Post
270
New interactive viz from AI World showing OpenAI's new open model gpt-oss-120b breaking into the top 50 most liked models of all time on the Hub in under a day! ☄️☄️☄️
AI & ML interests
None defined yet.
Recent Activity
View all activity
BrigitteTousi
posted
an
update
2 days ago
Post
209
🤖 ICYMI: Yesterday, Hugging Face and OpenAI partnered to bring open source GPT to the public. This is a Big Deal in "AI world".
0. Common ground setting: OpenAI is the ChatGPT people. An “open source” model is one whose weights are available — that means the model can be “yours”.
1. You don’t have to interact with the company directly, nor give them your interactions, to use the system. The company can't "surveil" you.
2. You can evaluate the unique contributions of their SOTA model much more rigorously than you can when there are collections of models+code behind a closed API. You can find out specifically what the model can and can't do.
3. And you can directly customize it for whatever you'd like. Fine-tuning, wherein you give the model data that's tailored to your use cases and train it some more on that data, is trivial* when you have the model weights.
*Provided you have the compute.
4. You can directly benchmark whatever you'd like. Biases? Energy usage? Strengths/weaknesses? Go for it. You wants it you gots it--this transparency helps people understand SOTA *in general*, not just for this model, but points to, e.g., what's going on with closed Google models as well.
5. One of the most powerful things about "openness" that I've learned is that it cultivates ecosystems of collaborators building on top of one another's brilliance to make systems that are significantly better than they would be if created in isolation.
But, caveat wrt my own philosophy...
6. I do not take it as a given that advancing LLMs is good, and have a lot more to say wrt where I think innovation should focus more. For example, a focus on *data* -- curation, measurement, consent, credit, compensation, safety -- would deeply improve technology for everyone.
7. The transparency this release provides is massive for people who want to *learn* about LLMs. For the next generation of technologists to advance over the current, they MUST be able to learn about what's happening now. (cont...)
0. Common ground setting: OpenAI is the ChatGPT people. An “open source” model is one whose weights are available — that means the model can be “yours”.
1. You don’t have to interact with the company directly, nor give them your interactions, to use the system. The company can't "surveil" you.
2. You can evaluate the unique contributions of their SOTA model much more rigorously than you can when there are collections of models+code behind a closed API. You can find out specifically what the model can and can't do.
3. And you can directly customize it for whatever you'd like. Fine-tuning, wherein you give the model data that's tailored to your use cases and train it some more on that data, is trivial* when you have the model weights.
*Provided you have the compute.
4. You can directly benchmark whatever you'd like. Biases? Energy usage? Strengths/weaknesses? Go for it. You wants it you gots it--this transparency helps people understand SOTA *in general*, not just for this model, but points to, e.g., what's going on with closed Google models as well.
5. One of the most powerful things about "openness" that I've learned is that it cultivates ecosystems of collaborators building on top of one another's brilliance to make systems that are significantly better than they would be if created in isolation.
But, caveat wrt my own philosophy...
6. I do not take it as a given that advancing LLMs is good, and have a lot more to say wrt where I think innovation should focus more. For example, a focus on *data* -- curation, measurement, consent, credit, compensation, safety -- would deeply improve technology for everyone.
7. The transparency this release provides is massive for people who want to *learn* about LLMs. For the next generation of technologists to advance over the current, they MUST be able to learn about what's happening now. (cont...)
- 1 reply
Post
1107
🔥 July highlights from Chinese AI community
zh-ai-community/july-2025-open-works-from-the-chinese-community-686586f1a8840797e477ae5a
✨ Another "DeepSeek moment" - Kimi K2 🙌
✨ Qwen goes fully matrixed - Instruct / Thinking / Coder models across 30B - 480B 🤯
✨ The multimodal wave🌊
- GLM-4.1V-Thinking: Image+Text > Text
- Intern-S1: Image+Text > Text
- Wan 2.2 - Text +Image > video
- Skywork-R1V3: Image+Text > Text
- Skywork-UniPic: Text > Image / Image > Text
- Tar-7B: Any-to-Any
- Ming-Lite-Omni-1.5: Any-to-Any
- Step3: Image+Text > Text
- HunyuanWorld-1: Image > 3D
- ThinkSound: Video > Audio
- Neta-Lumina: Text > Image
✨Tiny & deployable models 🤏
- SmallThinker runs on 1GB RAM
✨Agentic coding goes mainstream 💻
- Qwen3-Coder: fully spec'd tool calling
- GLM-4.5: browser agents, IDE assistant
- Qwen3 WebDev demo: text-to-frontend code
✨Domain-Specific & Utility Models/Tools/Dataset
- Science one S1: Scientific model
- Agentar DeepFinance: Finance dataset
- ObjectClear: Interactive Vision Tool
- Qwen3 MT Demo: Machine Translation Tool
✨ Big month not only for models, but for policy too🏛️
- Announced Global Action Plan for AI Governance
- Proposes to set up a World AI Cooperation Organization in Shanghai
- Released International AI Open Source Collaboration Initiative
- Published Risk Assessment Guidelines for Endpoint AI Agents
✨ Big event - WAIC
- 355K offline visitors
- 108 new released in 4 days
- 145 sessions across key domains
I’ve been tracking things closely, but July’s open-source wave still blew me away. Can’t wait to see what’s coming next! 🚀
zh-ai-community/july-2025-open-works-from-the-chinese-community-686586f1a8840797e477ae5a
✨ Another "DeepSeek moment" - Kimi K2 🙌
✨ Qwen goes fully matrixed - Instruct / Thinking / Coder models across 30B - 480B 🤯
✨ The multimodal wave🌊
- GLM-4.1V-Thinking: Image+Text > Text
- Intern-S1: Image+Text > Text
- Wan 2.2 - Text +Image > video
- Skywork-R1V3: Image+Text > Text
- Skywork-UniPic: Text > Image / Image > Text
- Tar-7B: Any-to-Any
- Ming-Lite-Omni-1.5: Any-to-Any
- Step3: Image+Text > Text
- HunyuanWorld-1: Image > 3D
- ThinkSound: Video > Audio
- Neta-Lumina: Text > Image
✨Tiny & deployable models 🤏
- SmallThinker runs on 1GB RAM
✨Agentic coding goes mainstream 💻
- Qwen3-Coder: fully spec'd tool calling
- GLM-4.5: browser agents, IDE assistant
- Qwen3 WebDev demo: text-to-frontend code
✨Domain-Specific & Utility Models/Tools/Dataset
- Science one S1: Scientific model
- Agentar DeepFinance: Finance dataset
- ObjectClear: Interactive Vision Tool
- Qwen3 MT Demo: Machine Translation Tool
✨ Big month not only for models, but for policy too🏛️
- Announced Global Action Plan for AI Governance
- Proposes to set up a World AI Cooperation Organization in Shanghai
- Released International AI Open Source Collaboration Initiative
- Published Risk Assessment Guidelines for Endpoint AI Agents
✨ Big event - WAIC
- 355K offline visitors
- 108 new released in 4 days
- 145 sessions across key domains
I’ve been tracking things closely, but July’s open-source wave still blew me away. Can’t wait to see what’s coming next! 🚀
Post
403
🤖 👾 Thanks so much to BBC News and the stellar Suranjana Tewari for having me on to talk about US <—> China relationship in AI, and what it means for AI ethics.
Post
1588
Qwen team did it again!!
They just released Qwen3-Coder-30B-A3B-Instruct on the hub🔥
Qwen/Qwen3-Coder-30B-A3B-Instruct
✨ Apache 2.0
✨30B total / 3.3B active (128 experts, 8 top-k)
✨ Native 256K context, extendable to 1M via Yarn
✨ Built for Agentic Coding
They just released Qwen3-Coder-30B-A3B-Instruct on the hub🔥
Qwen/Qwen3-Coder-30B-A3B-Instruct
✨ Apache 2.0
✨30B total / 3.3B active (128 experts, 8 top-k)
✨ Native 256K context, extendable to 1M via Yarn
✨ Built for Agentic Coding
Post
334
It’s here! After the WAIC announcement, StepFun has just dropped Step 3 🔥 their latest multimodal reasoning model on the hub.
Paper: Step-3 is Large yet Affordable: Model-system Co-design for Cost-effective Decoding (2507.19427)
Model: stepfun-ai/step3
✨ 321B total / 32B active - Apache 2.0
✨ MFA + AFD : cutting decoding cost by up to 70% vs. DeepSeek-V3
✨ 4T image-text pretraining: strong vision–language grounding
✨ Modular, efficient, deployable: runs on just 8×48GB GPUs
Paper: Step-3 is Large yet Affordable: Model-system Co-design for Cost-effective Decoding (2507.19427)
Model: stepfun-ai/step3
✨ 321B total / 32B active - Apache 2.0
✨ MFA + AFD : cutting decoding cost by up to 70% vs. DeepSeek-V3
✨ 4T image-text pretraining: strong vision–language grounding
✨ Modular, efficient, deployable: runs on just 8×48GB GPUs
Post
3505
Qwen3-30B-A3B-Thinking-2507 🔥 latest step in scaling thinking capabilities from Alibaba Qwen team.
Qwen/Qwen3-30B-A3B-Thinking-2507-FP8
✨ 30B total / 3B active - Apache 2.0
✨ Native 256K context
✨ SOTA coding, alignment, agentic reasoning
Qwen/Qwen3-30B-A3B-Thinking-2507-FP8
✨ 30B total / 3B active - Apache 2.0
✨ Native 256K context
✨ SOTA coding, alignment, agentic reasoning
Post
2694
Skywork UniPic 🔥a unified autoregressive multimodal model for image understanding, generation, & editing, by Skywork 天工
Skywork/skywork-unipic-6888c0789cdb82457b2acf32
✨ 1.5 B - MIT License
✨ Runs on RTX 4090
✨ Truly unified architecture
Skywork/skywork-unipic-6888c0789cdb82457b2acf32
✨ 1.5 B - MIT License
✨ Runs on RTX 4090
✨ Truly unified architecture
Post
1710
Qwen just released Qwen3-30B-A3B-Instruct-2507 🔥 an upgrade to the non-thinking mode model
Qwen/Qwen3-30B-A3B-Instruct-2507
✨ 30B MoE / 3.3B active - Apache 2.0
✨ Strong gains in reasoning, math, coding, & multilingual tasks
✨ Native support for 256K long-context inputs
Qwen/Qwen3-30B-A3B-Instruct-2507
✨ 30B MoE / 3.3B active - Apache 2.0
✨ Strong gains in reasoning, math, coding, & multilingual tasks
✨ Native support for 256K long-context inputs
Post
2950
💬 From Replika to everyday chatbots, millions of people are forming emotional bonds with AI, sometimes seeking comfort, sometimes seeking intimacy. But what happens when an AI tells you "I understand how you feel" and you actually believe it?
At Hugging Face, together with @frimelle and @yjernite , we dug into something we felt wasn't getting enough attention: the need to evaluate AI companionship behaviors. These are the subtle ways AI systems validate us, engage with us, and sometimes manipulate our emotional lives.
Here's what we found:
👉 Existing benchmarks (accuracy, helpfulness, safety) completely miss this emotional dimension.
👉 We mapped how leading AI systems actually respond to vulnerable prompts. 👉 We built the Interactions and Machine Attachment Benchmark (INTIMA): a first attempt at evaluating how models handle emotional dependency, boundaries, and attachment (with a full paper coming soon).
Check out the blog post: https://huggingface.co/blog/giadap/evaluating-companionship
🚢 We also shipped two visualization tools with Gradio to see how different models behave when things get emotionally intense:
- AI-companionship/intima-responses-2D
- giadap/INTIMA-responses
At Hugging Face, together with @frimelle and @yjernite , we dug into something we felt wasn't getting enough attention: the need to evaluate AI companionship behaviors. These are the subtle ways AI systems validate us, engage with us, and sometimes manipulate our emotional lives.
Here's what we found:
👉 Existing benchmarks (accuracy, helpfulness, safety) completely miss this emotional dimension.
👉 We mapped how leading AI systems actually respond to vulnerable prompts. 👉 We built the Interactions and Machine Attachment Benchmark (INTIMA): a first attempt at evaluating how models handle emotional dependency, boundaries, and attachment (with a full paper coming soon).
Check out the blog post: https://huggingface.co/blog/giadap/evaluating-companionship
🚢 We also shipped two visualization tools with Gradio to see how different models behave when things get emotionally intense:
- AI-companionship/intima-responses-2D
- giadap/INTIMA-responses
Post
427
Wan2.2 🔥A video diffusion model with MoE just released by Alibaba_Wan
Wan-AI/Wan2.2-TI2V-5B
Wan-AI/Wan2.2-I2V-A14B-Diffusers
✨ 5B/14B - Apache2.0
✨ Cinematic-level aesthetics (lighting, tone, composition)
✨ Massive training data (+83% videos)→ smoother motion
✨ Supports image-only video generation, even without a prompt.
Wan-AI/Wan2.2-TI2V-5B
Wan-AI/Wan2.2-I2V-A14B-Diffusers
✨ 5B/14B - Apache2.0
✨ Cinematic-level aesthetics (lighting, tone, composition)
✨ Massive training data (+83% videos)→ smoother motion
✨ Supports image-only video generation, even without a prompt.
Post
356
GLM-4.5 🔥 The largest open models yet from Zhipu.
Built for intelligent agents with unified capabilities: reasoning, coding, tool use.
zai-org/glm-45-687c621d34bda8c9e4bf503b
✨ 355B total / 32B active - MIT license
✨ Hybrid reasoning modes: Thinking mode for complex tasks/ Non-thinking mode for instant replies
Built for intelligent agents with unified capabilities: reasoning, coding, tool use.
zai-org/glm-45-687c621d34bda8c9e4bf503b
✨ 355B total / 32B active - MIT license
✨ Hybrid reasoning modes: Thinking mode for complex tasks/ Non-thinking mode for instant replies
Post
318
Panshi 磐石 🪨 Scientific Foundation Model by the Chinese Academy of Sciences
ScienceOne-AI/S1-Base-8B
ScienceOne-AI/S1-Base-32B
✨ 8B/32B- Apache2.0
✨ Trained on scientific data & laws across math, physics, chemistry, bio, etc.
✨ Supports 300+ tools, 170M+ papers, autonomous scientific planning
ScienceOne-AI/S1-Base-8B
ScienceOne-AI/S1-Base-32B
✨ 8B/32B- Apache2.0
✨ Trained on scientific data & laws across math, physics, chemistry, bio, etc.
✨ Supports 300+ tools, 170M+ papers, autonomous scientific planning
- 3 replies
Post
333
Tencent Hunyuan released their first 3D world model: Hunyuan World 1.0 🔥
tencent/HunyuanWorld-1
✨From a single prompt to explorable 3D scenes in minutes
✨ Supports Immersive roaming / Semantic-level interactivity / Physics-ready simulation
tencent/HunyuanWorld-1
✨From a single prompt to explorable 3D scenes in minutes
✨ Supports Immersive roaming / Semantic-level interactivity / Physics-ready simulation
BrigitteTousi
posted
an
update
15 days ago
Post
510
This is what Hugging Face is all about. We want everyone, hobbyists, researchers and industry alike, to be able to contribute to AI because everyone is affected by it. Kudos to HF's
@irenesolaiman
for spreading the word!🔥🤗
Post
1702
Big respect to the Qwen team! They just dropped another model🔥
Qwen3-235B-A22B-Thinking-2507 🧠 new reasoning model by Qwen
Qwen/Qwen3-235B-A22B-Thinking-2507
✨ 235B total / 22B active (8 experts)
✨ 256K context window
✨ Agent-ready with tool use & <think> reasoning mode
Hope the team gets some well-deserved rest this weekend after all the massive releases 🙌
Qwen3-235B-A22B-Thinking-2507 🧠 new reasoning model by Qwen
Qwen/Qwen3-235B-A22B-Thinking-2507
✨ 235B total / 22B active (8 experts)
✨ 256K context window
✨ Agent-ready with tool use & <think> reasoning mode
Hope the team gets some well-deserved rest this weekend after all the massive releases 🙌
Post
322
Ming-lite-omni v1.5 🔥 upgrade version of Ming-lite-omni, by AntGroup.
inclusionAI/Ming-Lite-Omni-1.5
✨ 20.3B / 3B active - MoE
✨ SOTA video understanding via 3D MRoPE + curriculum learning
✨ Real time speech synthesis + dialect support
✨ Enhanced multimodal generation with ID & scene consistency
inclusionAI/Ming-Lite-Omni-1.5
✨ 20.3B / 3B active - MoE
✨ SOTA video understanding via 3D MRoPE + curriculum learning
✨ Real time speech synthesis + dialect support
✨ Enhanced multimodal generation with ID & scene consistency
Post
1576
Qwen is on fire this week 🔥
They just released Qwen3-MT 🌍 a translation model supports 92 languages.
Demo is available on the hub.
Qwen/Qwen3-MT-Demo
✨ Highly Customizable: Supports custom terms, domain prompts, and translation memory for accurate, context-aware results.
✨ Fast and affordable: $0.5 per million tokens.
They just released Qwen3-MT 🌍 a translation model supports 92 languages.
Demo is available on the hub.
Qwen/Qwen3-MT-Demo
✨ Highly Customizable: Supports custom terms, domain prompts, and translation memory for accurate, context-aware results.
✨ Fast and affordable: $0.5 per million tokens.
Post
3375
Qwen3-Coder 💻 agentic code model by Alibaba Qwen team🚀
Qwen/Qwen3-Coder-480B-A35B-Instruct
✨ 480B total, 35B activated MoE
✨ Agentic Coding + Browser Use → Top code model performance
✨ 256K context (up to 1M via Yarn) for repo-scale understanding
Qwen/Qwen3-Coder-480B-A35B-Instruct
✨ 480B total, 35B activated MoE
✨ Agentic Coding + Browser Use → Top code model performance
✨ 256K context (up to 1M via Yarn) for repo-scale understanding
-
- 2 replies
Post
2674
KAT-V1 🔥 a LLM that tackles overthinking by switching between reasoning and direct answers, by Kuaishou.
Kwaipilot/KAT-V1-40B
✨ 40B
✨ Step-SRPO: smarter reasoning control via RL
✨ MTP + Distillation: efficient training, lower cost
Kwaipilot/KAT-V1-40B
✨ 40B
✨ Step-SRPO: smarter reasoning control via RL
✨ MTP + Distillation: efficient training, lower cost