisons, they clearly state that **results are reported in a setup which is advantageous to their models**.
(Which everybody does, but people usually don't say)
For a tech report, it makes a lot of sense to report model performance when used optimally!
On leaderboards on the other hand, comparison will be apples to apples, but in a potentially unoptimal way for a given model family (like some user interact sub-optimally with models)
Also contains a cool section (6) on training data memorization rate too! Important to see if your model will output the training data it has seen as such: always an issue for privacy/copyright/... but also very much for evaluation!
Because if your model knows its evals by heart, you're not testing for generalization.
Instead of doing over-hyped comparisons, they clearly state that **results are reported in a setup which is advantageous to their models**.
(Which everybody does, but people usually don't say)
For a tech report, it makes a lot of sense to report model performance when used optimally!
On leaderboards on the other hand, comparison will be apples to apples, but in a potentially unoptimal way for a given model family (like some user interact sub-optimally with models)
Also contains a cool section (6) on training data memorization rate too! Important to see if your model will output the training data it has seen as such: always an issue for privacy/copyright/... but also very much for evaluation!
Because if your model knows its evals by heart, you're not testing for generalization.
littlebird13
updated
a
model
4 days ago
Post
1047
🙋🏻♂️Hey there folks,
Did you know that you can use ModernBERT to detect model hallucinations ?
Check out the Demo : Tonic/hallucination-test
See here for Medical Context Demo : MultiTransformer/tonic-discharge-guard
check out the model from KRLabs : KRLabsOrg/lettucedect-large-modernbert-en-v1
and the library they kindly open sourced for it : https://github.com/KRLabsOrg/LettuceDetect
👆🏻if you like this topic please contribute code upstream 🚀
Did you know that you can use ModernBERT to detect model hallucinations ?
Check out the Demo : Tonic/hallucination-test
See here for Medical Context Demo : MultiTransformer/tonic-discharge-guard
check out the model from KRLabs : KRLabsOrg/lettucedect-large-modernbert-en-v1
and the library they kindly open sourced for it : https://github.com/KRLabsOrg/LettuceDetect
👆🏻if you like this topic please contribute code upstream 🚀
- 2 replies
Losin94
authored
a
paper
9 days ago
JustinLin610
authored
a
paper
9 days ago
jx-yang
authored
a
paper
9 days ago
Zhenru
authored
a
paper
9 days ago
Post
2257
Babel🗼A multilingual LLM supporting 25 languages, released by the Alibaba DAMO team.
Model: Tower-Babel/babel-67c172157372d4d6c4b4c6d5
Paper: Babel: Open Multilingual Large Language Models Serving Over 90% of Global Speakers (2503.00865)
✨ 9B/83B chat & base
✨ Supports 25 languages: English, Chinese, Hindi, Spanish, Arabic, French, Bengali, Portuguese, Russian, Urdu, Indonesian, German, Japanese, Swahili, Filipino, Tamil, Vietnamese, Turkish, Italian, Javanese, Korean, Hausa, Persian, Thai, and Burmese
Model: Tower-Babel/babel-67c172157372d4d6c4b4c6d5
Paper: Babel: Open Multilingual Large Language Models Serving Over 90% of Global Speakers (2503.00865)
✨ 9B/83B chat & base
✨ Supports 25 languages: English, Chinese, Hindi, Spanish, Arabic, French, Bengali, Portuguese, Russian, Urdu, Indonesian, German, Japanese, Swahili, Filipino, Tamil, Vietnamese, Turkish, Italian, Javanese, Korean, Hausa, Persian, Thai, and Burmese
- 1 reply
Post
660
Powered by
KRLabsOrg/lettucedect-large-modernbert-en-v1 from KRLabsOrg.
Detect hallucinations in answers based on context and questions using ModernBERT with 8192-token context support!
### Model Details
- **Model Name**: [lettucedect-large-modernbert-en-v1]( KRLabsOrg/lettucedect-large-modernbert-en-v1)
- **Organization**: [KRLabsOrg](https://huggingface.co/KRLabsOrg)
- **Github**: [https://github.com/KRLabsOrg/LettuceDetect](https://github.com/KRLabsOrg/LettuceDetect)
- **Architecture**: ModernBERT (Large) with extended context support up to 8192 tokens
- **Task**: Token Classification / Hallucination Detection
- **Training Dataset**: [RagTruth]( wandb/RAGTruth-processed)
- **Language**: English
- **Capabilities**: Detects hallucinated spans in answers, provides confidence scores, and calculates average confidence across detected spans.
LettuceDetect excels at processing long documents to determine if an answer aligns with the provided context, making it a powerful tool for ensuring factual accuracy.
Detect hallucinations in answers based on context and questions using ModernBERT with 8192-token context support!
### Model Details
- **Model Name**: [lettucedect-large-modernbert-en-v1]( KRLabsOrg/lettucedect-large-modernbert-en-v1)
- **Organization**: [KRLabsOrg](https://huggingface.co/KRLabsOrg)
- **Github**: [https://github.com/KRLabsOrg/LettuceDetect](https://github.com/KRLabsOrg/LettuceDetect)
- **Architecture**: ModernBERT (Large) with extended context support up to 8192 tokens
- **Task**: Token Classification / Hallucination Detection
- **Training Dataset**: [RagTruth]( wandb/RAGTruth-processed)
- **Language**: English
- **Capabilities**: Detects hallucinated spans in answers, provides confidence scores, and calculates average confidence across detected spans.
LettuceDetect excels at processing long documents to determine if an answer aligns with the provided context, making it a powerful tool for ensuring factual accuracy.
Post
1665
Qilin 🔥a large scale multimodal dataset for search, recommendation and RAG research, released by Xiaohongshu & Tsinghua University
Dataset: THUIR/Qilin
Paper: Qilin: A Multimodal Information Retrieval Dataset with APP-level User Sessions (2503.00501)
✨Multiple content modalities (text, images, video thumbnails)
✨Rich user interaction data ( from Xiaohongshu’s 300M+ MAUs, 70%+ search penetration)
✨Comprehensive evaluation metrics
✨Support for RAG system development
Dataset: THUIR/Qilin
Paper: Qilin: A Multimodal Information Retrieval Dataset with APP-level User Sessions (2503.00501)
✨Multiple content modalities (text, images, video thumbnails)
✨Rich user interaction data ( from Xiaohongshu’s 300M+ MAUs, 70%+ search penetration)
✨Comprehensive evaluation metrics
✨Support for RAG system development
terryyz
authored
a
paper
12 days ago
Post
2760
CogView-4 is out🔥🚀 The SoTa OPEN text to image model by ZhipuAI
Model: THUDM/CogView4-6B
Demo: THUDM-HF-SPACE/CogView4
✨ 6B with Apache2.0
✨ Supports Chinese & English Prompts by ANY length
✨ Generate Chinese characters within images
✨ Creates images at any resolution within a given range
Model: THUDM/CogView4-6B
Demo: THUDM-HF-SPACE/CogView4
✨ 6B with Apache2.0
✨ Supports Chinese & English Prompts by ANY length
✨ Generate Chinese characters within images
✨ Creates images at any resolution within a given range
Post
4001
Exciting releases from the Chinese community this February🔥
👉 zh-ai-community/2025-february-67a35aaa68e97812def5b6ef
MLLM:
✨ Ovis2 by Alibaba
AIDC-AI/ovis2-67ab36c7e497429034874464
✨ Step Audio Chat by StepFun AI
stepfun-ai/step-audio-67b33accf45735bb21131b0b
Audio:
✨ Step Audio TTS by StepFunAI
stepfun-ai/Step-Audio-TTS-3B
✨ InspireMusic by Alibaba
https://huggingface.co/FunAudioLLM
✨ Baichuan Audio by BaichuanAI
baichuan-inc/Baichuan-Audio-Instruct
Video:
✨ Wan2.1 by Alibaba_Wan
Wan-AI/Wan2.1-T2V-14B
✨ Stepvideo-T2V by StepFun AI
stepfun-ai/stepvideo-t2v
✨ SkyReels-V1 by Skywork
Skywork/skyreels-v1-67b34676ff65b4ec02d16307
✨ LLaDA-8B by RenminUniversity
GSAI-ML/LLaDA-8B-Instruct
MoE:
✨ Moonlight-16B by MoonshotAI (Kimi)
moonshotai/Moonlight-16B-A3B-Instruct
Reasoning:
✨ TinyR1-32B by Qihoo360
qihoo360/TinyR1-32B-Preview
Dataset:
✨ Chinese DeepSeek R1-Distill data -110k
Congliu/Chinese-DeepSeek-R1-Distill-data-110k
👉 zh-ai-community/2025-february-67a35aaa68e97812def5b6ef
MLLM:
✨ Ovis2 by Alibaba
AIDC-AI/ovis2-67ab36c7e497429034874464
✨ Step Audio Chat by StepFun AI
stepfun-ai/step-audio-67b33accf45735bb21131b0b
Audio:
✨ Step Audio TTS by StepFunAI
stepfun-ai/Step-Audio-TTS-3B
✨ InspireMusic by Alibaba
https://huggingface.co/FunAudioLLM
✨ Baichuan Audio by BaichuanAI
baichuan-inc/Baichuan-Audio-Instruct
Video:
✨ Wan2.1 by Alibaba_Wan
Wan-AI/Wan2.1-T2V-14B
✨ Stepvideo-T2V by StepFun AI
stepfun-ai/stepvideo-t2v
✨ SkyReels-V1 by Skywork
Skywork/skyreels-v1-67b34676ff65b4ec02d16307
✨ LLaDA-8B by RenminUniversity
GSAI-ML/LLaDA-8B-Instruct
MoE:
✨ Moonlight-16B by MoonshotAI (Kimi)
moonshotai/Moonlight-16B-A3B-Instruct
Reasoning:
✨ TinyR1-32B by Qihoo360
qihoo360/TinyR1-32B-Preview
Dataset:
✨ Chinese DeepSeek R1-Distill data -110k
Congliu/Chinese-DeepSeek-R1-Distill-data-110k