A Short Summary of Chinese AI Global Expansion
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Adina Yakefu
AdinaY
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Reacted to
lin-tan's
post with 🔥
about 17 hours ago
Post
423
Can language models replace developers? #RepoCod says “Not Yet”, because GPT-4o and other LLMs have <30% accuracy/pass@1 on real-world code generation tasks.
- Leaderboard https://lt-asset.github.io/REPOCOD/
- Dataset: lt-asset/REPOCOD
@jiang719 @shanchao @Yiran-Hu1007
Compared to #SWEBench, RepoCod tasks are
- General code generation tasks, while SWE-Bench tasks resolve pull requests from GitHub issues.
- With 2.6X more tests per task (313.5 compared to SWE-Bench’s 120.8).
Compared to #HumanEval, #MBPP, #CoderEval, and #ClassEval, RepoCod has 980 instances from 11 Python projects, with
- Whole function generation
- Repository-level context
- Validation with test cases, and
- Real-world complex tasks: longest average canonical solution length (331.6 tokens) and the highest average cyclomatic complexity (9.00)
Introducing hashtag #RepoCod-Lite 🐟 for faster evaluations: 200 of the toughest tasks from RepoCod with:
- 67 repository-level, 67 file-level, and 66 self-contains tasks
- Detailed problem descriptions (967 tokens) and long canonical solutions (918 tokens)
- GPT-4o and other LLMs have < 10% accuracy/pass@1 on RepoCod-Lite tasks.
- Dataset: lt-asset/REPOCOD_Lite
#LLM4code #LLM #CodeGeneration #Security
- Leaderboard https://lt-asset.github.io/REPOCOD/
- Dataset: lt-asset/REPOCOD
@jiang719 @shanchao @Yiran-Hu1007
Compared to #SWEBench, RepoCod tasks are
- General code generation tasks, while SWE-Bench tasks resolve pull requests from GitHub issues.
- With 2.6X more tests per task (313.5 compared to SWE-Bench’s 120.8).
Compared to #HumanEval, #MBPP, #CoderEval, and #ClassEval, RepoCod has 980 instances from 11 Python projects, with
- Whole function generation
- Repository-level context
- Validation with test cases, and
- Real-world complex tasks: longest average canonical solution length (331.6 tokens) and the highest average cyclomatic complexity (9.00)
Introducing hashtag #RepoCod-Lite 🐟 for faster evaluations: 200 of the toughest tasks from RepoCod with:
- 67 repository-level, 67 file-level, and 66 self-contains tasks
- Detailed problem descriptions (967 tokens) and long canonical solutions (918 tokens)
- GPT-4o and other LLMs have < 10% accuracy/pass@1 on RepoCod-Lite tasks.
- Dataset: lt-asset/REPOCOD_Lite
#LLM4code #LLM #CodeGeneration #Security
- 1 reply
Reacted to
merve's
post with 🔥
about 19 hours ago
Post
685
What a week! A recap for everything you missed ❄️
merve/nov-22-releases-673fbbcfc1c97c4f411def07
Multimodal ✨
> Mistral AI
released Pixtral 124B, a gigantic open vision language model
> Llava-CoT (formerly known as Llava-o1) was released, a multimodal reproduction of o1 model by PKU
> OpenGVLab released MMPR: a new multimodal reasoning dataset
> Jina has released Jina-CLIP-v2 0.98B multilingual multimodal embeddings
> Apple released new SotA vision encoders AIMv2
LLMs 🦙
> AllenAI dropped a huge release of models, datasets and scripts for Tülu, a family of models based on Llama 3.1 aligned with SFT, DPO and a new technique they have developed called RLVR
> Jina has released embeddings-v3: new multilingual embeddings with longer context
> Hugging Face released SmolTalk: synthetic dataset used to align SmolLM2 using supervised fine-tuning
> Microsoft released orca-agentinstruct-1M-v1: a gigantic instruction dataset of 1M synthetic instruction pairs
Image Generation 🖼️
> Black Forest Labs released Flux 1. tools: four new models for different image modifications and two LoRAs to do image conditioning and better steer generations
Lastly Hugging Face released a new library Observers: a lightweight SDK for monitoring interactions with AI APIs and easily store and browse them 📚
$ pip install observers
merve/nov-22-releases-673fbbcfc1c97c4f411def07
Multimodal ✨
> Mistral AI
released Pixtral 124B, a gigantic open vision language model
> Llava-CoT (formerly known as Llava-o1) was released, a multimodal reproduction of o1 model by PKU
> OpenGVLab released MMPR: a new multimodal reasoning dataset
> Jina has released Jina-CLIP-v2 0.98B multilingual multimodal embeddings
> Apple released new SotA vision encoders AIMv2
LLMs 🦙
> AllenAI dropped a huge release of models, datasets and scripts for Tülu, a family of models based on Llama 3.1 aligned with SFT, DPO and a new technique they have developed called RLVR
> Jina has released embeddings-v3: new multilingual embeddings with longer context
> Hugging Face released SmolTalk: synthetic dataset used to align SmolLM2 using supervised fine-tuning
> Microsoft released orca-agentinstruct-1M-v1: a gigantic instruction dataset of 1M synthetic instruction pairs
Image Generation 🖼️
> Black Forest Labs released Flux 1. tools: four new models for different image modifications and two LoRAs to do image conditioning and better steer generations
Lastly Hugging Face released a new library Observers: a lightweight SDK for monitoring interactions with AI APIs and easily store and browse them 📚
$ pip install observers
Reacted to
m-ric's
post with ❤️
about 19 hours ago
Post
426
Made a new app to visualize the LLM race ⇒ 𝗡𝗼 𝗘𝘂𝗿𝗼𝗽𝗲𝗮𝗻 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗶𝗻 𝘁𝗵𝗲 𝘁𝗼𝗽 𝟭𝟬 🇪🇺❌
See the app here 👉 m-ric/llm-race-to-the-top
I've adapted an app by @andrewrreed that tracks progress of LLMs ( andrewrreed/closed-vs-open-arena-elo), on the Chatbot Arena leaderboard, to compare companies from different countries.
The outcome is quite sad, as a Frenchman and European.
The top 10 is exclusively US 🇺🇸 and Chinese 🇨🇳 companies (after great Chinese LLM releases recently, like the Qwen2.5 series), with the notable exception of Mistral AI 🇫🇷.
American companies are making fast progress, Chinese ones even faster. Europe is at risk of being left behind. And the EU AI Act hasn't even come into force yet to slow down the EU market. We need to wake up 😬
⚠️ Caution: This Chatbot Arena ELO ranking is not the most accurate, especially at high scores like this, because LLM makers can game it to some extent.
See the app here 👉 m-ric/llm-race-to-the-top
I've adapted an app by @andrewrreed that tracks progress of LLMs ( andrewrreed/closed-vs-open-arena-elo), on the Chatbot Arena leaderboard, to compare companies from different countries.
The outcome is quite sad, as a Frenchman and European.
The top 10 is exclusively US 🇺🇸 and Chinese 🇨🇳 companies (after great Chinese LLM releases recently, like the Qwen2.5 series), with the notable exception of Mistral AI 🇫🇷.
American companies are making fast progress, Chinese ones even faster. Europe is at risk of being left behind. And the EU AI Act hasn't even come into force yet to slow down the EU market. We need to wake up 😬
⚠️ Caution: This Chatbot Arena ELO ranking is not the most accurate, especially at high scores like this, because LLM makers can game it to some extent.
Reacted to
THUdyh's
post with 🔥👀
about 19 hours ago
Post
329
🚀🚀🚀Introducing Insight-V! An early attempt towards o1-like multi-modal reasoning.
We offer a structured long-chain visual reasoning data generation pipeline and a multi-agent system to unleash the reasoning potential of MLLMs.
📜 Paper: https://arxiv.org/abs/2411.14432
🛠️ Github: https://github.com/dongyh20/Insight-V
💼 Model Weight: THUdyh/insight-v-673f5e1dd8ab5f2d8d332035
We offer a structured long-chain visual reasoning data generation pipeline and a multi-agent system to unleash the reasoning potential of MLLMs.
📜 Paper: https://arxiv.org/abs/2411.14432
🛠️ Github: https://github.com/dongyh20/Insight-V
💼 Model Weight: THUdyh/insight-v-673f5e1dd8ab5f2d8d332035
posted
an
update
about 19 hours ago
Post
349
Marco-o1🔥 an open Reasoning Models by AIDC team
Model: AIDC-AI/Marco-o1
Paper: Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions (2411.14405)
✨Fine-tuned with CoT data (open-source + synthetic).
✨Expands solution space with MCTS, guided by model confidence.
✨Novel reasoning strategies & self-reflection enhance complex problem-solving.
✨Pioneers LRM in multilingual machine translation.
Model: AIDC-AI/Marco-o1
Paper: Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions (2411.14405)
✨Fine-tuned with CoT data (open-source + synthetic).
✨Expands solution space with MCTS, guided by model confidence.
✨Novel reasoning strategies & self-reflection enhance complex problem-solving.
✨Pioneers LRM in multilingual machine translation.
Reacted to
AkimfromParis's
post with 👍
3 days ago
Post
1339
🇯🇵 The Open Japanese LLM Leaderboard created by LLM-jp 🌸 in partnership with HuggingFace 🤗 was released today!
Blog: https://huggingface.co/blog/leaderboard-japanese
Space: llm-jp/open-japanese-llm-leaderboard
🌍 The leaderboard is available in both Japanese and English
📚 Based on the evaluation tool, llm-jp-eval with more than 20 datasets for Japanese LLMs
📊 The leaderboard showcases all the metrics for NLP experts, plus averages for NLP beginners
💻 For the comfort of users, we chose a horizontal UI, and implemented it in a light and dark theme on Gradio
🔬 The radar chart provides a very interesting visualization of metrics!
🌱 We are using the Japanese research platform, MDX, so please be patient!
⚡ LLMs bigger than +70B will be evaluated soon…
How do you say “GPUs Go Brrr” in Japanese - > GPUがブンブン～! (To pronounce "GPU ga bunbun!") 🔥
Blog: https://huggingface.co/blog/leaderboard-japanese
Space: llm-jp/open-japanese-llm-leaderboard
🌍 The leaderboard is available in both Japanese and English
📚 Based on the evaluation tool, llm-jp-eval with more than 20 datasets for Japanese LLMs
📊 The leaderboard showcases all the metrics for NLP experts, plus averages for NLP beginners
💻 For the comfort of users, we chose a horizontal UI, and implemented it in a light and dark theme on Gradio
🔬 The radar chart provides a very interesting visualization of metrics!
🌱 We are using the Japanese research platform, MDX, so please be patient!
⚡ LLMs bigger than +70B will be evaluated soon…
How do you say “GPUs Go Brrr” in Japanese - > GPUがブンブン～! (To pronounce "GPU ga bunbun!") 🔥
- 4 replies
posted
an
update
4 days ago
Post
968
Build a collection for the trending demos recently released by the Chinese community 🚀 From Qwen2.5 Turbo to FishAgent, see what these models can really do 🔥
zh-ai-community/trending-demo-673b6ca2416a3b3c9d3bf8f1
zh-ai-community/trending-demo-673b6ca2416a3b3c9d3bf8f1
Reacted to
maxiw's
post with ❤️
5 days ago
Post
4522
I was curious to see what people post here on HF so I created a dataset with all HF Posts:
maxiw/hf-posts
Some interesting stats:
Top 5 Authors by Total Impressions:
-----------------------------------
@merve : 171,783 impressions (68 posts)
@fdaudens : 135,253 impressions (81 posts)
@singhsidhukuldeep : 122,591 impressions (81 posts)
@akhaliq : 119,526 impressions (78 posts)
@MonsterMMORPG : 112,500 impressions (45 posts)
Top 5 Users by Number of Reactions Given:
----------------------------------------
@osanseviero : 1278 reactions
@clem : 910 reactions
@John6666 : 899 reactions
@victor : 674 reactions
@samusenps : 655 reactions
Top 5 Most Used Reactions:
-------------------------
❤️: 7048 times
🔥: 5921 times
👍: 4856 times
🚀: 2549 times
🤗: 2065 times
Some interesting stats:
Top 5 Authors by Total Impressions:
-----------------------------------
@merve : 171,783 impressions (68 posts)
@fdaudens : 135,253 impressions (81 posts)
@singhsidhukuldeep : 122,591 impressions (81 posts)
@akhaliq : 119,526 impressions (78 posts)
@MonsterMMORPG : 112,500 impressions (45 posts)
Top 5 Users by Number of Reactions Given:
----------------------------------------
@osanseviero : 1278 reactions
@clem : 910 reactions
@John6666 : 899 reactions
@victor : 674 reactions
@samusenps : 655 reactions
Top 5 Most Used Reactions:
-------------------------
❤️: 7048 times
🔥: 5921 times
👍: 4856 times
🚀: 2549 times
🤗: 2065 times
- 10 replies
posted
an
update
5 days ago
Post
366
LLaVA-o1 🔥 NEW visual language model with spontaneous and systematic reasoning, like GPT-o1!
Paper: LLaVA-o1: Let Vision Language Models Reason Step-by-Step (2411.10440)
Github: https://github.com/PKU-YuanGroup/LLaVA-o1
✨ Autonomous Multistage Reasoning
✨ Efficient with Small Data: Trained on 100k samples
✨ Innovative Inference: Stepwise beam search boosts precision & scalability in reasoning.
Paper: LLaVA-o1: Let Vision Language Models Reason Step-by-Step (2411.10440)
Github: https://github.com/PKU-YuanGroup/LLaVA-o1
✨ Autonomous Multistage Reasoning
✨ Efficient with Small Data: Trained on 100k samples
✨ Innovative Inference: Stepwise beam search boosts precision & scalability in reasoning.
posted
an
update
5 days ago
Post
1405
LLaMA Mesh 🔥 Unifying 3D Mesh Generation with Language Models
Model: Zhengyi/LLaMA-Mesh
Demo: Zhengyi/LLaMA-Mesh
Paper: LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models (2411.09595)
✨ Unified 3D generation & text understanding.
✨ 3D meshes as plain text for seamless LLM integration.
✨ High-quality 3D outputs rivaling specialized models.
Model: Zhengyi/LLaMA-Mesh
Demo: Zhengyi/LLaMA-Mesh
Paper: LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models (2411.09595)
✨ Unified 3D generation & text understanding.
✨ 3D meshes as plain text for seamless LLM integration.
✨ High-quality 3D outputs rivaling specialized models.
Reacted to
reach-vb's
post with 🔥
5 days ago
Post
3974
What a brilliant week for Open Source AI!
Qwen 2.5 Coder by Alibaba - 0.5B / 1.5B / 3B / 7B / 14B/ 32B (Base + Instruct) Code generation LLMs, with 32B tackling giants like Gemnini 1.5 Pro, Claude Sonnet
Qwen/qwen25-coder-66eaa22e6f99801bf65b0c2f
LLM2CLIP from Microsoft - Leverage LLMs to train ultra-powerful CLIP models! Boosts performance over the previous SOTA by ~17%
microsoft/llm2clip-672323a266173cfa40b32d4c
Athene v2 Chat & Agent by NexusFlow - SoTA general LLM fine-tuned from Qwen 2.5 72B excels at Chat + Function Calling/ JSON/ Agents
Nexusflow/athene-v2-6735b85e505981a794fb02cc
Orca Agent Instruct by Microsoft - 1 million instruct pairs covering text editing, creative writing, coding, reading comprehension, etc - permissively licensed
microsoft/orca-agentinstruct-1M-v1
Ultravox by FixieAI - 70B/ 8B model approaching GPT4o level, pick any LLM, train an adapter with Whisper as Audio Encoder
reach-vb/ultravox-audio-language-model-release-67373b602af0a52b2a88ae71
JanusFlow 1.3 by DeepSeek - Next iteration of their Unified MultiModal LLM Janus with RectifiedFlow
deepseek-ai/JanusFlow-1.3B
Common Corpus by Pleais - 2,003,039,184,047 multilingual, commercially permissive and high quality tokens!
PleIAs/common_corpus
I'm sure I missed a lot, can't wait for the next week!
Put down in comments what I missed! 🤗
Qwen 2.5 Coder by Alibaba - 0.5B / 1.5B / 3B / 7B / 14B/ 32B (Base + Instruct) Code generation LLMs, with 32B tackling giants like Gemnini 1.5 Pro, Claude Sonnet
Qwen/qwen25-coder-66eaa22e6f99801bf65b0c2f
LLM2CLIP from Microsoft - Leverage LLMs to train ultra-powerful CLIP models! Boosts performance over the previous SOTA by ~17%
microsoft/llm2clip-672323a266173cfa40b32d4c
Athene v2 Chat & Agent by NexusFlow - SoTA general LLM fine-tuned from Qwen 2.5 72B excels at Chat + Function Calling/ JSON/ Agents
Nexusflow/athene-v2-6735b85e505981a794fb02cc
Orca Agent Instruct by Microsoft - 1 million instruct pairs covering text editing, creative writing, coding, reading comprehension, etc - permissively licensed
microsoft/orca-agentinstruct-1M-v1
Ultravox by FixieAI - 70B/ 8B model approaching GPT4o level, pick any LLM, train an adapter with Whisper as Audio Encoder
reach-vb/ultravox-audio-language-model-release-67373b602af0a52b2a88ae71
JanusFlow 1.3 by DeepSeek - Next iteration of their Unified MultiModal LLM Janus with RectifiedFlow
deepseek-ai/JanusFlow-1.3B
Common Corpus by Pleais - 2,003,039,184,047 multilingual, commercially permissive and high quality tokens!
PleIAs/common_corpus
I'm sure I missed a lot, can't wait for the next week!
Put down in comments what I missed! 🤗
Reacted to
cfahlgren1's
post with 🔥
8 days ago
Post
2199
Why use Google Drive when you can have:
• Free storage with generous limits🆓
• Dataset Viewer (Sorting, Filtering, FTS) 🔍
• Third Party Library Support
• SQL Console 🟧
• Security 🔒
• Community, Reach, and Visibility 📈
It's a no brainer!
Check out our post on what you get instantly out of the box when you create a dataset.
https://huggingface.co/blog/researcher-dataset-sharing
• Free storage with generous limits🆓
• Dataset Viewer (Sorting, Filtering, FTS) 🔍
• Third Party Library Support
• SQL Console 🟧
• Security 🔒
• Community, Reach, and Visibility 📈
It's a no brainer!
Check out our post on what you get instantly out of the box when you create a dataset.
https://huggingface.co/blog/researcher-dataset-sharing
- 1 reply
Reacted to
merve's
post with 🔥
8 days ago
Post
1933
Amazing past days at open ML, it's raining coding models, let's have a recap 🌧️ Find all models and datasets here
merve/nov-15-releases-67372d0ebdc354756a52ecd0
Models
💻 Coding: Qwen team released two Qwen2.5-Coder checkpoints of 32B and 7B. Infly released OpenCoder: 1.5B and 8B coding models with instruction SFT'd versions and their datasets! 💗
🖼️ Image/Video Gen: Alibaba vision lab released In-context LoRA -- 10 LoRA models on different themes based on Flux. Also Mochi the sota video generation model with A2.0 license now comes natively supported in diffusers 👏
🖼️ VLMs/Multimodal: NexaAIDev released Omnivision 968M a new vision language model aligned with DPO for reducing hallucinations, also comes with GGUF ckpts 👏 Microsoft released LLM2CLIP, a new CLIP-like model with longer context window allowing complex text inputs and better search
🎮 AGI?: Etched released Oasis 500M, a diffusion based open world model that takes keyboard input and outputs gameplay 🤯
Datasets
Common Corpus: A text dataset with 2T tokens with permissive license for EN/FR on various sources: code, science, finance, culture 📖
Models
💻 Coding: Qwen team released two Qwen2.5-Coder checkpoints of 32B and 7B. Infly released OpenCoder: 1.5B and 8B coding models with instruction SFT'd versions and their datasets! 💗
🖼️ Image/Video Gen: Alibaba vision lab released In-context LoRA -- 10 LoRA models on different themes based on Flux. Also Mochi the sota video generation model with A2.0 license now comes natively supported in diffusers 👏
🖼️ VLMs/Multimodal: NexaAIDev released Omnivision 968M a new vision language model aligned with DPO for reducing hallucinations, also comes with GGUF ckpts 👏 Microsoft released LLM2CLIP, a new CLIP-like model with longer context window allowing complex text inputs and better search
🎮 AGI?: Etched released Oasis 500M, a diffusion based open world model that takes keyboard input and outputs gameplay 🤯
Datasets
Common Corpus: A text dataset with 2T tokens with permissive license for EN/FR on various sources: code, science, finance, culture 📖
posted
an
update
8 days ago
Post
2492
Let’s dive into the exciting releases from the Chinese community last week 🔥🚀
More details 👉 https://huggingface.co/zh-ai-community
Code model:
✨Qwen 2.5 coder by Alibaba Qwen
Qwen/qwen25-coder-66eaa22e6f99801bf65b0c2f
✨OpenCoder by InflyAI - Fully open code model🙌
infly/opencoder-672cec44bbb86c39910fb55e
Image model:
✨Hunyuan3D-1.0 by Tencent
tencent/Hunyuan3D-1
MLLM:
✨JanusFlow by DeepSeek
deepseek-ai/JanusFlow-1.3B
deepseek-ai/JanusFlow-1.3B
✨Mono-InternVL-2B by OpenGVlab
OpenGVLab/Mono-InternVL-2B
Video model:
✨CogVideoX 1.5 by ChatGLM
THUDM/CogVideoX1.5-5B-SAT
Audio model:
✨Fish Agent by FishAudio
fishaudio/fish-agent-v0.1-3b
Dataset:
✨OPI dataset by BAAIBeijing
BAAI/OPI
More details 👉 https://huggingface.co/zh-ai-community
Code model:
✨Qwen 2.5 coder by Alibaba Qwen
Qwen/qwen25-coder-66eaa22e6f99801bf65b0c2f
✨OpenCoder by InflyAI - Fully open code model🙌
infly/opencoder-672cec44bbb86c39910fb55e
Image model:
✨Hunyuan3D-1.0 by Tencent
tencent/Hunyuan3D-1
MLLM:
✨JanusFlow by DeepSeek
deepseek-ai/JanusFlow-1.3B
deepseek-ai/JanusFlow-1.3B
✨Mono-InternVL-2B by OpenGVlab
OpenGVLab/Mono-InternVL-2B
Video model:
✨CogVideoX 1.5 by ChatGLM
THUDM/CogVideoX1.5-5B-SAT
Audio model:
✨Fish Agent by FishAudio
fishaudio/fish-agent-v0.1-3b
Dataset:
✨OPI dataset by BAAIBeijing
BAAI/OPI
posted
an
update
23 days ago
Post
677
The most upvoted papers from the Chinese community on the Daily Papers - October🔥
zh-ai-community/trending-papers-october-6723995e5d93f8e480928b8c
zh-ai-community/trending-papers-october-6723995e5d93f8e480928b8c
Reacted to
merve's
post with ❤️
30 days ago
Post
2440
Lotus 🪷 is a new foundation model on monocular depth estimation ✨
Compared to previous diffusion-based MDE models, Lotus is modified for dense prediction tasks
Authors also released a model for normal prediction 🤗
Find everything in this collection merve/lotus-6718fb957dc1c85a47ca1210
Compared to previous diffusion-based MDE models, Lotus is modified for dense prediction tasks
Authors also released a model for normal prediction 🤗
Find everything in this collection merve/lotus-6718fb957dc1c85a47ca1210
Reacted to
THUdyh's
post with 🔥
30 days ago
Post
3132
🔥🔥🔥Introducing Oryx-1.5!
A series of unified MLLMs with much stronger performance on all the image, video, and 3D benchmarks 😍
🛠️Github: https://github.com/Oryx-mllm/Oryx
🚀Model: THUdyh/oryx-15-6718c60763845525c2bba71d
🎨Demo: THUdyh/Oryx
👋Try the top-tier MLLM yourself!
👀Stay tuned for more explorations on MLLMs!
A series of unified MLLMs with much stronger performance on all the image, video, and 3D benchmarks 😍
🛠️Github: https://github.com/Oryx-mllm/Oryx
🚀Model: THUdyh/oryx-15-6718c60763845525c2bba71d
🎨Demo: THUdyh/Oryx
👋Try the top-tier MLLM yourself!
👀Stay tuned for more explorations on MLLMs!
Reacted to
merve's
post with 🔥
about 1 month ago
Post
1644
Tencent released a new depth model that generates temporally consistent depth maps over videos ⏯️
Model: tencent/DepthCrafter
Demo: tencent/DepthCrafter
Paper: DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos (2409.02095)
You don't need to input anything other than video itself, no need for optical flow or camera poses! 🤩
Model: tencent/DepthCrafter
Demo: tencent/DepthCrafter
Paper: DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos (2409.02095)
You don't need to input anything other than video itself, no need for optical flow or camera poses! 🤩
Reacted to
yuexiang96's
post with 🔥
about 1 month ago
Post
2964
🌍 I’ve always had a dream of making AI accessible to everyone, regardless of location or language. However, current open MLLMs often respond in English, even to non-English queries!
🚀 Introducing Pangea: A Fully Open Multilingual Multimodal LLM supporting 39 languages! 🌐✨
https://neulab.github.io/Pangea/
https://arxiv.org/pdf/2410.16153
The Pangea family includes three major components:
🔥 Pangea-7B: A state-of-the-art multilingual multimodal LLM capable of 39 languages! Not only does it excel in multilingual scenarios, but it also matches or surpasses English-centric models like Llama 3.2, Molmo, and LlavaOneVision in English performance.
📝 PangeaIns: A 6M multilingual multimodal instruction tuning dataset across 39 languages. 🗂️ With 40% English instructions and 60% multilingual instructions, it spans various domains, including 1M culturally-relevant images sourced from LAION-Multi. 🎨
🏆 PangeaBench: A comprehensive evaluation benchmark featuring 14 datasets in 47 languages. Evaluation can be tricky, so we carefully curated existing benchmarks and introduced two new datasets: xChatBench (human-annotated wild queries with fine-grained evaluation criteria) and xMMMU (a meticulously machine-translated version of MMMU).
Check out more details: https://x.com/xiangyue96/status/1848753709787795679
🚀 Introducing Pangea: A Fully Open Multilingual Multimodal LLM supporting 39 languages! 🌐✨
https://neulab.github.io/Pangea/
https://arxiv.org/pdf/2410.16153
The Pangea family includes three major components:
🔥 Pangea-7B: A state-of-the-art multilingual multimodal LLM capable of 39 languages! Not only does it excel in multilingual scenarios, but it also matches or surpasses English-centric models like Llama 3.2, Molmo, and LlavaOneVision in English performance.
📝 PangeaIns: A 6M multilingual multimodal instruction tuning dataset across 39 languages. 🗂️ With 40% English instructions and 60% multilingual instructions, it spans various domains, including 1M culturally-relevant images sourced from LAION-Multi. 🎨
🏆 PangeaBench: A comprehensive evaluation benchmark featuring 14 datasets in 47 languages. Evaluation can be tricky, so we carefully curated existing benchmarks and introduced two new datasets: xChatBench (human-annotated wild queries with fine-grained evaluation criteria) and xMMMU (a meticulously machine-translated version of MMMU).
Check out more details: https://x.com/xiangyue96/status/1848753709787795679